Myocardial tissue and mechanics — CMR phenotype documentation

Myocardium

Native T1 Values

One of the unique aspects of magnetic resonance imaging (MRI) is the sensitivity of the soft tissue image contrast to tissue composition, which can be a reflection of physiology and pathophysiology. The T1 relaxation time, a measure of how fast the nuclear spin magnetization returns to its equilibrium state after a radio frequency (RF) pulse in the MRI scanner, is a key source of soft tissue contrast in MRI. It was generally considered sufficient to have the T1 relaxation properties "encoded" in the pixel intensity of images. Advances in CMR imaging techniques have rendered it feasible to generate color-encoded T1 maps, in which the pixel values represent the T1 in each voxel rather than a signal intensity in arbitrary units. T1 maps can depict even relatively small variations of T1 within the heart muscle to highlight tissue pathology. T1 mapping is becoming an essential tool for the CMR imager trying to understand myocardial tissue pathology and its prognostic implications. In addition, by quantifying tissue characteristics through T1 mapping, it becomes feasible to follow longitudinal changes, an essential aspect for using these novel markers in treatment trials (Taylor et al. 2016).

T1 mapping without administration of a paramagnetic contrast agent, referred to as "native" T1 mapping, is sensitive to myocardial edema, iron overload, and the presence of myocardial infarcts and scarring (Taylor et al. 2016).

The normal myocardial blood volume averages approximately 6% of the myocardium. Since the T1 value of blood is variable and higher than that of the myocardium, the T1 value of myocardium is thus expected to be influenced by both the T1 value, and the amount of blood in the myocardium. Assuming a linear correlation between septal myocardial T1 and blood measurements, the T1 value can be corrected by utilizing the T1<sup>*</sup> measured in the blood pool, using the equation T1<sub>corrected</sub> = T1<sub>uncorrected</sub> + α × (R<sub>mean</sub>−R<sub>patient</sub>) where R<sub>mean</sub> is the mean T1 relaxation rate R1 = 1/T1 for the patient cohort, and α is calculated as the slope of the linear regression between myocardial T1 and blood T1<sup>*</sup> measurements (Nickander et al. 2016; Puyol-Antón et al. 2020).

<figure> <img src="/latex/images/myocardium/T1.png" id="fig:Native_T1" alt="Native myocardial T1 values of the LV intra-ventricular septum, LV blood pool and RV blood pool (Puyol-Antón et al. 2020)." /><figcaption aria-hidden="true">Native myocardial T1 values of the LV intra-ventricular septum, LV blood pool and RV blood pool <span>(Puyol-Antón et al. 2020)</span>.</figcaption> </figure>

<figure> <img src="/latex/images/myocardium/T1_mapping.png" id="fig:T1_mapping" alt="Myocardial T1 mapping. Native T1 mapping allows tissue characterization and differential diagnosis of cardiomyopathies based on pre-contrast T1 values (Barison et al. 2022)." /><figcaption aria-hidden="true">Myocardial T1 mapping. Native T1 mapping allows tissue characterization and differential diagnosis of cardiomyopathies based on pre-contrast T1 values <span>(Barison et al. 2022)</span>.</figcaption> </figure>

Definition: T1 relaxation time representing how fast the nuclear spin magnetization returns to its equilibrium state after a radio frequency (RF) pulse.
Acquisition Type: T1 mapping
Reference Range (performed at 1.5T Siemens scanner using the Shortened Modified Look-Locker Inversion Recovery (ShMOLLI):
1. Global:
  Study Cohort Size Gender Reference Value (ms) Note
  (Puyol-Antón et al. 2020) 932 (945, 54)
  (Piechnik et al. 2013) 173 female (973, 23)
  169 male (950, 20)
  (Kawel-Boehm et al. 2020) 971 (960, 29)
2. Intra-ventricular septum (divided by RV-LV intersection):
  Study Cohort Size Reference Value (ms) Note
  (Puyol-Antón et al. 2020) 937 (952, 41)
3. Free-wall (divided by RV-LV intersection):
  Study Cohort Size Reference Value (ms) Note
  (Puyol-Antón et al. 2020) 930 (941, 65)
4. Midwall (myocardial contour following a 1-pixel erosion):
  Study Cohort Size Gender Reference Value (ms) Note
  (Piechnik et al. 2013) 173 female (964, 21)
  169 male (943, 19)
5. LV blood:
  Study Cohort Size Gender Reference Value (ms) Note
  (Piechnik et al. 2013) 173 female (1577, 70)
  169 male (1491, 55)
6. RV blood:
  Study Cohort Size Gender Reference Value (ms) Note
  (Piechnik et al. 2013) 173 female (1567, 82)
  169 male (1461, 66)
Clinical Associations: Native T1 mapping carries prognostic value for a variety of conditions. T1 values are elevated in patients with myocardial edema (Vanessa M. Ferreira et al. 2012), myocarditis (Vanessa M. Ferreira et al. 2013; Vanessa M. Ferreira et al. 2014), MI (Piechnik et al. 2010; Dall’Armellina et al. 2012; Kali et al. 2014), amyloidosis (Karamitsos et al. 2013; Brooks, Kramer, and Salerno 2013; Barison et al. 2022), LV hypertrophy (Sado et al. 2013), and AS (Bull et al. 2013; Mahmod et al. 2014; Lee et al. 2015). Increased T1 values are also observed in patients with HCM, DCM, and cardiac sarcoidosis (Puyol-Antón et al. 2020). Conversely, T1 values are decreased in patients with AF (Puyol-Antón et al. 2020) and Anderson-Fabry disease (AFD) (Sado et al. 2013; Barison et al. 2022).
ICC:
1. Global: Note: 0.34
2. Intra-ventricular septum: Note: 0.34
3. Free wall: Note: 0.33
4. LV blood: 0.66
5. RV blood: 0.57

Study	Cohort Size	Gender	Reference Value (ms)
(Puyol-Antón et al. 2020)	932		(945, 54)
(Piechnik et al. 2013)	173	female	(973, 23)
	169	male	(950, 20)
(Kawel-Boehm et al. 2020)	971		(960, 29)

Study	Cohort Size	Reference Value (ms)	Note
(Puyol-Antón et al. 2020)	937	(952, 41)

Study	Cohort Size	Reference Value (ms)	Note
(Puyol-Antón et al. 2020)	930	(941, 65)

Study	Cohort Size	Gender	Reference Value (ms)	Note
(Piechnik et al. 2013)	173	female	(964, 21)
	169	male	(943, 19)

Study	Cohort Size	Gender	Reference Value (ms)	Note
(Piechnik et al. 2013)	173	female	(1577, 70)
	169	male	(1491, 55)

Study	Cohort Size	Gender	Reference Value (ms)	Note
(Piechnik et al. 2013)	173	female	(1567, 82)
	169	male	(1461, 66)

Extracellular Volume (ECV)*

Through the use of extracellular paramagnetic contrast agents, structural changes in the myocardium can be "amplified". For example, it is possible with T1 mapping performed before and after injection of a contrast agent to measure a parameter called the extracellular volume (ECV), which quantifies the relative expansion of the extracellular matrix as a result of diffuse reactive fibrosis in multiple cardiac pathologies (Taylor et al. 2016).

Myocardium can be grossly divided into 3 compartments: (1) an intracellular compartment consisting of myocytes, fibrolasts, endothelial cells, and smooth muscle cells; (2) an intra-vascular compartment (blood); and (3) an interstitial space (the residual space within the myocardium once the intra-cellular and intra-vascular compartments are removed. ECV comprises the interstitial and intra-vascular spaces, and, in general, it is assumed that changes in ECV are predominantly driven by changes in the interstitial volume fraction (Taylor et al. 2016).

<figure> <img src="/latex/images/myocardium/ECV.png" id="fig:ECV" alt="Myocardial T1 mapping is sensitive to changes in tissue structure and composition. The native myocardial T1 becomes longer with an increase of mobile water species, and/or interstitial decomposition of collagen, or amyloid protein. If T1 is performed before and after contrast administration, one can relate the changes of 1/T1 in myocardium to the corresponding change in blood to determine the myocardial tissue partition coefficient that corresponds to the slope of the line going through the measured values of 1/T1. This slope increases as a result of the expansion of the extracellular volume, e.g., as a result of diffuse interstitial fibrosis (Taylor et al. 2016)." /><figcaption aria-hidden="true">Myocardial T1 mapping is sensitive to changes in tissue structure and composition. The native myocardial T1 becomes longer with an increase of mobile water species, and/or interstitial decomposition of collagen, or amyloid protein. If T1 is performed before and after contrast administration, one can relate the changes of <span class="math inline">1/<em>T</em>1</span> in myocardium to the corresponding change in blood to determine the myocardial tissue partition coefficient that corresponds to the slope of the line going through the measured values of <span class="math inline">1/<em>T</em>1</span>. This slope increases as a result of the expansion of the extracellular volume, e.g., as a result of diffuse interstitial fibrosis <span>(Taylor et al. 2016)</span>.</figcaption> </figure>

<figure> <img src="/latex/images/myocardium/ECV_mapping.png" id="fig:ECV_mapping" alt="After gadolinium injection, ECV can be calculated from pre-contrast T1, post-contrast T1, and hematocrit: further tissue characterization is thus possible based on the different degree of extracellular volume expansion across different cardiomyopathies (Barison et al. 2022)." /><figcaption aria-hidden="true">After gadolinium injection, ECV can be calculated from pre-contrast T1, post-contrast T1, and hematocrit: further tissue characterization is thus possible based on the different degree of extracellular volume expansion across different cardiomyopathies <span>(Barison et al. 2022)</span>.</figcaption> </figure>

Definition: The proportion of the myocardial extracellular space relative to the total myocardial volume
Calculation:
Let the change of R1 = 1/T1 in blood and tissue is expressed as Δ**R<sub>1b</sub> and Δ**R<sub>1t</sub> respectively
$\text{ECV}=\frac{\Delta R_{1t}}{\Delta R_{1b}}\times (1-\text{Hct})$ where Hct refers to the hematocrit, the percentage of blood volume occupied by red blood cell (Taylor et al. 2016).
Acquisition Type: T1 mapping
Reference Range:
Study Cohort Size Reference Value (%) Note
(Kawel-Boehm et al. 2020) 295 (27, 3)
Clinical Associations: A number of disease processes that affect the myocardium can be understood on the basis of ECV changes. ECV is consistently elevated in patients with AS (Mahmod et al. 2014; Taylor et al. 2016), HCM (Taylor et al. 2016; Méndez et al. 2018; Barison et al. 2022), MI (Barison et al. 2022), valvular heart diseases and cardiomyopathy (Taylor et al. 2016).

