Motion-Informed 3D Deep Learning Reconstruction in Patients with Cognitive Impairment.

Background and purpose: Motion artifacts remain a key limitation in brain MRI, particularly during 3D acquisitions in cognitively impaired patients. Most deep learning (DL) reconstruction techniques improve signal-to-noise ratio but lack explicit mechanisms to correct for motion. This study aims to validate a DL reconstruction method that integrates retrospective motion correction into the reconstruction pipeline for 3D T1-weighted brain MRI.

Materials and methods: This prospective, intra-individual comparison study included a controlled-motion cohort of healthy volunteers and a clinical cohort of patients undergoing evaluation for memory loss. Each cohort was scanned at distinct imaging sites between October 2022 and August 2023 in staggered periods. All participants underwent 4-fold under-sampled 3D magnetization-prepared rapid gradient-echo imaging with integrated Scout Accelerated Motion Estimation and Reduction (SAMER) acquisition. Image volumes were reconstructed using standard-of-care methods and the proposed DL approach. Quantitative morphometric accuracy was assessed by comparing brain segmentation results of instructed-motion scans to motion-free reference scans in the healthy volunteers. Image quality was rated by two board-certified neuroradiologists using a five-point Likert scale. Statistical analysis included Wilcoxon tests and intraclass correlation coefficients.

Results: A total of 41 participants (15 women [37%]; mean age, 58 years) and 154 image volumes were evaluated. The DL-based method with integrated motion correction significantly reduced segmentation error under moderate and severe motion (12.4% to 3.5% and 44.2% to 12.5%, respectively; P < .001). Visual ratings showed improved scores across all criteria compared with standard reconstructions (overall image quality, 4.26 ± 0.72 vs. 3.59 ± 0.82; P < .001). In 47% of cases, motion artifact severity was improved following DL-based processing. Inter-reader agreement ranged from moderate to substantial.

Conclusions: Motion-informed DL reconstruction improved both morphometric accuracy and perceived image quality in 3D T1-weighted brain MRI. This technique may enhance diagnostic utility and reduce scan failure rates in motion-prone patients with cognitive impairment.

Abbreviations: AD = Alzheimer's disease; DL = deep learning; ICC = intra-class correlation coefficient; SAMER = scout accelerated motion estimation and reduction.