Deep‐learning based super‐resolution for 3D isotropic coronary MR angiography in less than a minute

Küstner, Thomas; Muñoz, Camila; Psenicny, Alina; Bustin, Aurélien; Fuin, Niccolò; Qi, Haikun; Neji, Radhouène; Kunze, Karl; Hajhosseiny, Reza; Prieto, Claudia; Botnar, René M.

doi:10.1002/mrm.28911

Cited by 34 publications

(27 citation statements)

References 75 publications

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“…In contrast to simple denoising algorithms, super-resolution aims to increase the spatial resolution via DL-based post-processing [ 40 , 41 , 42 ]. This concept was successfully implemented in head and neck imaging, as well as abdominal and cardiac imaging [ 40 , 43 , 44 ]. Especially fast sequences, such as gradient echo (GRE) imaging, benefit from these implementations, due to their relatively low signal-to-noise ratios.…”

Section: Deep Learning Applications In Radiologymentioning

confidence: 99%

Deep Learning Applications in Magnetic Resonance Imaging: Has the Future Become Present?

et al. 2021

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Deep learning technologies and applications demonstrate one of the most important upcoming developments in radiology. The impact and influence of these technologies on image acquisition and reporting might change daily clinical practice. The aim of this review was to present current deep learning technologies, with a focus on magnetic resonance image reconstruction. The first part of this manuscript concentrates on the basic technical principles that are necessary for deep learning image reconstruction. The second part highlights the translation of these techniques into clinical practice. The third part outlines the different aspects of image reconstruction techniques, and presents a review of the current literature regarding image reconstruction and image post-processing in MRI. The promising results of the most recent studies indicate that deep learning will be a major player in radiology in the upcoming years. Apart from decision and diagnosis support, the major advantages of deep learning magnetic resonance imaging reconstruction techniques are related to acquisition time reduction and the improvement of image quality. The implementation of these techniques may be the solution for the alleviation of limited scanner availability via workflow acceleration. It can be assumed that this disruptive technology will change daily routines and workflows permanently.

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Section: Deep Learning Applications In Radiologymentioning

confidence: 99%

Deep Learning Applications in Magnetic Resonance Imaging: Has the Future Become Present?

et al. 2021

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“…Extensive applications have been investigated in undersampled image-acquisition, automated analysis and post-processing and development of predictive models. x 4.8mm 3 data acquired in 50 s scan time (11). The proposed method showed similar quantitative and perceivable image quality of the high resolution 1.2 mm 3 images, achieving 16 x acceleration in acquisition time (Figure 3).…”

Section: Clinical Applicationsmentioning

confidence: 69%

“…Neural networks have been applied to reconstruct data from rapidly acquired undersampled MRI images across different sequences. A deep-learning based, super-resolution CMR Angiography framework has enabled reconstruction of low resolution 1.2 x 4.8 x 4.8mm 3 data acquired in 50 s scan time ( 11 ). The proposed method showed similar quantitative and perceivable image quality of the high resolution 1.2 mm 3 images, achieving 16 x acceleration in acquisition time ( Figure 3 ).…”

Section: Introductionmentioning

confidence: 99%

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Artificial Intelligence in Cardiac MRI: Is Clinical Adoption Forthcoming?

Fotaki

Puyol‐Antón

Chiribiri

et al. 2022

Front. Cardiovasc. Med.