Study	Cohort Size	Reference Value (%)	Note
(Kawel-Boehm et al. 2020)	295	(27, 3)

(Systolic Wall) Thickening

Definition: The relative incremental percentile value of wall thickness at end-systole compared with that at end-diastole (Cho et al. 2019).
Calculation: $\text{SWT}=\frac{\text{WT}_{\text{ES}}-\text{WT}_{\text{ED}}}{\text{WT}_{\text{ED}}}$ (Cho et al. 2019)
Acquisition Type: SAX, LAX
Reference Range:
Higher systolic wall thickening is found in lateral segments compared with anterior segments (Cho et al. 2019). When progressing from the base to the apex, there is a gradual increase in systolic wall thickening (Le Ven et al. 2016).
Reported normal values can be found in (Ubachs et al. 2009), (Dawson et al. 2011), (Le Ven et al. 2016) and (Cho et al. 2019)
Note: Thickening of segment 4 is lower than reference range.
Clinical Associations: Decreased thickening commonly occurs in patients with acute MI, chronic CAD and congestive cardiomyopathy (Corya et al. 1977).
ICC: Note: 0.33 (global average)

(Lagrangian) Strain

Deformation imaging has been shown to provide unique information on regional and global ventricular function (J.-U. Voigt et al. 2015). In cardiac imaging, the term strain is used to describe myocardial shortening and thickening, which are the fundamental features of myocardial fiber function. Strain imaging provides complementary information to LVEF, as it allows the quantification of segmental as well as global function, and can be used to assess both systolic and diastolic function (Smiseth et al. 2024).

Myocardial strain is a dimensionless measurement of myocardial deformation (Park 2019). Two common approaches are to use Lagrangian strain and natural strain. For the Lagrangian strain S<sub>L</sub>(t), a single reference length L<sub>0</sub> is defined, against which all subsequent deformation will be measured. Natural strain S<sub>N</sub>(t), on the other hand, employs a reference length that changes as the object deforms. It therefore describes the instantaneous length change (J.-U. Voigt et al. 2015). Formally, natural and Lagrangian strains are related so that one can be converted into the other: $$\begin{aligned} \text{S}_\text{L}(t)&=e^{\text{S}_\text{N}(t)}-1\\ \text{S}_\text{N}(t)&=\ln(\text{S}_\text{L}(t)+1)\\\end{aligned}$$

We will stick to the definition of Lagrangian strain throughout this documentation.

Clinically relevant strain values along strain curves are, but are not limited to (J.-U. Voigt et al. 2015):

End-systolic strain: The value at end-systole
Peak systolic strain: The peak value during systole
Positive peak systolic strain: A local myocardial stretching sometimes occurring to a minor extent in early systole, or as a relevant deformation in regional dysfunction.
Peak strain: The peak value during the entire heart cycle. The peak strain may coincide with the systolic or end-systolic peak, or may appear after the valve closure. In the latter case, it may be described as "post-systolic strain".

The earliest CMR methods used radio-frequency pulses and multiple saturation planes, which resulted in distinct lines in the myocardium that could be as a means of tissue tagging, as they follow myocardial deformation throughout the cardiac cycle. By the late 1980s, this has been refined to produce a 3D grid of tags known as a spatial modulation of magnetization. This method is considered the clinical gold standard for the validation of new strain methods. Despite great variability and some discrepancy from such "gold standard" tagging methods, as a result of simplicity of post-processing of standard cine images, feature tracking (FT) is now the dominant strain method used by the CMR community. More recently, other methods have emerged, including 3D FT and techniques such as displacement encoding with stimulated echoes and strain-encoded magnetic resonance imaging. The latter has superior spatial and temporal resolution compared with tagging and affords the opportunity of real-time imaging (Smiseth et al. 2024).

In cardiology, of special interest are strain and strain rate segmental and global values. The segmental strain is defined as the average value in the segment, while the global strain is calculated by using the entire myocardial line length while computing the deformation. Alternatively, global strain can also be computed by averaging the values computed at the segmental level from the same frame. If the global parameters are calculated by segmental averaging, the badly tracked segments can be excluded (J.-U. Voigt et al. 2015). For example, the global longitudinal strain (GLS) is a very reproducible measurement, as the value is obtained from a large part of the ventricle, and averaging data from multiple segments reduces effects of random signal noise and artifacts. In contrast, tracking segmental strain is performed in a much smaller region, making it more susceptible to regional image artifacts. Accordingly, single-segment strain values should be used with caution. When several adjacent segments have abnormal strains, these may be used to raise suspicion of region pathology. It is important to note that qualitative analysis of the patterns of segmental strain curves, as well as the bull’s eye display, should be preferred method for assessing regional strain (Smiseth et al. 2024).

The commonly measured myocardial strains include LV longitudinal and circumferential shortening strains, as well as radial thickening strain, as shown in the figure below. In addition to these orthogonal normal strains, a complete characterization of LV deformation includes 3 shear strains that are less commonly measured: circumferential-longitudinal shear (twist), circumferential-radial shear and radial-longitudinal shear (Smiseth et al. 2024).

Both longitudinal and circumferential strains also contribute to LV wall thickening. Measurements of radial and circumferential strains from the short-axis views are less feasible and have higher variability than longitudinal strain (Smiseth et al. 2024).

Longitudinal strain reflects systolic apex-to-base shortening (Jeung et al. 2012). It has been shown to be highly feasible, robust and reproducible, superior to many other conventional parameters. Particularly, left ventricle is the most common indication of the strain, and LV GLS is the most commonly used strain value (Park 2019). GLS is also a more sensitive parameter of systolic dysfunction than ejection fraction EF, because it primarily reflects the function of the vulnerable sub-endocardium, where ischemia and dysfunction occur earliest, while EF is less affected due to the dominant contribution of circumferential contractions and the preservation of EF in hypertrophic ventricles. Additionally, the elliptical geometry of the left ventricle means longitudinal shortening has a smaller impact on EF, allowing GLS to detect mild dysfunction before EF becomes abnormal (Smiseth et al. 2024).

<figure> <img src="/latex/images/myocardium/strain_type.png" id="fig:strain_type" alt="Figure illustrating three types of strain. In the long-axis plane, the systolic longitudinal deformation corresponds to apex-base shortening. In the short-axis plane, circumferential strain is tangential to the epicardial wall, and the radial strain is oriented toward the center of the LV cavity. When viewed from the apex, sections close to the apex have a counterclockwise systolic rotation, whereas sections close to the base have a clockwise rotation. Cd = circumferential strain in diastole, Cs = circumferential strain in systole, L_d = longitudinal strain in diastole, L_s = longitudinal strain in systole, R_d = radial strain in diastole, R_s = radial strain in systole (Jeung et al. 2012)." /><figcaption aria-hidden="true">Figure illustrating three types of strain. In the long-axis plane, the systolic longitudinal deformation corresponds to apex-base shortening. In the short-axis plane, circumferential strain is tangential to the epicardial wall, and the radial strain is oriented toward the center of the LV cavity. When viewed from the apex, sections close to the apex have a counterclockwise systolic rotation, whereas sections close to the base have a clockwise rotation. <span class="math inline"><em>C</em><em>d</em></span> = circumferential strain in diastole, <span class="math inline"><em>C</em><em>s</em></span> = circumferential strain in systole, <span class="math inline"><em>L</em><sub><em>d</em></sub></span> = longitudinal strain in diastole, <span class="math inline"><em>L</em><sub><em>s</em></sub></span> = longitudinal strain in systole, <span class="math inline"><em>R</em><sub><em>d</em></sub></span> = radial strain in diastole, <span class="math inline"><em>R</em><sub><em>s</em></sub></span> = radial strain in systole <span>(Jeung et al. 2012)</span>.</figcaption> </figure>

<figure> <img src="/latex/images/myocardium/AHA_segment3.jpg" id="fig:strain_segmentation" alt="Standardized myocardial segmentation and nomenclature." /><figcaption aria-hidden="true">Standardized myocardial segmentation and nomenclature.</figcaption> </figure>

Definition:
- Longitudinal strain: Deformation component parallel to the reference contour, viewed in from base to the apex (J.-U. Voigt et al. 2015).
- Radial strain: Deformation component perpendicular to the reference contour, viewed from the contour towards the LV cavity (J.-U. Voigt et al. 2015).
- Circumferential strain: Deformation component tangential to the reference contour, perpendicular to the LV long axis, with counterclockwise orientation when viewed from the apex (J.-U. Voigt et al. 2015).
Calculation: Define a single reference length L<sub>0</sub> at the reference time t<sub>0</sub> (usually at end-diastole), then $\text{S}_\text{L}(t)=\frac{L(t)-L_0}{L_0}$ (J.-U. Voigt et al. 2015).
Acquisition Type: SAX, LAX, Tagged MRI

Reference Range (global strain):

Longitudinal (measured in LAX):

Study	Cohort Size	Gender	Age	Reference Value (%)	Note
(Sugimoto et al. 2017)	227	male		(-21.7, 2.5)
	322	female		(-23.0, 2.7)
(Takigiku et al. 2012)	333	208 males, 125 females		(-21.3, 2.1)	GE vendor
	330	195 males, 135 females		(-18.9, 2.5)	Philips vendor
	337	235 males, 102 females		(-19.9, 2.4)	Toshiba vendor
(Mora et al. 2018)	52	male		(-20.7, 2.0)
	38	female		(-21.7, 2.1)
(Park et al. 2016)	236	male		(-19.5, 1.9)
	265	female		(-21.2, 2.2)
(Yingchoncharoen et al. 2013)	2597			(-20.4)-(-18.9)
(Kawel-Boehm et al. 2020)	295	male		(-19.4, 3.3)	measured using 2D FT
	301	female		(-21.4, 3.6)	measured using 2D FT
	100			(-14.6, 2.7)	measured using 3D FT
(Ruijsink et al. 2020)	304	male	45-54	(-22)-(-11)	measured in 2ch LAX
	384	male	55-64	(-22)-(-11)	measured in 2ch LAX
	241	male	65-74	(-23)-(-11)	measured in 2ch LAX
	297	female	45-54	(-22)-(-11)	measured in 2ch LAX
	322	female	55-64	(-22)-(-10)	measured in 2ch LAX
	213	female	65-74	(-21)-(-11)	measured in 2ch LAX
	304	male	45-54	(-21)-(-10)	measured in 4ch LAX
	384	male	55-64	(-22)-(-9)	measured in 4ch LAX
	241	male	65-74	(-22)-(-10)	measured in 4ch LAX
	297	female	45-54	(-21)-(-9)	measured in 4ch LAX
	322	female	55-64	(-21)-(-10)	measured in 4ch LAX
	213	female	65-74	(-21)-(-10)	measured in 4ch LAX
(Liu et al. 2017)	100			(-20)-(-9)
(Oxborough, George, and Birch 2012)	20			(-17.6, 2.0)	scan 1
	20			(-17.4, 2.1)	scan 2
(Moreira et al. 2017)	521			(-16.6, 2.3)	measured in 2ch LAX
	521			(-15.9, 2.2)	measured in 4ch LAX
(Saijo et al. 2022)	16			(-21.0, 1.4)
(Kimura et al. 2011)	137			(-16.5, 3.7)
(Wu et al. 2014)	10			(-27.9)-(-23.9)
(Schuster et al. 2015)	10			(-20.6)-(-17.0)
(Nucifora et al. 2015)	15			(-23.5)-(-20.5)
(Heiberg et al. 2015)	28			(-25.8)-(-23.6)
(Andre et al. 2015)	150			(-27.2)-(-25.8)
(Augustine et al. 2013)	54	male		(-20, 2)
	62	female		(-21, 3)

