Quantitative MR Imaging for the Evaluation of Neurovascular Disease

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biomedical Engineering and Biomaterials".

Deadline for manuscript submissions: 30 September 2024 | Viewed by 1139

Special Issue Editors

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Guest Editor
Department of Radiology, The University of Chicago, Chicago, IL, USA
Interests: imaging; magnetic resonance; physics; stroke; cerebrovascular; vascular disease

E-Mail Website
Guest Editor
Department of Radiology (M.C.H.), University of Chicago, Chicago, IL, USA
Interests: diagnostic and interventional neuroradiology; neurointerventional surgery

Special Issue Information

Dear Colleagues,

Neurovascular disease is one of the leading causes of death and disability worldwide. A continuing series of technical innovations overcoming initial technical challenges since the 1980s has resulted in a plethora of magnetic resonance neurovascular imaging pulse sequences and post-processing algorithms targeting the vessel lumen (TOF-MRA, CE-MRA, TR-MRA), vessel wall (3D TSE BB), cerebral perfusion (ASL, DSC, IVIM), and permeability (DCE). MRI images are traditionally presented as proportional or “weighted” images in which the signal intensity depends not only on the specific type of pulse sequence used but also sequence parameters such at the TR, TE and flip angle. An increasing number of quantitative acquisitions have been derived and commercialized to generate absolute measurements of the contribution to signal with parameters such as T1, T2, susceptibility, phase shift, and enhancement to generate quantitative pathophysiological properties such as velocity, flow, permeability and even tissue perfusion (cerebral blood flow (CBF)). Accurate quantification has the advantages of truly objective cross-sectional comparisons of normative and pathologic values as well as longitudinal assessment of a single patient in response to compensatory mechanisms. For example, longitudinal quantification of cerebral blood flow (qCBF) can enhance the clinical evaluation of chronic cerebrovascular ischemia progression versus response to treatment in patients with intracranial atherosclerotic disease and helps ensure that individual patients are triaged to receive the appropriate treatment in the case of ischemic stroke.

The goal of this Special Issue, entitled “Quantitative MR Imaging for the Evaluation of Neurovascular Disease”, is to highlight recent work in the development, validation and application of quantitative MRI. We welcome original research papers, communications, and review articles that reflect recent technical developments of quantitative MRI in the evaluation of neurovascular disease.

Prof. Dr. Timothy J. Carroll
Prof. Dr. Michael C Hurley
Guest Editors

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  • quantification
  • neurovascular
  • stroke
  • atherosclerosis
  • aneurysm
  • chronic
  • intervention

Published Papers (1 paper)

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13 pages, 4515 KiB  
Multi-Echo Complex Quantitative Susceptibility Mapping and Quantitative Blood Oxygen Level-Dependent Magnitude (mcQSM + qBOLD or mcQQ) for Oxygen Extraction Fraction (OEF) Mapping
by Junghun Cho, Jinwei Zhang, Pascal Spincemaille, Hang Zhang, Thanh D. Nguyen, Shun Zhang, Ajay Gupta and Yi Wang
Bioengineering 2024, 11(2), 131; https://doi.org/10.3390/bioengineering11020131 - 29 Jan 2024
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Oxygen extraction fraction (OEF), the fraction of oxygen that tissue extracts from blood, is an essential biomarker used to directly assess tissue viability and function in neurologic disorders. In ischemic stroke, for example, increased OEF can indicate the presence of penumbra—tissue with low [...] Read more.
Oxygen extraction fraction (OEF), the fraction of oxygen that tissue extracts from blood, is an essential biomarker used to directly assess tissue viability and function in neurologic disorders. In ischemic stroke, for example, increased OEF can indicate the presence of penumbra—tissue with low perfusion yet intact cellular integrity—making it a primary therapeutic target. However, practical OEF mapping methods are not currently available in clinical settings, owing to the impractical data acquisitions in positron emission tomography (PET) and the limitations of existing MRI techniques. Recently, a novel MRI-based OEF mapping technique, termed QQ, was proposed. It shows high potential for clinical use by utilizing a routine sequence and removing the need for impractical multiple gas inhalations. However, QQ relies on the assumptions of Gaussian noise in susceptibility and multi-echo gradient echo (mGRE) magnitude signals for OEF estimation. This assumption is unreliable in low signal-to-noise ratio (SNR) regions like disease-related lesions, risking inaccurate OEF estimation and potentially impacting clinical decisions. Addressing this, our study presents a novel multi-echo complex QQ (mcQQ) that models realistic Gaussian noise in mGRE complex signals. We implemented mcQQ using a deep learning framework (mcQQ-NET) and compared it with the existing QQ-NET in simulations, ischemic stroke patients, and healthy subjects, using identical training and testing datasets and schemes. In simulations, mcQQ-NET provided more accurate OEF than QQ-NET. In the subacute stroke patients, mcQQ-NET showed a lower average OEF ratio in lesions relative to unaffected contralateral normal tissue than QQ-NET. In the healthy subjects, mcQQ-NET provided uniform OEF maps, similar to QQ-NET, but without unrealistically high OEF outliers in areas of low SNR, such as SNR ≤ 15 (dB). Therefore, mcQQ-NET improves OEF accuracy by more accurately reflecting realistic Gaussian noise in complex mGRE signals. Its enhanced sensitivity to OEF abnormalities, based on more realistic biophysics modeling, suggests that mcQQ-NET has potential for investigating tissue variability in neurologic disorders. Full article
(This article belongs to the Special Issue Quantitative MR Imaging for the Evaluation of Neurovascular Disease)
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