Learning-based super-resolution technique significantly improves detection of coronary artery stenoses on 1.5T whole-heart coronary MRA

Ishida, Masaki; Nakayama, Ryohei; Uno, Mio; Ito, Tatsuro; Goto, Yoshitaka; Ichikawa, Yasutaka; Nagata, Motonori; Kitagawa, Kakuya; Nakamori, Shiro; Dohi, Kaoru; Ito, Masaaki; Sakuma, Hajime

doi:10.1186/1532-429x-16-s1-p218

Cited by 6 publications

(5 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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.

View full text Add to dashboard Cite

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.

show abstract

Section: Super Resolutionmentioning

confidence: 99%

Cardiac MR: From Theory to Practice

Ismail

Strugnell

Coletti

et al. 2022

Front. Cardiovasc. Med.

View full text Add to dashboard Cite

show abstract

“…Also, CNN-based methods can assist in the detection of artifacts 59 , prospective motion correction 60 , and image denoising 61 62 63 . In image super-resolution, deep learning techniques are implemented for reconstruction of higher-resolution images or image sequences from low-resolution images 64 65 66 . Further areas of application include image synthesis to derive new parametric images of tissue contrast from a collection of MR acquisitions 67 68 , quantitative susceptibility mapping (QSM) to noninvasively estimate magnetic susceptibility of biological tissue 69 70 , and MR fingerprinting (MRF) 71 .…”

Section: Data Postprocessing and Analysis: New Strategies With Deep L...mentioning

confidence: 99%

Multiparametric Functional MRI of the Kidney: Current State and Future Trends with Deep Learning Approaches

Zhang¹,

Schwartz

Küstner³

et al. 2022

Rofo

View full text Add to dashboard Cite

Background Until today, assessment of renal function has remained a challenge for modern medicine. In many cases, kidney diseases accompanied by a decrease in renal function remain undetected and unsolved, since neither laboratory tests nor imaging diagnostics provide adequate information on kidney status. In recent years, developments in the field of functional magnetic resonance imaging with application to abdominal organs have opened new possibilities combining anatomic imaging with multiparametric functional information. The multiparametric approach enables the measurement of perfusion, diffusion, oxygenation, and tissue characterization in one examination, thus providing more comprehensive insight into pathophysiological processes of diseases as well as effects of therapeutic interventions. However, application of multiparametric fMRI in the kidneys is still restricted mainly to research areas and transfer to the clinical routine is still outstanding. One of the major challenges is the lack of a standardized protocol for acquisition and postprocessing including efficient strategies for data analysis. This article provides an overview of the most common fMRI techniques with application to the kidney together with new approaches regarding data analysis with deep learning. Methods This article implies a selective literature review using the literature database PubMed in May 2021 supplemented by our own experiences in this field. Results and Conclusion Functional multiparametric MRI is a promising technique for assessing renal function in a more comprehensive approach by combining multiple parameters such as perfusion, diffusion, and BOLD imaging. New approaches with the application of deep learning techniques could substantially contribute to overcoming the challenge of handling the quantity of data and developing more efficient data postprocessing and analysis protocols. Thus, it can be hoped that multiparametric fMRI protocols can be sufficiently optimized to be used for routine renal examination and to assist clinicians in the diagnostics, monitoring, and treatment of kidney diseases in the future. Key Points: Citation Format

show abstract

“…There are several methods reported such as inter-slice reconstruction applied to 2D multislice MRI [99], image domain SR via patchbased sparse representation using overcomplete dictionaries [100], and deep learning-based SR [101][102][103]. In coronary MRA images, dictionary-based super-resolution applied to 1.5 T non-contrast coronary MRA was reported by Ishida et al [104]. Their SR technique showed significant improvement in the detection of coronary artery stenosis as compared to conventional resolution coronary MRA.…”

Section: Image Quality Improvement (1): High-resolution Coronary Mra mentioning

confidence: 99%

Non-contrast coronary magnetic resonance angiography: current frontiers and future horizons

Kato

Ambale-Venkatesh

Kassai

et al. 2020

Magn Reson Mater Phy

View full text Add to dashboard Cite

Coronary magnetic resonance angiography (coronary MRA) is advantageous in its ability to assess coronary artery morphology and function without ionizing radiation or contrast media. However, technical limitations including reduced spatial resolution, long acquisition times, and low signal-to-noise ratios prevent it from clinical routine utilization. Nonetheless, each of these limitations can be specifically addressed by a combination of novel technologies including super-resolution imaging, compressed sensing, and deep-learning reconstruction. In this paper, we first review the current clinical use and motivations for non-contrast coronary MRA, discuss currently available coronary MRA techniques, and highlight current technical developments that hold unique potential to optimize coronary MRA image acquisition and post-processing. In the final section, we examine the various research-based coronary MRA methods and metrics that can be leveraged to assess coronary stenosis severity, physiological function, and atherosclerotic plaque characterization. We specifically discuss how such technologies may contribute to the clinical translation of coronary MRA into a robust modality for routine clinical use.

show abstract

Learning-based super-resolution technique significantly improves detection of coronary artery stenoses on 1.5T whole-heart coronary MRA

Cited by 6 publications

References 0 publications

Cardiac MR: From Theory to Practice

Cardiac MR: From Theory to Practice

Multiparametric Functional MRI of the Kidney: Current State and Future Trends with Deep Learning Approaches

Non-contrast coronary magnetic resonance angiography: current frontiers and future horizons

Contact Info

Product

Resources

About