Circumferential (measured in SAX):

Study	Cohort Size	Gender	Age	Reference Value (%)	Note
(Sugimoto et al. 2017)	227	male		(-32.2, 4.4)
	322	female		(-32.2, 4.4)
(Takigiku et al. 2012)	333	208 males, 125 females		(-22.8, 2.9)	GE vendor
	330	195 males, 135 females		(-22.2, 3.2)	Philips vendor
	337	235 males, 102 females		(-30.5, 3.8)	Toshiba vendor
(Mora et al. 2018)	52	male		(-21.9, 4.3)
	38	female		(-21.3, 3.4)
(Yingchoncharoen et al. 2013)	2597			(-24.6)-(-22.1)
(Kawel-Boehm et al. 2020)	295	male		(-20.9, 3.2)	measured using 2D FT
	301	female		(-22.7, 3.3)	measured using 2D FT
	100			(-17.6, 2.6)	measured using 3D FT
(Ruijsink et al. 2020)	304	male	45-54	(-26)-(-14)
	384	male	55-64	(-26)-(-14)
	241	male	65-74	(-25)-(-15)
	297	female	45-54	(-26)-(-14)
	322	female	55-64	(-26)-(-14)
	213	female	65-74	(-26)-(-14)
(Liu et al. 2017)	100			(-23)-(-13)
(Oxborough, George, and Birch 2012)	20			(-18.3, 2.4)	scan 1
	20			(-18.5, 2.9)	scan 2
(Moreira et al. 2017)	521			(-15.7, 2.6)
(Wu et al. 2014)	10			(-27.9)-(-23.9)
(Schuster et al. 2015)	10			(-20.6)-(-17.0)
(Nucifora et al. 2015)	15			(-23.5)-(-20.5)
(Heiberg et al. 2015)	28			(-25.8)-(-23.6)
(Andre et al. 2015)	150			(-27.2)-(-25.8)
(Augustine et al. 2013)	54	male		(-20, 2)
	62	female		(-21, 3)

Radial (measured in SAX):

Study	Cohort Size	Gender	Age	Reference Value (%)	Note
(Sugimoto et al. 2017)	227	male		(36.3, 8.0)
	322	female		(38.2, 8.5)
(Takigiku et al. 2012)	333	208 males, 125 females		(54.6, 12.6)	GE vendor
	330	195 males, 135 females		(36.3, 8.2)	Philips vendor
	337	235 males, 102 females		(51.4, 8.0)	Toshiba vendor
(Mora et al. 2018)	52	male		(34.0, 9.9)
	38	female		(32.8, 10.7)
(Yingchoncharoen et al. 2013)	2597			43.6-51.0
(Ruijsink et al. 2020)	304	male	45-54	27-68
	384	male	55-64	30-66
	241	male	65-74	28-70
	297	female	45-54	24-68
	322	female	55-64	28-69
	213	female	65-74	27-68
(Liu et al. 2017)	100			22-73
(Oxborough, George, and Birch 2012)	20			(39.8, 11.6)	scan 1
	20			(40.1, 16.7)	scan 2
(Moreira et al. 2017)	521			(36.6, 11.0)
(Kimura et al. 2011)	137			(56.3, 22.6)
(Schuster et al. 2015)	10			28.0-35.8
(Heiberg et al. 2015)	28			60.8-68.2
(Andre et al. 2015)	150			32.9-36.1
(Augustine et al. 2013)	54	male		(23, 4)
	62	female		(22, 6)
(Fine et al. 2013)	186			(44.8, 21.7)

Clinical Associations: Reduced longitudinal strain has been widely reported across a broad spectrum of cardiovascular conditions, including severe CAD (Park 2019; Sharifov et al. 2023), ventricular arrhythmia (Tang et al. 2017; Smiseth et al. 2024), hypertension (Erdei et al. 2022), MI (Hoit 2011; Jeung et al. 2012; Huttin et al. 2016; Park 2019), DCM (Jeung et al. 2012; Moody et al. 2015), HCM (Jeung et al. 2012; Park 2019; Smiseth et al. 2024), HF (Jeung et al. 2012; Park 2019; Smiseth et al. 2024), and valvular diseases such as AS and MR (Scatteia, Baritussio, and Bucciarelli-Ducci 2017; Park 2019). Reduced longitudinal strain is also evident in patients with amyloidosis and LVNC when compared with healthy controls (Park 2019; Bogunovic et al. 2022). The guideline for cardio-oncology suggest that a reduction in LV GLS>15% from the baseline could suggest the risk of chemotherapeutic-agent-associated cardiotoxicity (Park 2019; Smiseth et al. 2024). Importantly, longitudinal strain has demonstrated superior prognostic value over LVEF for predicting sudden cardiac death in patients with implantable cardioverter defibrillators (ICDs) (Haugaa et al. 2010; Barros et al. 2016; Candan et al. 2017).
While longitudinal strain has the strongest evidence base, circumferential and radial strains may also provide essential complementary information in certain contexts. In the short-axis view, circumferential strain reflects tangential shortening of the myocardial wall, whereas radial strain represents myocardial thickening directed toward the center of the LV cavity (Jeung et al. 2012). Both circumferential and radial strains are reduced in ischemia, MI, and DCM (Jeung et al. 2012). Additionally, patients with ventricular arrhythmia exhibit diminished peak circumferential and radial strains in the basal and mid-LV regions (Tang et al. 2017). Circumferential strain is also impaired in patients with HF and AS (Jeung et al. 2012; Park 2019; Scatteia, Baritussio, and Bucciarelli-Ducci 2017).
ICC:
- Longitudinal (measured in LAX): 0.54 (global), 0.50-0.58 (segmental)
- Circumferential (measured in SAX): 0.68 (global), 0.43-0.62 (segmental)
- Radial (measured in SAX): 0.60 (global), 0.38-0.62 (segmental)
- Circumferential (measured in Tagged MRI): <span style="color: red">0.21 (global), 0.12-0.16 (segmental)</span>
- Radial (measured in Tagged MRI): Note: 0.23 (global), 0.08-0.15 (segmental)

Strain Rate

A variety of parameters can be derived along with these strains. Strain rate (SR) describes the rate of the deformation, i.e., how fast the deformation occurs. The Lagrangian strain rate is simply the derivative of the Lagrangian strain (J.-U. Voigt et al. 2015).

Definition: The velocity of deformation over time (J.-U. Voigt and Flachskampf 2004).
Calculation: $\text{SR}_\text{L}(t)=\frac{d\text{S}_L}{\text{dt}}$ (J.-U. Voigt and Flachskampf 2004).
Acquisition Type: SAX, LAX, Tagged MRI

Reference Range:

Longitudinal (measured in LAX):

Peak systolic:

Study	Cohort Size	Reference Value (1/s)	Note
(Liu et al. 2017)	100	(-1.09)-(-0.43)
(Oxborough, George, and Birch 2012)	20	(-0.88, 0.22)	scan 1
	20	(-0.90, 0.14)	scan 2
(Moreira et al. 2017)	391	(-0.70, 0.11)	measured in 2ch LAX
	521	(-0.68, 0.10)	measured in 4ch LAX
(Bogunovic et al. 2022)	150	(-1.23, 0.31)
(Zhao et al. 2025)	139	(-0.61, 0.39)
(Farsalinos et al. 2013)	38	(-1.13, 0.1)

Early diastolic:

Study	Cohort Size	Reference Value (1/s)	Note
(Liu et al. 2017)	100	0.36-1.30
(Oxborough, George, and Birch 2012)	20	(1.16, 0.32)	scan 1
	20	(1.12, 0.25)	scan 2
(Moreira et al. 2017)	391	(0.99, 0.26)	measured in 2ch LAX
	509	(0.88, 0.23)	measured in 4ch LAX
(Farsalinos et al. 2013)	38	(1.54, 0.27)

Late diastolic:

Study	Cohort Size	Reference Value (1/s)	Note
(Liu et al. 2017)	100	(0.18)-(0.68)
(Oxborough, George, and Birch 2012)	20	(0.54, 0.09)	scan 1
	20	(0.54, 0.09)	scan 2
(Moreira et al. 2017)	391	0.39-0.60	measured in 2ch LAX
	509	0.40-0.63	measured in 4ch LAX
(Farsalinos et al. 2013)	38	(-0.88, 0.20)

Circumferential (measured in SAX):

Peak systolic:

Study	Cohort Size	Reference Value (1/s)	Note
(Liu et al. 2017)	100	(-1.31)-(-0.53)
(Oxborough, George, and Birch 2012)	20	(-1.24, 0.27)	scan 1
	20	(-1.23, 0.21)	scan 2
(Moreira et al. 2017)	521	(-0.70, 0.13)
(Zhao et al. 2025)	139	(-0.83, 0.39)

Early diastolic:
Study Cohort Size Gender Age Reference Value (1/s) Note
(Liu et al. 2017) 100 0.47-1.45
(Oxborough, George, and Birch 2012) 20 (1.74, 0.34) scan 1
20 (1.71, 0.41) scan 2
(Moreira et al. 2017) 521 (0.84, 0.30)
Late diastolic:
Study Cohort Size Gender Age Reference Value (1/s) Note
(Liu et al. 2017) 100 0.17-0.79
(Oxborough, George, and Birch 2012) 20 (0.38, 0.15) scan 1
20 (0.38, 0.18) scan 2
(Moreira et al. 2017) 521 0.28-0.53

Study	Cohort Size	Reference Value (1/s)	Note
(Liu et al. 2017)	100	0.47-1.45
(Oxborough, George, and Birch 2012)	20	(1.74, 0.34)	scan 1
	20	(1.71, 0.41)	scan 2
(Moreira et al. 2017)	521	(0.84, 0.30)

