Comparison of multicenter MRI protocols for visualizing the spinal cord gray matter

脊髓 白质 图像质量 计算机科学 灰色(单位) 磁共振成像 医学 人工智能 核医学 模式识别(心理学) 放射科 图像(数学) 精神科
作者
Julien Cohen‐Adad,Eva Alonso‐Ortiz,Stephanie Alley,Maria Marcella Laganà,Francesca Baglio,S. Johanna Vannesjo,Haleh Karbasforoushan,Maryam Seif,Alan C. Seifert,Junqian Xu,Joo-won Kim,René Labounek,Ľubomír Vojtíšek,Marek Dostál,Jan Valošek,Rebecca S. Samson,Francesco Grussu,Marco Battiston,Claudia A. M. Gandini Wheeler‐Kingshott,Marios Yiannakas
出处
期刊:Magnetic Resonance in Medicine [Wiley]
卷期号:88 (2): 849-859 被引量:7
标识
DOI:10.1002/mrm.29249
摘要

Purpose Spinal cord gray‐matter imaging is valuable for a number of applications, but remains challenging. The purpose of this work was to compare various MRI protocols at 1.5 T, 3 T, and 7 T for visualizing the gray matter. Methods In vivo data of the cervical spinal cord were collected from nine different imaging centers. Data processing consisted of automatically segmenting the spinal cord and its gray matter and co‐registering back‐to‐back scans. We computed the SNR using two methods (SNR_single using a single scan and SNR_diff using the difference between back‐to‐back scans) and the white/gray matter contrast‐to‐noise ratio per unit time. Synthetic phantom data were generated to evaluate the metrics performance. Experienced radiologists qualitatively scored the images. We ran the same processing on an open‐access multicenter data set of the spinal cord MRI ( N = 267 participants). Results Qualitative assessments indicated comparable image quality for 3T and 7T scans. Spatial resolution was higher at higher field strength, and image quality at 1.5 T was found to be moderate to low. The proposed quantitative metrics were found to be robust to underlying changes to the SNR and contrast; however, the SNR_single method lacked accuracy when there were excessive partial‐volume effects. Conclusion We propose quality assessment criteria and metrics for gray‐matter visualization and apply them to different protocols. The proposed criteria and metrics, the analyzed protocols, and our open‐source code can serve as a benchmark for future optimization of spinal cord gray‐matter imaging protocols.

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