Texture analysis of cardiac cine magnetic resonance imaging to detect nonviable segments in patients with chronic myocardial infarction

Larroza, Andrés; López‐Lereu, María P.; Monmeneu, José V.; Gavara, José; Chorro, Francisco J.; Bodı́, Vicente; Moratal, David

doi:10.1002/mp.12783

Cited by 63 publications

(32 citation statements)

References 38 publications

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“…The feasibility of T 1r cardiac MRI (35,36) for nonenhanced detection of chronic MI has also been shown but the contrast-to-noise ratio between healthy tissue and MI is low and further sequence developments are required. More recently, texture analysis has been investigated for detecting subacute and chronic MI from nonenhanced cardiac cine; sensitivity of 86% and specificity of 82% was obtained by Bessler et al (37) and overall AUC of 0.85 was achieved by Larroza et al (38).…”

Section: Discussionmentioning

confidence: 99%

Deep Learning for Diagnosis of Chronic Myocardial Infarction on Nonenhanced Cardiac Cine MRI

Zhang¹,

Yang²,

Gao³

et al. 2019

Radiology

157

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iagnosis of chronic myocardial infarction (MI) is an important clinical task because the management of and treatment planning for patients is different for chronic MI versus acute MI (1,2). The extent of chronic MI, including location, size, and transmurality, provides rich information for patient diagnosis, prognosis, and therapy planning (3). Therefore, accurate delineation and comprehensive evaluation of chronic MI is of great clinical interest. Late gadolinium enhancement (LGE) MRI has been established as the ground truth reference technique for chronic MI evaluation (4-6). However, including LGE MRI in the MRI examination extends the scanning duration and there are also growing concerns about its safety (7-9). While LGE MRI is contraindicated in patients with severe renal impairment, a recent study has also shown that gadolinium might deposit into the skin, dentate nucleus, and globus pallidus of patients with normal renal function (10). A reliable technique to detect and delineate MI without the need for gadolinium-based contrast agent would therefore be highly desirable. T1 and T2 mapping techniques (11) are non-contrast material-enhanced approaches that show longer T1 and T2 relaxation times in acute MI compared with normal myocardium. In comparison, while T1 relaxation time is

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Section: Discussionmentioning

confidence: 99%

Deep Learning for Diagnosis of Chronic Myocardial Infarction on Nonenhanced Cardiac Cine MRI

Zhang¹,

Yang²,

Gao³

et al. 2019

Radiology

157

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“…As LV becomes globally thinner, MI scar tissue expands and causes extra workload in the LV, hypertrophy, and expansion of the healthy myocytes to maintain CO [12,27]. Recently, texture analysis applied on MRI cine images was demonstrated to differentiate nonviable and viable MI areas and remote areas of the myocardium [28]. Texture analysis finds the patterns and relationships among pixels from heterogeneities within the imaging target [28].…”

Section: Functional Cmrimentioning

confidence: 99%

“…Recently, texture analysis applied on MRI cine images was demonstrated to differentiate nonviable and viable MI areas and remote areas of the myocardium [28]. Texture analysis finds the patterns and relationships among pixels from heterogeneities within the imaging target [28].…”

Section: Functional Cmrimentioning

confidence: 99%

“…T 2 relaxation determines edema via increased T 2 relaxation time resulting from an increased amount of interstitial free water [28]. Therefore, T 2 relaxation time is suitable for the determination of the area at risk in acute MI [6,28,40]. However, T 2 suffers from a poor contrast-to-noise ratio compared to T 1 and longitudinal rotating frame relaxation time (T 1ρ ) [51].…”

Section: Conventional Relaxation Timesmentioning

confidence: 99%

“…However, T 2 suffers from a poor contrast-to-noise ratio compared to T 1 and longitudinal rotating frame relaxation time (T 1ρ ) [51]. T 2 * relaxation time can be chosen when myocardial iron content is used to detect myocardial hemorrhage since T 2 * relaxation time is more sensitive for magnetic susceptibilities than T 2 due to the accumulated iron content (Table 1) [15,28,40,52].…”

Section: Conventional Relaxation Timesmentioning

confidence: 99%

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Molecular Imaging to Monitor Left Ventricular Remodeling in Heart Failure

Ylä-Herttuala

Saraste

Knuuti

et al. 2019

Curr Cardiovasc Imaging Rep

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Purpose of Review Cardiovascular diseases are the leading cause of deaths worldwide. Many complex cellular and molecular pathways lead to myocardial remodeling after ischemic insults. Anatomy, function, and viability of the myocardium can be assessed by modern medical imaging techniques by both visualizing and quantifying damages. Novel imaging techniques aim for a precise and accurate visualization of the myocardium and for the detection of alternations at the molecular level. Recent Findings Magnetic resonance imaging assesses anatomy, function, and tissue characterization of the myocardium noninvasively with high spatial resolution, sensitivity, and specificity. Using hyperpolarized magnetic resonance imaging, molecular and metabolic conditions can be assessed non-invasively. Single photon-emission tomography and positron-emission tomography are the most sensitive techniques to detect biological processes in the myocardium. Cardiac perfusion, metabolism, and viability are the most common clinical targets. In addition, molecular-targeted imaging of biological processes involved in heart failure, such as myocardial innervation, inflammation, and extracellular matrix remodeling, is feasible. Summary Novel imaging techniques can provide a precise and accurate visualization of the myocardium and for the detection of alternations at molecular level.

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Radiomics: Technical Background

Baeßler

2022

Artificial Intelligence in Cardiothoracic Imaging

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Texture analysis of cardiac cine magnetic resonance imaging to detect nonviable segments in patients with chronic myocardial infarction

Abstract: Nonviable segments can be detected on cine MRI using texture analysis and this may be used as hypothesis for future research aiming to detect the infarcted myocardium by means of a gadolinium-free approach.

Cited by 63 publications

References 38 publications

Deep Learning for Diagnosis of Chronic Myocardial Infarction on Nonenhanced Cardiac Cine MRI

Deep Learning for Diagnosis of Chronic Myocardial Infarction on Nonenhanced Cardiac Cine MRI

Molecular Imaging to Monitor Left Ventricular Remodeling in Heart Failure

Radiomics: Technical Background

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