Study	Cohort Size	Reference Value (1/s)	Note
(Liu et al. 2017)	100	0.17-0.79
(Oxborough, George, and Birch 2012)	20	(0.38, 0.15)	scan 1
	20	(0.38, 0.18)	scan 2
(Moreira et al. 2017)	521	0.28-0.53

Radial (measured in SAX):

Peak systolic:

Study	Cohort Size	Reference Value (1/s)	Note
(Liu et al. 2017)	100	0.77-5.10
(Oxborough, George, and Birch 2012)	20	(1.85, 0.33)	scan 1
	20	(1.92, 0.49)	scan 2
(Moreira et al. 2017)	521	(1.69, 0.54)
(Zhao et al. 2025)	139	(1.38, 1.49)

Early diastolic:

Study	Cohort Size	Reference Value (1/s)	Note
(Liu et al. 2017)	100	(-5.10)-(-1.00)
(Oxborough, George, and Birch 2012)	20	(-1.98, 0.53)	scan 1
	20	(-1.80, 0.39)	scan 2
(Moreira et al. 2017)	521	(-3.06)-(-1.42)

Late diastolic:

Study	Cohort Size	Reference Value (1/s)	Note
(Liu et al. 2017)	100	(-1.20)-(-0.17)
(Oxborough, George, and Birch 2012)	20	(-0.94, 0.38)	scan 1
	20	(-0.92, 0.34)	scan 2
(Moreira et al. 2017)	521	(-1.44)-(-0.62)

Clinical Associations: Potentially, peak systolic strain rate may outperform peak strain as a measure of LV contractility, as it is less influenced by changes in cardiac load and structure (Smiseth et al. 2024). Radial strain rate of the posterior wall is reduced in patients with restrictive cardiomyopathy predominantly caused by amyloidosis, and peak longitudinal strain rate is also found to be lower in patients with amyloidosis (J.-U. Voigt and Flachskampf 2004). Peak systolic and early diastolic longitudinal strain rate can predict coronary artery stenosis (CAS) (Hoit 2011). The early diastolic longitudinal strain rate can also predict a response to heart failure therapy in patients with DCM (Goebel et al. 2014).
ICC:
- Longitudinal (measured in LAX): Note: 0.30 (early-diastolic), 0.27 (late-diastolic), 0.37 (peak systolic)
- Circumferential (measured in SAX): 0.48 (early-diastolic), 0.54 (late-diastolic), 0.57 (peak systolic)
- Radial (measured in SAX): Note: 0.35 (early-diastolic), 0.35 (late-diastolic), 0.39 (peak systolic)
- Circumferential (measured in Tagged MRI): <span style="color: red">0.25 (early-diastolic), 0.22 (peak systolic)</span>
- Radial (measured in Tagged MRI): Note: 0.17 (early-diastolic), 0.15 (peak systolic)

Time to Peak Strain

Definition: Time from R-wave in ECG to peak systolic strain (Bogunovic et al. 2022). It can be further indexed to the R-R interval duration to yield an unitless parameter (Asanuma et al. 2012).
Acquisition Type: SAX, LAX, Tagged MRI

Reference Range:

Longitudinal (measured in LAX):

Study	Cohort Size	Gender	Age	Reference Value (ms)	Note
(Ruijsink et al. 2020)	304	male	45-54	288-451	measured in 2ch LAX
	384	male	55-64	283-452	measured in 2ch LAX
	241	male	65-74	300-446	measured in 2ch LAX
	297	female	45-54	277-451	measured in 2ch LAX
	322	female	55-64	281-451	measured in 2ch LAX
	213	female	65-74	277-458	measured in 2ch LAX
	304	male	45-54	288-455	measured in 4ch LAX
	384	male	55-64	281-450	measured in 4ch LAX
	241	male	65-74	282-445	measured in 4ch LAX
	297	female	45-54	274-451	measured in 4ch LAX
	322	female	55-64	278-453	measured in 4ch LAX
	213	female	65-74	284-453	measured in 4ch LAX
(Oxborough, George, and Birch 2012)	20			(390, 40)	scan 1
	20			(400, 50)	scan 2
(Bogunovic et al. 2022)	150			(371, 42)

Circumferential (measured in SAX):

Study	Cohort Size	Gender	Age	Reference Value (ms)	Note
(Ruijsink et al. 2020)	304	male	45-54	280-423	measured in 2ch LAX
	384	male	55-64	279-420	measured in 2ch LAX
	241	male	65-74	291-408	measured in 2ch LAX
	297	female	45-54	278-413	measured in 2ch LAX
	322	female	55-64	279-416	measured in 2ch LAX
	213	female	65-74	277-417	measured in 2ch LAX
(Oxborough, George, and Birch 2012)	20			(380, 50)	scan 1
	20			(370, 50)	scan 2
(Wu et al. 2014)	10			(336, 34)	healthy control

Radial (measured in SAX):

Study	Cohort Size	Gender	Age	Reference Value (ms)	Note
(Ruijsink et al. 2020)	304	male	45-54	276-413	measured in 4ch LAX
	384	male	55-64	276-403	measured in 4ch LAX
	241	male	65-74	286-403	measured in 4ch LAX
	297	female	45-54	275-401	measured in 4ch LAX
	322	female	55-64	275-407	measured in 4ch LAX
	213	female	65-74	271-408	measured in 4ch LAX
(Oxborough, George, and Birch 2012)	20			(420, 50)	scan 1
	20			(400, 50)	scan 2

Clinical Associations: The time to peak strain value, particularly the one for longitudinal strain, is reduced in patients with amyloidosis (Bogunovic et al. 2022). Ischemia can also lead to delay in systolic strain, also known as the "tardokinesia" (J.-U. Voigt and Flachskampf 2004).
ICC:
- Longitudinal (measured in LAX): Note: 0.31 (unindexed), 0.37 (indexed)
- Circumferential (measured in SAX): Note: 0.41 (unindexed), 0.39 (indexed)
- Radial (measured in SAX): Note: 0.18 (unindexed), 0.22 (indexed)

Strain Imaging Diastolic Index (SI-DI)

Another parameter, the strain imaging diastolic index (SI-DI), measures the relative change of strain values from aortic valve closure (AVC) to that at one-third of diastole (Ishii et al. 2009).

<figure> <img src="/latex/images/myocardium/SI-DI.png" id="fig:SI-DI" alt="Both strain values at aortic valve closure (A) and at one-third of diastole duration are measured. The strain imaging diastolic index (SI-DI) is calculated as: (A-B)/A\times 100%. 1/3 DD = one-third of diastole duration; AVC = aortic valve closure (Ishii et al. 2009)." /><figcaption aria-hidden="true">Both strain values at aortic valve closure (A) and at one-third of diastole duration are measured. The strain imaging diastolic index (SI-DI) is calculated as: <span class="math inline">(<em>A</em>−<em>B</em>)/<em>A</em> × 100%</span>. 1/3 DD = one-third of diastole duration; AVC = aortic valve closure <span>(Ishii et al. 2009)</span>.</figcaption> </figure>

Definition and Calculation: (A−B)/A × 100% where A is the end systolic values of strain at the closure of the aortic valve and B is the one at the one-third point of diastole duration (Ishii et al. 2009).
Acquisition Type: SAX, LAX, Tagged MRI
Clinical Associations: The radial and longitudinal SI-DI can predict CAS as the values will decrease significantly (Kimura et al. 2011).
ICC:
- Longitudinal (measured in LAX): <span style="color: red">0.38</span>
- Circumferential (measured in SAX): 0.67
- Radial (measured in SAX): Note: 0.37

Pre-systolic Stretch Index

Certain metrics have been developed uniquely for the longitudinal strain, as it is of greater importance. Pre-systolic stretch index and post-systolic index (PSI) describe the early systolic stretching relative to the total combined shortening and the relative amount of total shortening after AVC (Bogunovic et al. 2022).

Definition: Early systolic stretching relative to the total combined shortening, equivalent to the sum of systolic stretching and shortening.
Calculation: Positive peak systolic strain/Positive peak systolic strain - Negative peak systolic strain × 100% (Bogunovic et al. 2022).
Acquisition Type: SAX, LAX, Tagged MRI
Reference Range:
Study Cohort Size Gender Age Reference Value (%) Note
(Bogunovic et al. 2022) 150 (1.3, 2.8)
(Coiro et al. 2017) 50 (1.38, 2.22)
Clinical Associations: Pre-systolic stretch (PSI) is significantly higher in patients with HCM (Saijo et al. 2022) and HF (Brainin et al. 2019). It is also increased in patients with ST-segment elevation MI (Huttin et al. 2016) and those with amyloidosis and LVNC (Bogunovic et al. 2022).
ICC: Note: 0.38

Study	Cohort Size	Gender	Age	Reference Value (%)	Note
(Bogunovic et al. 2022)	150			(1.3, 2.8)
(Coiro et al. 2017)	50			(1.38, 2.22)

Post Systolic Index (PSI)

In addition to diagnose diseases by showing reduction in systolic shortening (hypokinesia), what is equally important is the demonstration of systolic lengthening and post systolic shortening (Smiseth et al. 2024). Post-systolic shortening (PSS) is a phenomenon of regional contraction occurring after end-systole. It is commonly used as a marker of ischemia, and can occur in segments of prior ischemic damage (Saijo et al. 2022).

If a single wall segment within a myocardial wall exhibits PSI greater than 20%, then it will be considered as having PSS (Brainin et al. 2019).

<figure> <img src="/latex/images/myocardium/post_systolic_shortening.png" id="fig:post_systolic_shortening" alt="A single time-delayed segmental strain curves with values of peak end-systolic strain and maximum post-systolic strain marked by arrows. From these values, segmental post systolic strain index can be calculated. If no regional post systolic strain present, its post-systolic strain is 0. Time to peak interval, used to calculate mechanical dispersion index, is represented by a broken arrow (Saijo et al. 2022)." /><figcaption aria-hidden="true">A single time-delayed segmental strain curves with values of peak end-systolic strain and maximum post-systolic strain marked by arrows. From these values, segmental post systolic strain index can be calculated. If no regional post systolic strain present, its post-systolic strain is 0. Time to peak interval, used to calculate mechanical dispersion index, is represented by a broken arrow <span>(Saijo et al. 2022)</span>.</figcaption> </figure>

Definition: Relative amount of total shortening after aortic valve closure (Bogunovic et al. 2022).
Calculation: (Peak strain-Peak systolic strain)/Peak strain × 100% (Brainin et al. 2019; Saijo et al. 2022).
Acquisition Type: SAX, LAX, Tagged MRI

Reference Range:

Study	Cohort Size	Reference Value (%)	Note
(Bogunovic et al. 2022)	150	(2.5, 3.1)
(Saijo et al. 2022)	16	(2.1, 0.6)
(Brainin et al. 2018)	843	(1.2, 3.0)	no post systolic shortening
(Coiro et al. 2017)	50	(1.13, 2.07)

Clinical Associations: Although identified in healthy volunteers as well, the PSS is more pronounced during ischemia (J.-U. Voigt and Flachskampf 2004) and in patients with HF (Brainin et al. 2019). Similar to pre systolic stretch index, post systolic shortening index is also increased in patients with ST-segment elevation MI (Huttin et al. 2016) and those with amyloidosis and LVNC (Bogunovic et al. 2022). In addition, Ischemia leads to new occurrence of, or increase in post-systolic shortening during the isovolumetric relaxation period, which accounts for an increasing fraction of total shortening (J.-U. Voigt and Flachskampf 2004). PSI is also observed in patients with DCM instead of normal shortening (Jeung et al. 2012).
ICC: Note: 0.30

Mechanical Dispersion

Post systolic index is also intrinsically related to mechanical dyssynchrony. The presence of post systolic shortening by definition means an increase in mechanical dispersion, a dyssynchrony measurement that represents the standard deviation of time-to-peak of segmental strains. On the other hand, increased mechanical dispersion also means the presence of post systolic shortening, because contraction of some segments must have occurred after end-systole (Saijo et al. 2022).

