Present and Future Innovations in AI and Cardiac MRI

Morales, Manuel A.; Manning, Warren J.; Nezafat, Reza

doi:10.1148/radiol.231269

Cited by 13 publications

(5 citation statements)

References 95 publications

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“…In addition, during a single examination, these cardiovascular features can be combined with assessments of other bodily tissues to provide a comprehensive assessment of disease processes that affect multiple organ systems such as heart failure. [1][2][3][4][5] The 50-year development of CMR evolved over 3 stages: (1) the origins of clinical use of magnetic resonance in imaging, (2) the development of CMR-specific methods and techniques, and (3) the integration of CMR into cardiovascular care pathways. The first stage originated with Bloch and Purcell's description of nuclear magnetic resonance or nuclear magnetic resonance in 1946 (Figure ).…”

Section: Centennial Collectionmentioning

confidence: 99%

“…For example in 2024, physicians in community and academic centers can distinguish ischemic from nonischemic cardiomyopathies, identify myocarditis, distinguish myocardial infarction from stress-associated cardiomyopathy, and locate regional myocardial fibrosis that promotes ventricular arrhythmia. [1][2][3][4][5] Pivotal in the development and implementation of this technology are the inception and guidance provided by the Society for Cardiovascular Magnetic Resonance. 3 Conceived and originated in 1988, the society today uniquely combines physicists, cardiovascular medicine and surgery specialists, and radiologists representing preclinical and clinical science from North and South America, Europe, Africa, Asia, and Australia.…”

Section: Centennial Collectionmentioning

confidence: 99%

“…5 These will likely include (1) algorithms that facilitate rapid image acquisition and reconstruction that substantially reduce image acquisition times, (2) advanced computations pertaining to textures of cardiac features (a field called radiomics) that facilitate diagnoses beyond what may be identified from human interpretation of the images, and (3) deep learning solutions that combine a variety of quantitative measures obtain during CMR with information acquired through other testing modalities and the electronic health record to forecast cardiovascular risk. 5 In summary, with the inception of nuclear magnetic resonance in 1946, emergence of cardiovascular applications in the 1970 to 1980s, and guidance of the field by the Society for Cardiovascular Magnetic Resonance, CMR has rapidly advanced our ability to research, diagnose, and treat cardiovascular disease noninvasively without exposure to ionizing radiation. [1][2][3][4][5] This capability provides an invaluable tool to researchers trying to understand the mechanisms of cardiovascular disease and clinicians diagnosing and treating these diseases.…”

Section: Centennial Collectionmentioning

confidence: 99%

“…4 In the decades to come, it is anticipated the CMR methods will continue to evolve, guided by the Society for Cardiovascular Magnetic Resonance and other societies implementing heart and other organ system evaluations without the need for gadolinium contrast, all the while leveraging the evolving field of artificial intelligence. 3,5…”

Section: Centennial Collectionmentioning

confidence: 99%

“…5 In summary, with the inception of nuclear magnetic resonance in 1946, emergence of cardiovascular applications in the 1970 to 1980s, and guidance of the field by the Society for Cardiovascular Magnetic Resonance, CMR has rapidly advanced our ability to research, diagnose, and treat cardiovascular disease noninvasively without exposure to ionizing radiation. [1][2][3][4][5] This capability provides an invaluable tool to researchers trying to understand the mechanisms of cardiovascular disease and clinicians diagnosing and treating these diseases. 4 In the decades to come, it is anticipated the CMR methods will continue to evolve, guided by the Society for Cardiovascular Magnetic Resonance and other societies implementing heart and other organ system evaluations without the need for gadolinium contrast, all the while leveraging the evolving field of artificial intelligence.…”

Section: Centennial Collectionmentioning

confidence: 99%

See 4 more Smart Citations

Fifty Years of Cardiovascular Magnetic Resonance: Continuing Evolution Toward the “One-Stop Shop” for Cardiovascular Diagnosis

Hundley

2024

Circulation

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Section: Centennial Collectionmentioning

confidence: 99%

Section: Centennial Collectionmentioning

confidence: 99%

Section: Centennial Collectionmentioning

confidence: 99%

Section: Centennial Collectionmentioning

confidence: 99%

Section: Centennial Collectionmentioning

confidence: 99%

See 3 more Smart Citations

Fifty Years of Cardiovascular Magnetic Resonance: Continuing Evolution Toward the “One-Stop Shop” for Cardiovascular Diagnosis

Hundley

2024

Circulation

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Exercise MR of Skeletal Muscles, the Heart, and the Brain

