The Olympic rings of Duchenne muscular dystrophy – cardiac computed tomography wins gold

Krommydas, Argyris; Rajani, Ronak; Hart, Nicholas; Kapetanakis, Stamatis

doi:10.1016/j.repc.2015.12.010

Cited by 1 publication

(1 citation statement)

References 4 publications

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“…It is for these reasons that CMR has become the gold standard for evaluating CM in the DMD patient population for its ability to provide superior diagnostic capability ( 15 – 19 ). Additional cardiac imaging modalities such as magnetic resonance spectroscopy, single-photon emission computed tomography, and x-ray computed tomography can be used for assessment, but are not commonly used in assessment of CM in DMD ( 20 – 23 ). In the DMD patient population, heart function abnormalities such as changes in left ventricular ejection fraction (LVEF) are often masked in the early stages of CM as these patients may be wheelchair-bound and incapable of robust physical activity from skeletal muscle degradation.…”

Section: Introductionmentioning

confidence: 99%

Myocardial strain imaging in Duchenne muscular dystrophy

Earl

Soslow

Markham

et al. 2022

Front. Cardiovasc. Med.

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Cardiomyopathy (CM) is the leading cause of death for individuals with Duchenne muscular dystrophy (DMD). While DMD CM progresses rapidly and fatally for some in teenage years, others can live relatively symptom-free into their thirties or forties. Because CM progression is variable, there is a critical need for biomarkers to detect early onset and rapid progression. Despite recent advances in imaging and analysis, there are still no reliable methods to detect the onset or progression rate of DMD CM. Cardiac strain imaging is a promising technique that has proven valuable in DMD CM assessment, though much more work has been done in adult CM patients. In this review, we address the role of strain imaging in DMD, the mechanical and functional parameters used for clinical assessment, and discuss the gaps where emerging imaging techniques could help better characterize CM progression in DMD. Prominent among these emerging techniques are strain assessment from 3D imaging and development of deep learning algorithms for automated strain assessment. Improved techniques in tracking the progression of CM may help to bridge a crucial gap in optimizing clinical treatment for this devastating disease and pave the way for future research and innovation through the definition of robust imaging biomarkers and clinical trial endpoints.

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