<figure> <img src="/latex/images/myocardium/mechanical_dispersion.png" id="fig:mechanical_dispersion" alt="Mechanical dispersion index is calculated as the standard deviation of segmental time-to-peak intervals. By default, absence of mechanical dispersion means absence of post-systolic shortening and vice versa (Saijo et al. 2022)." /><figcaption aria-hidden="true">Mechanical dispersion index is calculated as the standard deviation of segmental time-to-peak intervals. By default, absence of mechanical dispersion means absence of post-systolic shortening and vice versa <span>(Saijo et al. 2022)</span>.</figcaption> </figure>

Definition: Time to peak negative interval is measured from the peak of the Q wave or R wave of the ECG to the peak negative strain in multiple LV segments. Mechanical dispersion index is then calculated as the standard deviation of all LV segmental time-to-peak intervals (Saijo et al. 2022; Smiseth et al. 2024).
Acquisition Type: SAX, LAX, Tagged MRI

Reference Range:

Note: Mechanical dispersion is larger than reference range.

Study	Cohort Size	Reference Value (ms)	Note
(Saijo et al. 2022)	16	(38, 7)
(Bogunovic et al. 2022)	150	(32, 8)
(Haugaa et al. 2010)	23	(22, 10)
(Barros et al. 2016)	34	(49, 21)
(Schnell et al. 2017)	36	(30.8, 13.7)	secondary control
	36	(30.7, 9.1)	healthy athlete
(Zhao et al. 2025)	139	(68.1, 25.0)

Clinical Associations: Larger mechanical dispersion, indicating higher inter-segmental variability, is associated with higher risk of HF (Zhao et al. 2025), ventricular arrhythmias (Park 2019; Smiseth et al. 2024; Zhao et al. 2025), HCM (Jeung et al. 2012; Schnell et al. 2017; Saijo et al. 2022), amyloidosis (Saijo et al. 2022) and ventricular aneurysm (VA) (Jeung et al. 2012). It is more pronounced in patients receiving ICD therapy after HCM, MI and Chagas disease (Haugaa et al. 2010; Barros et al. 2016; Candan et al. 2017).
ICC: Note: 0.31

Torsion

Left ventricular twist describes the systolic rotational deformation resulting from opposing apical and basal rotations. When viewed from the ventricular apex toward the base, the apex rotates counterclockwise while the base rotates clockwise, generating a net twisting motion of the myocardium (Esch and Warburton 2009). This phenomenon contributes substantially to left ventricular ejection, alongside atrioventricular plane displacement and radial contraction. Quantitatively, myocardial rotation is defined as the angular displacement of myocardial points around the ventricular long axis relative to end-diastole, with twist calculated as the difference between apical and basal rotations (Jeung et al. 2012). Torsion further normalizes this twist angle by the distance between apical and basal imaging planes, accounting for ventricular length and enabling inter-subject comparison (Kowallick et al. 2014; Smiseth et al. 2024).

<figure> <img src="/latex/images/myocardium/rotational_displacement.png" id="fig:rotational_displacement" alt="3D model of LV rotational displacement. Rotation between time points (angular differences between red and green contours) is computed in each slice, and it is then linearly interpolated between slices (Kowallick et al. 2014)." /><figcaption aria-hidden="true">3D model of LV rotational displacement. Rotation between time points (angular differences between red and green contours) is computed in each slice, and it is then linearly interpolated between slices <span>(Kowallick et al. 2014)</span>.</figcaption> </figure>

<figure> <img src="/latex/images/myocardium/twist.png" id="fig:twist" alt="Illustration of LV circumferential-longitudinal shear strain that is usually referred to as the twist. Twist is calculated as the difference between rotations at the apex and base (Smiseth et al. 2024)." /><figcaption aria-hidden="true">Illustration of LV circumferential-longitudinal shear strain that is usually referred to as the twist. Twist is calculated as the difference between rotations at the apex and base <span>(Smiseth et al. 2024)</span>.</figcaption> </figure>

<figure> <img src="/latex/images/myocardium/torsion.png" id="fig:torsion" alt="Definitions to calculate torsion. CMR feature tracking is performed in all slices of a short-axis stack. Four models to calculate torsion are evaluated. LV torsion is calculated as the difference in counter-clockwise apical rotation (\phi_{apex}) and clockwise rotation at the base (\phi_{base}) divided by the inter-slice distance (D). The rotation points at 0% and 100 % correspond to the most apical and most basal levels. The rotation of points at 25% and 75% distance correspond to points that are typically located in between slices (Kowallick et al. 2014)." /><figcaption aria-hidden="true">Definitions to calculate torsion. CMR feature tracking is performed in all slices of a short-axis stack. Four models to calculate torsion are evaluated. LV torsion is calculated as the difference in counter-clockwise apical rotation (<span class="math inline"><em>ϕ</em><sub><em>a</em><em>p</em><em>e</em><em>x</em></sub></span>) and clockwise rotation at the base (<span class="math inline"><em>ϕ</em><sub><em>b</em><em>a</em><em>s</em><em>e</em></sub></span>) divided by the inter-slice distance (<span class="math inline"><em>D</em></span>). The rotation points at 0% and 100 % correspond to the most apical and most basal levels. The rotation of points at 25% and 75% distance correspond to points that are typically located in between slices <span>(Kowallick et al. 2014)</span>.</figcaption> </figure>

Definition and Calculation:
- Simple twist ("twist"): Subtraction of basal clockwise rotation from apical counter-clockwise rotation (Kowallick et al. 2014, 2016).
  Twist = ϕ<sub>apex</sub> − ϕ<sub>base</sub>
- Twist normalized to LV length ("torsion"): Twist divided by the distance between apical and basal imaging planes, as calculated from the sum of inter-slice gaps and slice thickness (Kowallick et al. 2014, 2016).
  $\text{Normalized twist}=\frac{\phi_{\text{apex}}-\phi_{\text{base}}}{D}$
Acquisition Type: SAX, LAX, Tagged MRI

Reference Range (Global):

Study	Cohort Size	Gender	Age	Reference Value (/cm)	Note
(Kowallick et al. 2014)	10			(2.7, 1.5)	model 1
	10			(3.2, 1.2)	model 2
	10			(3.1, 1.5)	model 3
	10			(3.0, 1.3)	model 4
(Yoneyama et al. 2012)	792	male		(3.5, 1.1)
	686	female		(4.2, 1.3)
(Steinmetz et al. 2016)	31			(1.45, 1.00)
(Zhang et al. 2024)	48	male	51-60	(1.03, 0.37)
	28	male	61-70	(1.25, 0.42)
	34	male	51-60	(1.33, 0.42)
	31	male	61-70	(1.47, 0.31)

Clinical Associations: Left ventricular torsion exhibits distinct alterations across cardiovascular disease phenotypes. Torsion is typically increased in patients with hypertrophic cardiomyopathy and aortic stenosis, reflecting augmented systolic twisting in the presence of concentric remodeling and pressure overload (Götte et al. 2006; Esch and Warburton 2009). In contrast, reduced torsion has been consistently observed in patients following myocardial infarction and in dilated cardiomyopathy, where impaired myocardial contractility and altered ventricular geometry limit effective systolic rotation. In dilated cardiomyopathy, torsion is not only reduced in magnitude but also characterized by an earlier time to peak torsion, indicating altered systolic mechanics (Jeung et al. 2012; Young and Cowan 2012).
ICC:
- Global: 0.50
- Endocardial: 0.50
- Epicardial: 0.49

Peak Recoil Rate

During early diastole, the left ventricle undergoes rapid untwisting in the opposite direction of systolic twist, a process commonly referred to as diastolic recoil. This recoil reflects the release of stored elastic energy accumulated during systole and plays a key role in ventricular relaxation and early diastolic filling (Esch and Warburton 2009). Impaired untwisting dynamics have been associated with diastolic dysfunction, highlighting recoil-related parameters as sensitive markers of abnormal diastolic mechanics (Kowallick et al. 2016; Smiseth et al. 2024).

<figure> <img src="/latex/images/myocardium/recoil_rate.png" id="fig:recoil_rate" alt="Evaluation of rotation. Rotational displacement of voxels are tracked throughout the cardiac cycle. Left ventricular torsion is calculated as the difference in counter-clockwise (positive) apical rotation and clockwise (negative) rotation at the base divided by the inter-slice distance (Kowallick et al. 2014)." /><figcaption aria-hidden="true">Evaluation of rotation. Rotational displacement of voxels are tracked throughout the cardiac cycle. Left ventricular torsion is calculated as the difference in counter-clockwise (positive) apical rotation and clockwise (negative) rotation at the base divided by the inter-slice distance <span>(Kowallick et al. 2014)</span>.</figcaption> </figure>

Definition: Maximum slope ( − d**θ/d**t) of the diastolic limb of the torsion-time curve (Kowallick et al. 2014).
Acquisition Type: SAX, LAX, Tagged MRI

Reference Range (Global):

Study	Cohort Size	Gender	Age	Reference Value (/cm/s)	Note
(Kowallick et al. 2014)	10			(-30.1, 11.1)	model 1
	10			(-36.3, 12.7)	model 2
	10			(-33.5, 9.1)	model 3
	10			(-38.5, 16.4)	model 4
(Ambale-Venkatesh et al. 2014)	1544			(-19, 11)
(Steinmetz et al. 2016)	31			(-12.47, 6.61)
(Zhang et al. 2024)	48	male	51-60	(-6.17, 2.40)
	28	male	61-70	(-7.59, 2.58)
	34	male	51-60	(-8.26, 3.16)
	31	male	61-70	(-10.09, 3.10)

Clinical Associations: Diastolic recoil, reflecting the rate and extent of ventricular untwisting during early diastole, is similarly affected in pathological conditions. Patients with heart failure with reduced ejection fraction exhibit marked reductions in recoil rate, consistent with impaired elastic energy release and delayed relaxation (Esch and Warburton 2009). In contrast, pressure-overload conditions such as aortic stenosis are associated with prolonged untwisting and delayed recoil, despite preserved or increased systolic torsion, highlighting a dissociation between systolic twist and diastolic recoil in these patients (Götte et al. 2006; Esch and Warburton 2009; Jeung et al. 2012; Young and Cowan 2012).
ICC:
- Global: Note: 0.30
- Endocardial: Note: 0.28
- Epicardial: Note: 0.31

Ambale-Venkatesh, B., A. C. Armstrong, C.-Y. Liu, S. Donekal, K. Yoneyama, C. O. Wu, A. S. Gomes, G. W. Hundley, D. A. Bluemke, and J. A. Lima. 2014. “Diastolic Function Assessed from Tagged MRI Predicts Heart Failure and Atrial Fibrillation over an 8-Year Follow-up Period: The Multi-Ethnic Study of Atherosclerosis.” European Heart Journal - Cardiovascular Imaging 15 (4): 442–49. https://doi.org/10.1093/ehjci/jet189.