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Artificial intelligence (AI) refers to the area of knowledge that develops computerised models to perform tasks that typically require human intelligence. These algorithms are programmed to learn and identify patterns from “training data,” that can be subsequently applied to new datasets, without being explicitly programmed to do so. AI is revolutionising the field of medical imaging and in particular of Cardiovascular Magnetic Resonance (CMR) by providing deep learning solutions for image acquisition, reconstruction and analysis, ultimately supporting the clinical decision making. Numerous methods have been developed over recent years to enhance and expedite CMR data acquisition, image reconstruction, post-processing and analysis; along with the development of promising AI-based biomarkers for a wide spectrum of cardiac conditions. The exponential rise in the availability and complexity of CMR data has fostered the development of different AI models. Integration in clinical routine in a meaningful way remains a challenge. Currently, innovations in this field are still mostly presented in proof-of-concept studies with emphasis on the engineering solutions; often recruiting small patient cohorts or relying on standardised databases such as Multi-ethnic Study on atherosclerosis (MESA), UK Biobank and others. The wider incorporation of clinically valid endpoints such as symptoms, survival, need and response to treatment remains to be seen. This review briefly summarises the current principles of AI employed in CMR and explores the relevant prospective observational studies in cardiology patient cohorts. It provides an overview of clinical studies employing undersampled reconstruction techniques to speed up the scan encompassing cine imaging, whole-heart imaging, multi-parametric mapping and magnetic resonance fingerprinting along with the clinical utility of AI applications in image post-processing, and analysis. Specific focus is given to studies that have incorporated CMR-derived prediction models for prognostication in cardiac disease. It also discusses current limitations and proposes potential developments to enable multi-disciplinary collaboration for improved evidence-based medicine. AI is an extremely promising field and the timely integration of clinician's input in the ingenious technical investigator's paradigm holds promise for a bright future in the medical field.

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“…Images are acquired at a lowresolution (with or without undersampling) and retrospectively reconstructed to the high-resolution target. This has been studied for cardiac cine (329,330) and whole-heart CMR (331)(332)(333)(334)(335)(336).…”

Section: Super Resolutionmentioning

confidence: 99%

Cardiac MR: From Theory to Practice

Ismail

Strugnell

Coletti

et al. 2022

Front. Cardiovasc. Med.

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Cardiovascular disease (CVD) is the leading single cause of morbidity and mortality, causing over 17. 9 million deaths worldwide per year with associated costs of over $800 billion. Improving prevention, diagnosis, and treatment of CVD is therefore a global priority. Cardiovascular magnetic resonance (CMR) has emerged as a clinically important technique for the assessment of cardiovascular anatomy, function, perfusion, and viability. However, diversity and complexity of imaging, reconstruction and analysis methods pose some limitations to the widespread use of CMR. Especially in view of recent developments in the field of machine learning that provide novel solutions to address existing problems, it is necessary to bridge the gap between the clinical and scientific communities. This review covers five essential aspects of CMR to provide a comprehensive overview ranging from CVDs to CMR pulse sequence design, acquisition protocols, motion handling, image reconstruction and quantitative analysis of the obtained data. (1) The basic MR physics of CMR is introduced. Basic pulse sequence building blocks that are commonly used in CMR imaging are presented. Sequences containing these building blocks are formed for parametric mapping and functional imaging techniques. Commonly perceived artifacts and potential countermeasures are discussed for these methods. (2) CMR methods for identifying CVDs are illustrated. Basic anatomy and functional processes are described to understand the cardiac pathologies and how they can be captured by CMR imaging. (3) The planning and conduct of a complete CMR exam which is targeted for the respective pathology is shown. Building blocks are illustrated to create an efficient and patient-centered workflow. Further strategies to cope with challenging patients are discussed. (4) Imaging acceleration and reconstruction techniques are presented that enable acquisition of spatial, temporal, and parametric dynamics of the cardiac cycle. The handling of respiratory and cardiac motion strategies as well as their integration into the reconstruction processes is showcased. (5) Recent advances on deep learning-based reconstructions for this purpose are summarized. Furthermore, an overview of novel deep learning image segmentation and analysis methods is provided with a focus on automatic, fast and reliable extraction of biomarkers and parameters of clinical relevance.

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Deep‐learning based super‐resolution for 3D isotropic coronary MR angiography in less than a minute

Cited by 34 publications

References 75 publications

Deep Learning Applications in Magnetic Resonance Imaging: Has the Future Become Present?

Deep Learning Applications in Magnetic Resonance Imaging: Has the Future Become Present?

Artificial Intelligence in Cardiac MRI: Is Clinical Adoption Forthcoming?

Cardiac MR: From Theory to Practice

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