Quantitative Susceptibility Mapping MRI With Computer Vision Metrics to Reduce Scan Time for Brain Hemorrhage Assessment

IF 3 4区 计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC International Journal of Imaging Systems and Technology Pub Date : 2025-03-20 DOI:10.1002/ima.70070
Huiyu Huang, Shreyas Balaji, Bulent Aslan, Yan Wen, Magdy Selim, Ajith J. Thomas, Aristotelis Filippidis, Pascal Spincemaille, Yi Wang, Salil Soman
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Abstract

Optimizing clinical imaging parameters balances scan time and image quality. quantitative susceptibility mapping (QSM) MRI, particularly, for detecting intracranial hemorrhage (ICH), involves multiple echo times (TEs), leading to longer scan durations that can impact patient comfort and imaging efficiency. This study evaluates the necessity of specific TEs for QSM MRI in ICH patients and identifies shorter scan protocols using computer vision metrics (CVMs) to maintain diagnostic accuracy. Fifty-four patients with suspected ICH were retrospectively recruited. multiecho gradient recalled echo (mGRE) sequences with 11 TEs were used for QSM MRI (reference). Subsets of TEs compatible with producing QSM MRI images were generated, producing 71 subgroups per patient. QSM images from each subgroup were compared to reference images using 14 CVMs. Linear regression and Wilcoxon signed-rank tests identified optimal subgroups minimizing scan time while preserving image quality as part of the computer vision optimized rapid imaging (CORI) method described. CVM-based analysis demonstrated Subgroup 1 (TE1-3) to be optimal using several CVMs, supporting a reduction in scan time from 4.5 to 1.23 min (73% reduction). Other CVMs suggested longer maximum TE subgroups as optimal, achieving scan time reductions of 9%–37%. Visual assessments by a neuroradiologist and trained research assistant confirmed no significant difference in ICH area measurements between reference and CORI-identified optimal subgroup-derived QSM, while CORI-identified worst subgroups derived QSM differed significantly (p < 0.05). The findings support using shorter QSM MRI protocols for ICH evaluation and suggest CVMs may aid optimization efforts for other clinical imaging protocols.

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来源期刊
International Journal of Imaging Systems and Technology
International Journal of Imaging Systems and Technology 工程技术-成像科学与照相技术
CiteScore
6.90
自引率
6.10%
发文量
138
审稿时长
3 months
期刊介绍: The International Journal of Imaging Systems and Technology (IMA) is a forum for the exchange of ideas and results relevant to imaging systems, including imaging physics and informatics. The journal covers all imaging modalities in humans and animals. IMA accepts technically sound and scientifically rigorous research in the interdisciplinary field of imaging, including relevant algorithmic research and hardware and software development, and their applications relevant to medical research. The journal provides a platform to publish original research in structural and functional imaging. The journal is also open to imaging studies of the human body and on animals that describe novel diagnostic imaging and analyses methods. Technical, theoretical, and clinical research in both normal and clinical populations is encouraged. Submissions describing methods, software, databases, replication studies as well as negative results are also considered. The scope of the journal includes, but is not limited to, the following in the context of biomedical research: Imaging and neuro-imaging modalities: structural MRI, functional MRI, PET, SPECT, CT, ultrasound, EEG, MEG, NIRS etc.; Neuromodulation and brain stimulation techniques such as TMS and tDCS; Software and hardware for imaging, especially related to human and animal health; Image segmentation in normal and clinical populations; Pattern analysis and classification using machine learning techniques; Computational modeling and analysis; Brain connectivity and connectomics; Systems-level characterization of brain function; Neural networks and neurorobotics; Computer vision, based on human/animal physiology; Brain-computer interface (BCI) technology; Big data, databasing and data mining.
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