</div>

Andre, Florian, Henning Steen, Philipp Matheis, Maria Westkott, Kristin Breuninger, Yannick Sander, Rebekka Kammerer, et al. 2015. “Age- and Gender-Related Normal Left Ventricular Deformation Assessed by Cardiovascular Magnetic Resonance Feature Tracking.” Journal of Cardiovascular Magnetic Resonance 17 (1): 25. https://doi.org/10.1186/s12968-015-0123-3.

</div>

Asanuma, Toshihiko, Yumi Fukuta, Kasumi Masuda, Ayana Hioki, Mariko Iwasaki, and Satoshi Nakatani. 2012. “Assessment of Myocardial Ischemic Memory Using Speckle Tracking Echocardiography.” JACC: Cardiovascular Imaging 5 (1): 1–11. https://doi.org/10.1016/j.jcmg.2011.09.019.

</div>

Augustine, Daniel, Adam J Lewandowski, Merzaka Lazdam, Aitzaz Rai, Jane Francis, Saul Myerson, Alison Noble, et al. 2013. “Global and Regional Left Ventricular Myocardial Deformation Measures by Magnetic Resonance Feature Tracking in Healthy Volunteers: Comparison with Tagging and Relevance of Gender.” Journal of Cardiovascular Magnetic Resonance 15 (1): 8. https://doi.org/10.1186/1532-429X-15-8.

</div>

Barison, Andrea, Alberto Aimo, Giancarlo Todiere, Chrysanthos Grigoratos, Giovanni Donato Aquaro, and Michele Emdin. 2022. “Cardiovascular Magnetic Resonance for the Diagnosis and Management of Heart Failure with Preserved Ejection Fraction.” Heart Failure Reviews 27 (1): 191–205.

</div>

Barros, Marcio V. L., Ida S. Leren, Thor Edvardsen, Kristina H. Haugaa, Andre A. L. Carmo, Thais A. R. Lage, Maria Carmo P. Nunes, Manoel O. C. Rocha, and Antonio L. P. Ribeiro. 2016. “Mechanical Dispersion Assessed by Strain Echocardiography Is Associated with Malignant Arrhythmias in Chagas Cardiomyopathy.” Journal of the American Society of Echocardiography 29 (4): 368–74.

</div>

Bogunovic, Nikola, Martin Farr, Lukas Pirl, Cornelia Piper, Volker Rudolph, and Fabian Roder. 2022. “Multi-Parametric Speckle Tracking Analyses to Characterize Cardiac Amyloidosis: A Comparative Study of Systolic Left Ventricular Longitudinal Myocardial Mechanics.” Heart and Vessels 37 (9): 1526–40.

</div>

Brainin, Philip, Sofie Reumert Biering-Sørensen, Rasmus Møgelvang, Peter Søgaard, Jan Skov Jensen, and Tor Biering-Sørensen. 2018. “Postsystolic Shortening by Speckle Tracking Echocardiography Is an Independent Predictor of Cardiovascular Events and Mortality in the General Population.” Journal of the American Heart Association 7 (6): e008367. https://doi.org/10.1161/JAHA.117.008367.

</div>

Brainin, Philip, Kristoffer Grundtvig Skaarup, Allan Zeeberg Iversen, Peter Godsk Jørgensen, Elke Platz, Jan Skov Jensen, and Tor Biering-Sørensen. 2019. “Post-Systolic Shortening Predicts Heart Failure Following Acute Coronary Syndrome.” International Journal of Cardiology 276: 191–97.

</div>

Brooks, Jeremy, Christopher M. Kramer, and Michael Salerno. 2013. “Markedly Increased Volume of Distribution of Gadolinium in Cardiac Amyloidosis Demonstrated by T<sub>1</sub> Mapping.” Journal of Magnetic Resonance Imaging 38 (6): 1591–95.

</div>

Bull, Sacha, Steven K White, Stefan K Piechnik, Andrew S Flett, Vanessa M Ferreira, Margaret Loudon, Jane M Francis, et al. 2013. “Human Non-Contrast T1 Values and Correlation with Histology in Diffuse Fibrosis.” Heart 99 (13): 932–37.

</div>

Candan, Ozkan, Cetin Gecmen, Emrah Bayam, Ahmet Guner, Mehmet Celik, and Cem Doğan. 2017. “Mechanical Dispersion and Global Longitudinal Strain by Speckle Tracking Echocardiography: Predictors of Appropriate Implantable Cardioverter Defibrillator Therapy in Hypertrophic Cardiomyopathy.” Echocardiography 34 (6): 835–42.

</div>

Cho, Young Hoon, Joon-Won Kang, Seong Hoon Choi, Dong Hyun Yang, Tran Thi Xuan Anh, Eun-Seok Shin, and Young-Hak Kim. 2019. “Reference Parameters for Left Ventricular Wall Thickness, Thickening, and Motion in Stress Myocardial Perfusion CT: Global and Regional Assessment.” Clinical Imaging 56: 81–87.

</div>

Coiro, Stefano, Olivier Huttin, Erwan Bozec, Christine Selton-Suty, Zohra Lamiral, Erberto Carluccio, Annie Trinh, et al. 2017. “Reproducibility of Echocardiographic Assessment of <span class="nocase">2D-derived</span> Longitudinal Strain Parameters in a Population-Based Study (the STANISLAS Cohort Study).” The International Journal of Cardiovascular Imaging 33 (9): 1361–69. https://doi.org/10.1007/s10554-017-1117-z.

</div>

Corya, B C, S Rasmussen, H Feigenbaum, S B Knoebel, and M J Black. 1977. “Systolic Thickening and Thinning of the Septum and Posterior Wall in Patients with Coronary Artery Disease, Congestive Cardiomyopathy, and Atrial Septal Defect.” Circulation 55 (1): 109–14.

</div>

Dall’Armellina, Erica, Stefan K Piechnik, Vanessa M Ferreira, Quang Le Si, Matthew D Robson, Jane M Francis, Florim Cuculi, et al. 2012. “Cardiovascular Magnetic Resonance by Non Contrast <span class="nocase">T1-mapping</span> Allows Assessment of Severity of Injury in Acute Myocardial Infarction.” Journal of Cardiovascular Magnetic Resonance 14 (1): 15.

</div>

Dawson, Dana K., Alicia M. Maceira, Vimal J. Raj, Catriona Graham, Dudley J. Pennell, and Philip J. Kilner. 2011. “Regional Thicknesses and Thickening of Compacted and Trabeculated Myocardial Layers of the Normal Left Ventricle Studied by Cardiovascular Magnetic Resonance.” Circulation. Cardiovascular Imaging 4 (2): 139–46.

</div>

Erdei, T., J. C. L. Rodrigues, R. Hartley-Davies, A. G. Dastidar, G. V. Szantho, E. C. Hart, A. K. Nightingale, N. E. Manghat, and M. C. K. Hamilton. 2022. “The Effect of Left Ventricular Longitudinal Strain on Left Atrial Function and Ventricular Filling in Hypertension.” Clinical Radiology 77 (5): e379–86.

</div>

Esch, Ben T., and Darren E. R. Warburton. 2009. “Left Ventricular Torsion and Recoil: Implications for Exercise Performance and Cardiovascular Disease.” Journal of Applied Physiology 106 (2): 362–69.

</div>

Farsalinos, Konstantinos, Dimitrios Tsiapras, Stamatis Kyrzopoulos, and Vassilis Voudris. 2013. “Acute and Chronic Effects of Smoking on Myocardial Function in Healthy Heavy Smokers: A Study of <span class="smallcaps">D</span> Oppler Flow, <span class="smallcaps">D</span> Oppler Tissue Velocity, and Two-Dimensional Speckle Tracking Echocardiography.” Echocardiography 30 (3): 285–92. https://doi.org/10.1111/echo.12052.

</div>

Ferreira, Vanessa M., Stefan K. Piechnik, Erica Dall’Armellina, Theodoros D. Karamitsos, Jane M. Francis, Ntobeko Ntusi, Cameron Holloway, et al. 2013. “T1 Mapping for the Diagnosis of Acute Myocarditis Using CMR.” JACC: Cardiovascular Imaging 6 (10): 1048–58.

</div>

Ferreira, Vanessa M, Stefan K Piechnik, Erica Dall’Armellina, Theodoros D Karamitsos, Jane M Francis, Robin P Choudhury, Matthias G Friedrich, Matthew D Robson, and Stefan Neubauer. 2012. “Non-Contrast <span class="nocase">T1-mapping</span> Detects Acute Myocardial Edema with High Diagnostic Accuracy: A Comparison to <span class="nocase">T2-weighted</span> Cardiovascular Magnetic Resonance.” Journal of Cardiovascular Magnetic Resonance 14 (1): 53.

</div>

Ferreira, Vanessa M, Stefan K Piechnik, Erica Dall’Armellina, Theodoros D Karamitsos, Jane M Francis, Ntobeko Ntusi, Cameron Holloway, et al. 2014. “Native <span class="nocase">T1-mapping</span> Detects the Location, Extent and Patterns of Acute Myocarditis Without the Need for Gadolinium Contrast Agents.” Journal of Cardiovascular Magnetic Resonance 16 (1): 36.