Hooijmans,

Jeneson,

Jørstad

et al. 2024

Magnetic Resonance Imaging

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Magnetic resonance (MR) imaging (MRI) is routinely used to evaluate organ morphology and pathology in the human body at rest or in combination with pharmacological stress as an exercise surrogate. With MR during actual physical exercise, we can assess functional characteristics of tissues and organs under real‐life stress conditions. This is particularly relevant in patients with limited exercise capacity or exercise intolerance, and where complaints typically present only during physical activity, such as in neuromuscular disorders, inherited metabolic diseases, and heart failure. This review describes practical and physiological aspects of exercise MR of skeletal muscles, the heart, and the brain. The acute effects of physical exercise on these organs are addressed in the light of various dynamic quantitative MR readouts, including phosphorus‐31 MR spectroscopy (31P‐MRS) of tissue energy metabolism, phase‐contrast MRI of blood flow and muscle contraction, real‐time cine MRI of cardiac performance, and arterial spin labeling MRI of muscle and brain perfusion. Exercise MR will help advancing our understanding of underlying mechanisms that contribute to exercise intolerance, which often proceed structural and anatomical changes in disease. Its potential to detect disease‐driven alterations in organ function, perfusion, and metabolism under physiological stress renders exercise MR stress testing a powerful noninvasive imaging modality to aid in disease diagnosis and risk stratification. Although not yet integrated in most clinical workflows, and while some applications still require thorough validation, exercise MR has established itself as a comprehensive and versatile modality for characterizing physiology in health and disease in a noninvasive and quantitative way.Evidence Level5Technical EfficacyStage 1

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Accelerated cardiac cine with spatio‐coil regularized deep learning reconstruction

Demirel,

Ghanbari,

Morales

et al. 2024

Magnetic Resonance in Med

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PurposeTo develop an iterative deep learning (DL) reconstruction with spatio‐coil regularization and multichannel k‐space data consistency for accelerated cine imaging.MethodsThis study proposes a Spatio‐Coil Regularized DL (SCR‐DL) approach for iterative deep learning reconstruction incorporating multicoil information in data consistency and regularizer. SCR‐DL uses shift‐invariant convolutional kernels to interpolate missing k‐space lines and reconstruct individual coil images, followed by a regularizer that operates simultaneously across spatial and coil dimensions using learned image priors. At 8‐fold acceleration, SCR‐DL was compared with Generalized Autocalibrating Partially Parallel Acquisition (GRAPPA), sensitivity encoding (SENSE)‐based DL and spatio‐temporal regularized (STR)–DL reconstruction. In the retrospective undersampled cine, images were quantitatively evaluated using normalized mean square error (NMSE) and structural similarity index measure (SSIM). Additionally, agreement for left‐ventricular ejection fraction and left‐ventricular mass were assessed using prospectively accelerated cine images at 2‐fold and 8‐fold accelerations.ResultsThe SCR‐DL algorithm successfully reconstructed highly accelerated cine images. SCR‐DL had significant improvements in NMSE (0.03 ± 0.02) and SSIM (91.4% ± 2.7%) compared with GRAPPA (NMSE: 0.09 ± 0.04, SSIM: 69.9% ± 11.1%; p < 0.001), SENSE‐DL (NMSE: 0.07 ± 0.04, SSIM: 86.9% ± 3.2%; p < 0.001), and STR‐DL (NMSE: 0.04 ± 0.03, SSIM: 90.0% ± 2.5%; p < 0.001) with retrospective undersampled cine. Despite the 3‐fold reduction in scan time, there was no difference between left‐ventricular ejection fraction (59.8 ± 4.5 vs. 60.8 ± 4.8, p = 0.46) or left‐ventricular mass (73.6 ± 19.4 g vs. 73.2 ± 19.7 g, p = 0.95) between R = 2 and R = 8 prospectively accelerated cine images.ConclusionsSCR‐DL enabled highly accelerated cardiac cine imaging, significantly reducing breath‐hold time. Compared with GRAPPA or SENSE‐DL, images reconstructed with SCR‐DL showed superior NMSE and SSIM.

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Present and Future Innovations in AI and Cardiac MRI

Cited by 13 publications

References 95 publications

Fifty Years of Cardiovascular Magnetic Resonance: Continuing Evolution Toward the “One-Stop Shop” for Cardiovascular Diagnosis

Fifty Years of Cardiovascular Magnetic Resonance: Continuing Evolution Toward the “One-Stop Shop” for Cardiovascular Diagnosis

Exercise MR of Skeletal Muscles, the Heart, and the Brain

Accelerated cardiac cine with spatio‐coil regularized deep learning reconstruction

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