Organizations: School of Mathematics and Statistics, Xi’an Jiaotong University, No.28, Xianning West Road, Xi’an, 710049, Shaanxi, China. · Research Center for Intelligent Medical Equipment and Devices (IMED), Xi’an Jiaotong University, No.28, Xianning West Road, Xi’an, 710049, Shaanxi, China. · Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi’an Jiaotong University, No.28, Xianning West Road, Xi’an, 710049, Shaanxi, China.
Magnetic Resonance Imaging (MRI) is a cornerstone in medicine and healthcare but suffers from long acquisition times. Traditional accelerated MRI methods optimize for generic image quality, lacking adaptability for specific clinical tasks. To address this, we introduce PASS (Personalized, Anomaly-aware Sampling and reconStruction), an intelligent MRI framework that leverages a Vision-Language Model (VLM) to guide a deep unrolling network for task-oriented, fast imaging. PASS dynamically personalizes the imaging pipeline through three core contributions: (1) a deep unrolled reconstruction network derived from a physics-based MRI model; (2) a sampling module that generates patient-specific k-space trajectories; and (3) an anomaly-aware prior, extracted from a pretrained VLM, which steers both sampling and reconstruction toward clinically relevant regions. By integrating the high-level clinical reasoning of a VLM with an interpretable, physics-aware network, PASS achieves superior image quality across diverse anatomies, contrasts, anomalies, and acceleration factors. This enhancement directly translates to improvements in downstream diagnostic tasks, including fine-grained anomaly detection, localization, and diagnosis.
Figures & tables
Figure 1: PASS is a closed-loop system for personalized MRI. It introduces a paradigm where a fine-tuned VLM ( NetAD ) guides the entire imaging pipeline from acquisition to reconstruction. As detailed in (a), the system overview, anomaly attention maps from the VLM direct a sampling network to acquire patient-specific k -space data, while simultaneously conditioning a deep unrolling reconstructor to enhance clinical features. The VLM’s adaptation process (b) and the specialized architectures for sampling (c) and reconstruction (d) enable this co-design, ensuring the output is optimized for diagnostic relevance. Central k -space regions (autocalibration signals, ACS) provide a low-resolution prior, while image- and pixel-level adapters (ILA/PLA) enable precise VLM fine-tuning.
Figure 2: Quantitative comparison of reconstruction methods across datasets and acceleration factors. Left and right columns correspond to 4× and 8× acceleration, respectively. a–b, T1w brain, c–d, FLAIR brain, e–f, PD knee. PSNR and SSIM are reported for each method, with bars indicating mean values across the test set and error bars representing standard deviation.
Figure 3: Visual comparison of reconstructed images at 4× (a) and 8× (b) acceleration across different methods. Representative results for T1w brain, FLAIR brain, and PD knee datasets. Each panel shows the reconstructed image and its corresponding error map relative to the fully sampled reference. PASS leads to sharper anatomical details and reduced residuals, particularly within pathological regions.
Figure 4: Lesion-focused reconstruction evaluation. Comparison of reconstruction quality across methods for T1w brain images. Left and right columns correspond to 4× and 8× acceleration, respectively. a-b, PSNR for the five most frequent lesions in the cohort: Type 1, Posttreatment change; Type 2, Mass; Type 3, Craniotomy; Type 4, Enlarged ventricles; Type 5, Edema. c-d, PSNR for five less frequent lesions in the cohort: Type 1, Resection cavity; Type 2, Normal variant; Type 3, Extra-axial mass; Type 4, Motion artifact; Type 5, Paranasal sinus opacification. e-f, Overall lesion-focused metrics (LF-PSNR and LF-SSIM) across all annotated regions. PASS consistently achieves superior fidelity in pathological areas.
Figure 5: Comparative analysis of sampling strategies for T1w brain images. Four strategies are evaluated: “No-learn”, a fixed non-learnable mask, “LS-MoDL”, a model-based baseline that jointly optimizes a reconstruction network and a learnable sampling mask, “Global Learn”, a simplified variant of our framework employing LOUPE-based global sampling shared across all subjects without anomaly-aware sampling, and “Ours”, the full PASS model integrating adaptive anomaly-aware sampling. a–b, Quantitative reconstruction results at 4× and 8× , respectively, including PSNR, SSIM, LF-PSNR, and LF-SSIM. c–d, Visual comparisons of reconstructed images and corresponding sampling masks at 4× and 8× . Red arrows indicate regions where PASS adaptively increases sampling density in lesion-relevant k -space areas compared with other strategies.
Figure 6: Evaluation of downstream pathological tasks for T1w brain images. a, Anomaly detection AUC of a VLM-based detection model on reconstructed images generated by different methods under 4× and 8× , evaluated across all lesions collectively. b, Localization of pathological regions by the VLM-based detection model on PASS-reconstructed images. c–d, Multi-label classification accuracy for individual lesion types using a fine-tuned CLIP image encoder with an MLP classifier, at 4× (c) and 8× (d), respectively.
NYU Center for Data Science, NY, USA · Center for Advanced Imaging Innovation and Research (CAI²R), Department of Radiology, NYU Grossman School of Medicine, NY, USA · Courant Institute of Mathematical Sciences, NY, USA +1