</div>

Fine, Nowell M., Aijaz A. Shah, Il-Yong Han, Yang Yu, Ju-feng Hsiao, Yuki Koshino, Hayder K. Saleh, et al. 2013. “Left and Right Ventricular Strain and Strain Rate Measurement in Normal Adults Using Velocity Vector Imaging: An Assessment of Reference Values and Intersystem Agreement.” The International Journal of Cardiovascular Imaging 29 (3): 571–80. https://doi.org/10.1007/s10554-012-0120-7.

</div>

Goebel, Björn, Kristina H. Haugaa, Kathleen Meyer, Sylvia Otto, Christian Jung, Alexander Lauten, Hans R. Figulla, Thor Edvardsen, and Tudor C. Poerner. 2014. “Early Diastolic Strain Rate Predicts Response to Heart Failure Therapy in Patients with Dilated Cardiomyopathy.” The International Journal of Cardiovascular Imaging 30 (3): 505–13.

</div>

Götte, Marco J. W., Tjeerd Germans, Iris K. Rüssel, Jaco J. M. Zwanenburg, J. Tim Marcus, Albert C. Van Rossum, and Dirk J. Van Veldhuisen. 2006. “Myocardial Strain and Torsion Quantified by Cardiovascular Magnetic Resonance Tissue Tagging.” Journal of the American College of Cardiology 48 (10): 2002–11.

</div>

Haugaa, Kristina H., Marit Kristine Smedsrud, Torkel Steen, Erik Kongsgaard, Jan Pål Loennechen, Terje Skjaerpe, Jens-Uwe Voigt, et al. 2010. “Mechanical Dispersion Assessed by Myocardial Strain in Patients After Myocardial Infarction for Risk Prediction of Ventricular Arrhythmia.” JACC: Cardiovascular Imaging 3 (3): 247–56.

</div>

Heiberg, J., S. Ringgaard, M. R. Schmidt, A. Redington, and V. E. Hjortdal. 2015. “Structural and Functional Alterations of the Right Ventricle Are Common in Adults Operated for Ventricular Septal Defect as Toddlers.” European Heart Journal - Cardiovascular Imaging 16 (5): 483–89. https://doi.org/10.1093/ehjci/jeu292.

</div>

Hoit, Brian D. 2011. “Strain and Strain Rate Echocardiography and Coronary Artery Disease.” Circulation: Cardiovascular Imaging 4 (2): 179–90.

</div>

Huttin, Olivier, Pierre-Yves Marie, Maxime Benichou, Erwan Bozec, Simon Lemoine, Damien Mandry, Yves Juillière, et al. 2016. “Temporal Deformation Pattern in Acute and Late Phases of <span class="nocase">ST-elevation</span> Myocardial Infarction: Incremental Value of Longitudinal Post-Systolic Strain to Assess Myocardial Viability.” Clinical Research in Cardiology 105 (10): 815–26.

</div>

Ishii, Katsuhisa, Makoto Imai, Tamaki Suyama, Motoyoshi Maenaka, Takahiro Nagai, Masaki Kawanami, and Yutaka Seino. 2009. “Exercise-Induced Post-Ischemic Left Ventricular Delayed Relaxation or Diastolic Stunning.” Journal of the American College of Cardiology 53 (8): 698–705.

</div>

Jeung, Mi-Young, Philippe Germain, Pierre Croisille, Soraya El Ghannudi, Catherine Roy, and Afshin Gangi. 2012. “Myocardial Tagging with MR Imaging: Overview of Normal and Pathologic Findings.” RadioGraphics 32 (5): 1381–98.

</div>

Kali, Avinash, Ivan Cokic, Richard L Q Tang, Hsin-Jung Yang, Behzad Sharif, Eduardo Marbán, Debiao Li, Daniel Berman, and Rohan Dharmakumar. 2014. “Determination of Location, Size and Transmurality of Chronic Myocardial Infarction Without Exogenous Contrast Media Using Cardiac Magnetic Resonance Imaging at 3t.” Circulation. Cardiovascular Imaging 7 (3): 471–81.

</div>

Karamitsos, Theodoros D., Stefan K. Piechnik, Sanjay M. Banypersad, Marianna Fontana, Ntobeko B. Ntusi, Vanessa M. Ferreira, Carol J. Whelan, et al. 2013. “Noncontrast T1 Mapping for the Diagnosis of Cardiac Amyloidosis.” JACC: Cardiovascular Imaging 6 (4): 488–97.

</div>

Kawel-Boehm, Nadine, Scott J. Hetzel, Bharath Ambale-Venkatesh, Gabriella Captur, Christopher J. Francois, Michael Jerosch-Herold, Michael Salerno, et al. 2020. “Reference Ranges (‘Normal Values’) for Cardiovascular Magnetic Resonance (CMR) in Adults and Children: 2020 Update.” Journal of Cardiovascular Magnetic Resonance 22 (1): 87.

</div>

Kimura, Koichi, Katsu Takenaka, XiaoFang Pan, Aya Ebihara, Kansei Uno, Nobuaki Fukuda, Takahide Kohro, Hiroyuki Morita, Yutaka Yatomi, and Ryozo Nagai. 2011. “Prediction of Coronary Artery Stenosis Using Strain Imaging Diastolic Index at Rest in Patients with Preserved Ejection Fraction.” Journal of Cardiology 57 (3): 311–15.

</div>

Kowallick, Johannes T., Pablo Lamata, Shazia T. Hussain, Shelby Kutty, Michael Steinmetz, Jan M. Sohns, Martin Fasshauer, et al. 2014. “Quantification of Left Ventricular Torsion and Diastolic Recoil Using Cardiovascular Magnetic Resonance Myocardial Feature Tracking.” Edited by Zhuoli Zhang. PLoS ONE 9 (10): e109164.

</div>

Kowallick, Johannes T., Geraint Morton, Pablo Lamata, Roy Jogiya, Shelby Kutty, Joachim Lotz, Gerd Hasenfuß, Eike Nagel, Amedeo Chiribiri, and Andreas Schuster. 2016. “Inter-Study Reproducibility of Left Ventricular Torsion and Torsion Rate Quantification Using MR Myocardial Feature Tracking: Reproducibility of LV Torsion.” Journal of Magnetic Resonance Imaging 43 (1): 128–37.

</div>

Le Ven, Florent, Karine Bibeau, Élianne De Larochellière, Helena Tizón-Marcos, Stéphanie Deneault-Bissonnette, Philippe Pibarot, Christian F. Deschepper, and Éric Larose. 2016. “Cardiac Morphology and Function Reference Values Derived from a Large Subset of Healthy Young Caucasian Adults by Magnetic Resonance Imaging.” European Heart Journal Cardiovascular Imaging 17 (9): 981–90.

</div>

Lee, Seung-Pyo, Whal Lee, Joo Myung Lee, Eun-Ah Park, Hyung-Kwan Kim, Yong-Jin Kim, and Dae-Won Sohn. 2015. “Assessment of Diffuse Myocardial Fibrosis by Using MR Imaging in Asymptomatic Patients with Aortic Stenosis.” Radiology 274 (2): 359–69.

</div>

Liu, Boyang, Ahmed M. Dardeer, William E. Moody, Manvir K. Hayer, Shanat Baig, Anna M. Price, Francisco Leyva, Nicola C. Edwards, and Richard P. Steeds. 2017. “Reference Ranges for Three-Dimensional Feature Tracking Cardiac Magnetic Resonance: Comparison with Two-Dimensional Methodology and Relevance of Age and Gender.” The International Journal of Cardiovascular Imaging, November. https://doi.org/10.1007/s10554-017-1277-x.

</div>

Mahmod, Masliza, Stefan K Piechnik, Eylem Levelt, Vanessa M Ferreira, Jane M Francis, Andrew Lewis, Nikhil Pal, et al. 2014. “Adenosine Stress Native T1 Mapping in Severe Aortic Stenosis: Evidence for a Role of the Intravascular Compartment on Myocardial T1 Values.” Journal of Cardiovascular Magnetic Resonance 16 (1): 92.

</div>

Méndez, Cristina, Rafaela Soler, Esther Rodríguez, Roberto Barriales, Juan Pablo Ochoa, and Lorenzo Monserrat. 2018. “Differential Diagnosis of Thickened Myocardium: An Illustrative MRI Review.” Insights into Imaging 9 (5): 695–707.

</div>

Moody, William E., Robin J. Taylor, Nicola C. Edwards, Colin D. Chue, Fraz Umar, Tiffany J. Taylor, Charles J. Ferro, et al. 2015. “Comparison of Magnetic Resonance Feature Tracking for Systolic and Diastolic Strain and Strain Rate Calculation with Spatial Modulation of Magnetization Imaging Analysis.” Journal of Magnetic Resonance Imaging 41 (4): 1000–1012.

</div>

Mora, Vicente, Ildefonso Roldán, Elena Romero, Diana Romero, Javier Bertolín, Natalia Ugalde, Carmen Pérez-Olivares, Melisa Rodriguez-Israel, Jana Pérez-Gozalbo, and Jorge A. Lowenstein. 2018. “Comprehensive Assessment of Left Ventricular Myocardial Function by Two-Dimensional Speckle-Tracking Echocardiography.” Cardiovascular Ultrasound 16 (1): 16. https://doi.org/10.1186/s12947-018-0135-x.

</div>

Moreira, Henrique T., Chike C. Nwabuo, Anderson C. Armstrong, Satoru Kishi, Ola Gjesdal, Jared P. Reis, Pamela J. Schreiner, et al. 2017. “Reference Ranges and Regional Patterns of Left Ventricular Strain and Strain Rate Using Two-Dimensional Speckle-Tracking Echocardiography in a Healthy Middle-Aged Black and White Population: The CARDIA Study.” Journal of the American Society of Echocardiography 30 (7): 647–658.e2. https://doi.org/10.1016/j.echo.2017.03.010.

</div>

Nickander, Jannike, Magnus Lundin, Goran Abdula, Peder Sörensson, Stefania Rosmini, James C. Moon, Peter Kellman, Andreas Sigfridsson, and Martin Ugander. 2016. “Blood Correction Reduces Variability and Gender Differences in Native Myocardial T1 Values at 1.5 T Cardiovascular Magnetic Resonance – a Derivation/Validation Approach.” Journal of Cardiovascular Magnetic Resonance 19 (1): 41.

</div>

Nucifora, Gaetano, Daniele Muser, Pasquale Gianfagna, Giorgio Morocutti, and Alessandro Proclemer. 2015. “Systolic and Diastolic Myocardial Mechanics in Hypertrophic Cardiomyopathy and Their Link to the Extent of Hypertrophy, Replacement Fibrosis and Interstitial Fibrosis.” The International Journal of Cardiovascular Imaging 31 (8): 1603–10. https://doi.org/10.1007/s10554-015-0720-0.

