Tissue Characterization with MRI: The Value of the MR Parameters

Ortendahl, Douglas A.; Hylton, Nola M.; Kaufman, Léon

doi:10.1007/978-3-642-74993-3_20

Cited by 2 publications

(2 citation statements)

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“…However, despite an essential role in routine clinical diagnosis, the day‐to‐day use of MRI today is substantially limited to the qualitative assessment of contrast‐weighted images, which are created based on the variation of underlying MR tissue parameters (eg T 1 , T 2 ) across different types of tissue 3 . The changes in these tissue parameters in lesions typically result in hyper‐intense or hypo‐intense features, thus generating useful information for routine clinical diagnosis 4,5 …”

Section: Introductionmentioning

confidence: 99%

“…3 The changes in these tissue parameters in lesions typically result in hyper-intense or hypo-intense features, thus generating useful information for routine clinical diagnosis. 4,5 MRI also allows quantitative measurements of inherent tissue T 1 and T 2 values, which are referred to as T 1 /T 2 MR relaxometry or T 1 /T 2 mapping. The estimation of spin-lattice relaxation in the rotating frame (T 1ρ ) 6,7 (T 1ρ mapping) is also performed in some applications.…”

Section: Introductionmentioning

confidence: 99%

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Rapid MR relaxometry using deep learning: An overview of current techniques and emerging trends

Feng

Liu

2020

NMR in Biomedicine

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Quantitative mapping of MR tissue parameters such as the spin‐lattice relaxation time (T1), the spin‐spin relaxation time (T2), and the spin‐lattice relaxation in the rotating frame (T1ρ), referred to as MR relaxometry in general, has demonstrated improved assessment in a wide range of clinical applications. Compared with conventional contrast‐weighted (eg T1‐, T2‐, or T1ρ‐weighted) MRI, MR relaxometry provides increased sensitivity to pathologies and delivers important information that can be more specific to tissue composition and microenvironment. The rise of deep learning in the past several years has been revolutionizing many aspects of MRI research, including image reconstruction, image analysis, and disease diagnosis and prognosis. Although deep learning has also shown great potential for MR relaxometry and quantitative MRI in general, this research direction has been much less explored to date. The goal of this paper is to discuss the applications of deep learning for rapid MR relaxometry and to review emerging deep‐learning‐based techniques that can be applied to improve MR relaxometry in terms of imaging speed, image quality, and quantification robustness. The paper is comprised of an introduction and four more sections. Section 2 describes a summary of the imaging models of quantitative MR relaxometry. In Section 3, we review existing “classical” methods for accelerating MR relaxometry, including state‐of‐the‐art spatiotemporal acceleration techniques, model‐based reconstruction methods, and efficient parameter generation approaches. Section 4 then presents how deep learning can be used to improve MR relaxometry and how it is linked to conventional techniques. The final section concludes the review by discussing the promise and existing challenges of deep learning for rapid MR relaxometry and potential solutions to address these challenges.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Rapid MR relaxometry using deep learning: An overview of current techniques and emerging trends

Feng

Liu

2020

NMR in Biomedicine

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Myocardial Tissue Characterization by Cardiac Magnetic Resonance: A Primer for the Clinician

Raj

Gowda²,

Kothari³

2023

Journal of the Indian Academy of Echocardiography &Amp; Cardiovascular Imaging

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Imaging plays an important role in the diagnosis, management, and prognosis of cardiac conditions. Over the last three decades, cardiac magnetic resonance (CMR) has established itself as a promising imaging tool in the assessment of patients with various cardiac ailments. CMR is now being considered as a one-stop-shop diagnostic test because of its ability to comprehensively assess the heart. The greatest strength of CMR is its ability to characterize the myocardium noninvasively aiding in the diagnosis of patients. Despite its growing use worldwide, the adoption of the modality in India has been relatively slow. One major reason for this is the lack of expertise and resources to perform CMR. Another reason for the slow adoption of CMR in India is the limited understanding of practicing clinicians on how the modality works and what the various clinical applications of CMR are. In this review, we aimed to address these shortcomings by outlining some basics of CMR and its utility in day-to-day practice with a special focus on myocardial tissue characterization.

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Tissue Characterization with MRI: The Value of the MR Parameters

Cited by 2 publications

References 13 publications

Rapid MR relaxometry using deep learning: An overview of current techniques and emerging trends

Rapid MR relaxometry using deep learning: An overview of current techniques and emerging trends

Myocardial Tissue Characterization by Cardiac Magnetic Resonance: A Primer for the Clinician

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