Sclocco R, Coll-Font J, Kuo B, Napadow V, Nguyen C
Department of Physical Medicine and Rehabilitation and Schoen and Adams Discovery Center for Chronic Pain Recovery, Spaulding Rehabilitation Hospital, Harvard Medical School, Charlestown, Massachusetts, USA.
Department of Gastroenterology and Center for Neurointestinal Health, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.
NMR Biomed. 2025 Nov;38(11):e70160. doi: 10.1002/nbm.70160.
Magnetic resonance imaging (MRI) applications to the study of gastric function in humans have started to incorporate dynamic volumetric imaging, thus calling for specialized approaches for motion correction. A method for retrospective respiratory motion correction in free-breathing, four-dimensional (4D) abdominal MRI is presented. Our gastric low-rank tensor-based (GLOW) algorithm uses a low-rank tensor (LRT) model to separate the temporal components that correspond to breathing motion from those related to gut motion, which are preserved due to being uncorrelated and spatially localized. As a proof-of-concept, the GLOW algorithm is applied to a human 4D gastric MRI dataset that includes data collected during both a fasted and fed state using a food-based contrast meal. This approach allows for a more robust and accurate assessment of gastric peristalsis. The GLOW algorithm represents an important step toward the effective application of noninvasive, naturalistic approaches to robustly and accurately evaluate gastric function via MRI.
磁共振成像（MRI）在人体胃功能研究中的应用已开始纳入动态容积成像，因此需要专门的运动校正方法。本文提出了一种用于自由呼吸的四维（4D）腹部MRI的回顾性呼吸运动校正方法。我们基于胃低秩张量的（GLOW）算法使用低秩张量（LRT）模型，将与呼吸运动对应的时间成分与肠道运动相关的成分分离，由于这些成分不相关且在空间上局部化，因此得以保留。作为概念验证，GLOW算法应用于一个人类4D胃MRI数据集，该数据集包括使用基于食物的对比餐在禁食和进食状态下收集的数据。这种方法能够对胃蠕动进行更稳健、准确的评估。GLOW算法代表了朝着有效应用非侵入性、自然主义方法通过MRI稳健且准确地评估胃功能迈出的重要一步。