</div>

Oxborough, David, Keith George, and Karen M. Birch. 2012. “Intraobserver Reliability of Two-Dimensional Ultrasound Derived Strain Imaging in the Assessment of the Left Ventricle, Right Ventricle, and Left Atrium of Healthy Human Hearts.” Echocardiography 29 (7): 793–802. https://doi.org/10.1111/j.1540-8175.2012.01698.x.

</div>

Park, Jae-Hyeong. 2019. “Two-Dimensional Echocardiographic Assessment of Myocardial Strain: Important Echocardiographic Parameter Readily Useful in Clinical Field.” Korean Circulation Journal 49 (10): 908–31.

</div>

Park, Jae-Hyeong, Ju-Hee Lee, Sang Yeub Lee, Jin-Oh Choi, Mi-Seung Shin, Mi-Jeong Kim, Hae Ok Jung, et al. 2016. “Normal 2-Dimensional Strain Values of the Left Ventricle: A Substudy of the Normal Echocardiographic Measurements in Korean Population Study.” Journal of Cardiovascular Ultrasound 24 (4): 285. https://doi.org/10.4250/jcu.2016.24.4.285.

</div>

Piechnik, Stefan K, Vanessa M Ferreira, Erica Dall’Armellina, Lowri E Cochlin, Andreas Greiser, Stefan Neubauer, and Matthew D Robson. 2010. “Shortened Modified Look-Locker Inversion Recovery (ShMOLLI) for Clinical Myocardial <span class="nocase">T1-mapping</span> at 1.5 and 3 T Within a 9 Heartbeat Breathhold.” Journal of Cardiovascular Magnetic Resonance 12 (1): 69.

</div>

Piechnik, Stefan K, Vanessa M Ferreira, Adam J Lewandowski, Ntobeko Ab Ntusi, Rajarshi Banerjee, Cameron Holloway, Mark Bm Hofman, et al. 2013. “Normal Variation of Magnetic Resonance T1 Relaxation Times in the Human Population at 1.5 T Using ShMOLLI.” Journal of Cardiovascular Magnetic Resonance 15 (1): 13.

</div>

Puyol-Antón, Esther, Bram Ruijsink, Christian F. Baumgartner, Pier-Giorgio Masci, Matthew Sinclair, Ender Konukoglu, Reza Razavi, and Andrew P. King. 2020. “Automated Quantification of Myocardial Tissue Characteristics from Native T1 Mapping Using Neural Networks with Uncertainty-Based Quality-Control.” Journal of Cardiovascular Magnetic Resonance 22 (1): 60.

</div>

Ruijsink, Bram, Esther Puyol-Antón, Ilkay Oksuz, Matthew Sinclair, Wenjia Bai, Julia A. Schnabel, Reza Razavi, and Andrew P. King. 2020. “Fully Automated, Quality-Controlled Cardiac Analysis From CMR: Validation and Large-Scale Application to Characterize Cardiac Function.” JACC: Cardiovascular Imaging 13 (3): 684–95.

</div>

Sado, Daniel M., Steven K. White, Stefan K. Piechnik, Sanjay M. Banypersad, Thomas Treibel, Gabriella Captur, Marianna Fontana, et al. 2013. “Identification and Assessment of Anderson-Fabry Disease by Cardiovascular Magnetic Resonance Noncontrast Myocardial T1 Mapping.” Circulation: Cardiovascular Imaging 6 (3): 392–98.

</div>

Saijo, Yoshihito, Tom Kai Ming Wang, Nicholas Chan, Brett W. Sperry, Dermot Phelan, Milind Y. Desai, Brian Griffin, Richard A. Grimm, and Zoran B. Popović. 2022. “Post-Systolic Shortening Index by Echocardiography Evaluation of Dyssynchrony in the Non-Dilated and Hypertrophied Left Ventricle.” Edited by Vincenzo Lionetti. PLOS ONE 17 (8): e0273419.

</div>

Scatteia, A., A. Baritussio, and C. Bucciarelli-Ducci. 2017. “Strain Imaging Using Cardiac Magnetic Resonance.” Heart Failure Reviews 22 (4): 465–76.

</div>

Schnell, Frédéric, David Matelot, Magalie Daudin, Gaelle Kervio, Philippe Mabo, François Carré, and Erwan Donal. 2017. “Mechanical Dispersion by Strain Echocardiography: A Novel Tool to Diagnose Hypertrophic Cardiomyopathy in Athletes.” Journal of the American Society of Echocardiography 30 (3): 251–61.

</div>

Schuster, A., V.-C. Stahnke, C. Unterberg-Buchwald, J. T. Kowallick, P. Lamata, M. Steinmetz, S. Kutty, et al. 2015. “Cardiovascular Magnetic Resonance Feature-Tracking Assessment of Myocardial Mechanics: Intervendor Agreement and Considerations Regarding Reproducibility.” Clinical Radiology 70 (9): 989–98. https://doi.org/10.1016/j.crad.2015.05.006.

</div>

Sharifov, Oleg, Thomas S. Denney, Andrew A. Girard, Himanshu Gupta, and Steven G. Lloyd. 2023. “Coronary Artery Disease Is Associated With Impaired Atrial Function Regardless of Left Ventricular Filling Pressure.” International Journal of Cardiology 387: 131102.

</div>

Smiseth, Otto A., Oliver Rider, Marta Cvijic, Ladislav Valkovič, Espen W. Remme, and Jens-Uwe Voigt. 2024. “Myocardial Strain Imaging.” JACC: Cardiovascular Imaging, S1936878X24003012.

</div>

Steinmetz, Michael, Simon Usenbenz, Johannes Tammo Kowallick, Olga Hösch, Wieland Staab, Torben Lange, Shelby Kutty, et al. 2016. “Left Ventricular Synchrony, Torsion, and Recoil Mechanics in Ebstein’s Anomaly: Insights from Cardiovascular Magnetic Resonance.” Journal of Cardiovascular Magnetic Resonance 19 (1): 101. https://doi.org/10.1186/s12968-017-0414-y.

</div>

Sugimoto, Tadafumi, Raluca Dulgheru, Anne Bernard, Federica Ilardi, Laura Contu, Karima Addetia, Luis Caballero, et al. 2017. “Echocardiographic Reference Ranges for Normal Left Ventricular 2d Strain: Results from the EACVI NORRE Study.” European Heart Journal - Cardiovascular Imaging 18 (8): 833–40. https://doi.org/10.1093/ehjci/jex140.

</div>

Takigiku, Kiyohiro, Masaaki Takeuchi, Chisato Izumi, Satoshi Yuda, Konomi Sakata, Nobuyuki Ohte, Kazuaki Tanabe, and Satoshi Nakatani. 2012. “Normal Range of Left Ventricular 2-Dimensional Strain: – Japanese Ultrasound Speckle Tracking of the Left Ventricle (JUSTICE) Study –.” Circulation Journal 76 (11): 2623–32. https://doi.org/10.1253/circj.CJ-12-0264.

</div>

Tang, Xuepei, Sisi Yu, Yaohan Yu, Haibo Ren, Shuhao Li, Li Zhou, Zhen Yang, Hailong Wu, Wei Zhou, and Lianggeng Gong. 2017. “Left Ventricular Myocardial Strain in Ventricular Arrhythmia Without Structural Heart Disease Using Cardiac Magnetic Resonance.” American Journal of Translational Research 9 (6): 3006–16.

</div>

Taylor, Andrew J., Michael Salerno, Rohan Dharmakumar, and Michael Jerosch-Herold. 2016. “T1 Mapping.” JACC: Cardiovascular Imaging 9 (1): 67–81.

</div>

Ubachs, Joey, Einar Heiberg, Katarina Steding, and Håkan Arheden. 2009. “Normal Values for Wall Thickening by Magnetic Resonance Imaging.” Journal of Cardiovascular Magnetic Resonance 11 (1): P61.

</div>

Voigt, Jens-Uwe, Gianni Pedrizzetti, Peter Lysyansky, Tom H. Marwick, Hélène Houle, Rolf Baumann, Stefano Pedri, et al. 2015. “Definitions for a Common Standard for 2d Speckle Tracking Echocardiography: Consensus Document of the EACVI/ASE/Industry Task Force to Standardize Deformation Imaging.” Journal of the American Society of Echocardiography 28 (2): 183–93.

</div>

Voigt, J.-U., and F. A. Flachskampf. 2004. “Strain and Strain Rate.” Clinical Research in Cardiology 93 (4): 249–58.

</div>

Wu, LiNa, Tjeerd Germans, Ahmet Güçlü, Martijn W Heymans, Cornelis P Allaart, and Albert C Van Rossum. 2014. “Feature Tracking Compared with Tissue Tagging Measurements of Segmental Strain by Cardiovascular Magnetic Resonance.” Journal of Cardiovascular Magnetic Resonance 16 (1): 10. https://doi.org/10.1186/1532-429X-16-10.

</div>

Yingchoncharoen, Teerapat, Shikhar Agarwal, Zoran B. Popović, and Thomas H. Marwick. 2013. “Normal Ranges of Left Ventricular Strain: A Meta-Analysis.” Journal of the American Society of Echocardiography 26 (2): 185–91. https://doi.org/10.1016/j.echo.2012.10.008.

</div>

Yoneyama, Kihei, Ola Gjesdal, Eui-Young Choi, Colin O. Wu, W. Gregory Hundley, Antoinette S. Gomes, Chia-Ying Liu, Robyn L. McClelland, David A. Bluemke, and Joao A. C. Lima. 2012. “Age, Sex, and Hypertension-Related Remodeling Influences Left Ventricular Torsion Assessed by Tagged Cardiac Magnetic Resonance in Asymptomatic Individuals: The Multi-Ethnic Study of Atherosclerosis.” Circulation 126 (21): 2481–90.

</div>

Young, Alistair A, and Brett R Cowan. 2012. “Evaluation of Left Ventricular Torsion by Cardiovascular Magnetic Resonance.” Journal of Cardiovascular Magnetic Resonance 14 (1): 56.

</div>

Zhang, Zhen, Gengxiao Li, Yiyuan Gao, Shanshan Zhou, Jianan Xie, Shurong Liu, Zhiwei Zhao, et al. 2024. “Healthy Adult Left and Right Ventricular Torsion and Torsion Rates With <span class="smallcaps">MR</span> -Feature Tracking.” Journal of Magnetic Resonance Imaging 60 (4): 1591–601. https://doi.org/10.1002/jmri.29201.

</div>

Zhao, Xiaoying, Li Zhang, Lujing Wang, Wanqiu Zhang, Yujiao Song, Xinxiang Zhao, and Yanli Li. 2025. “Magnetic Resonance Imaging Quantification of Left Ventricular Mechanical Dispersion and Scar Heterogeneity Optimize Risk Stratification After Myocardial Infarction.” BMC Cardiovascular Disorders 25 (1): 2.

</div>