Evidence map›Paper›PMID 42855900›Full record

ArticleJournal of applied clinical medical physics2026

Frequency Schrödinger bridge diffusion model for fast MRI reconstruction in abdominal MR-guided online adaptive radiotherapy.

Zhiqun Wang, Shaobin Wang, Nan Liu, Qiu Guan, Qi Chen, Fang Chen, Bei Wang, Yongguang Liang, Xia Liu, Bo Yang and 3 more

Abstract read
In one paragraph

Article in Journal of applied clinical medical physics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Zhiqun Wang *Department of Radiation Oncology, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China.ORCID https://orcid.org/0009-0007-0545-0229
Shaobin Wang *School of Biomedical Engineering, Tsinghua University, Beijing, China.
Nan LiuDepartment of Radiation Oncology, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China.
Qiu GuanDepartment of Radiation Oncology, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China.
Qi ChenMedMind Technology Co., Ltd, Beijing, China.
Fang ChenSchool of Biomedical Engineering and the Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China.
Bei WangDepartment of Radiation Oncology, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China.
Yongguang LiangDepartment of Radiation Oncology, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China.
Xia LiuDepartment of Radiation Oncology, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China.
Bo YangDepartment of Radiation Oncology, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China.
Lang YuDepartment of Radiation Oncology, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China.
Hongen LiaoSchool of Biomedical Engineering, Tsinghua University, Beijing, China.
Jie QiuDepartment of Radiation Oncology, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China.

Funding

Ministry of Science and Technology of the People's Republic of China 2022YFC2404606National Key R&D Program of ChinaScience and Technology Program Project of the State Administration for Market Regulation 2024MK196
6 · The paper itself

Abstract

backgroundOnline adaptive radiotherapy (ART) employs modalities like CBCT, CT, and MRI. For abdominal malignancies, MRI stands out with superior soft-tissue contrast, no ionizing radiation, and functional imaging capabilities. Yet, MRI-guided ART for such cases is challenged by long treatment time. Thus, boosting efficiency, especially via accelerated MRI scanning, is vital. PURPOSE: This study presents the first accurate and fast MRI reconstruction method tailored for the Unity MR-Linear system. By slashing MRI acquisition time, it aims to enhance patient compliance, reduce intra-fraction anatomical motion, and optimize clinical resource use.

methodsA frequency Schrödinger bridge diffusion (FSBD) model for MRI reconstruction (converting 22-second MRI to full-duration MRI) was proposed, based on a retrospective analysis of 31 fractional MRI scans from 14 patients. Its reconstruction performance was compared with state-of-the-art methods: paired diffusion-based Refusion, and unpaired CycleGAN and CSGAN. Of the initially acquired 40 fractional scans, 9 examinations were excluded following predefined exclusion criteria, leaving 31 valid cases. Dataset was split strictly at patient level: 23 fractional scans for training and the remaining independent patients for testing. An extra baseline using pairwise full-duration MRI from different fractions of identical patients was supplemented for reliability verification. All metrics were calculated on complete 3D volumetric MRI rather than individual 2D slices.

resultsQuantitative evaluation shows the FSBD model's superiority. It achieved significantly lower error metrics: MAE (86.00) and RMSE (123.42), outperforming Refusion (MAE: 99.95, RMSE: 172.55), CycleGAN (MAE: 112.92, RMSE: 149.16), and CSGAN (MAE: 103.04, RMSE: 162.29). In image quality, FSBD's PSNR (28.44 dB) and SSIM (0.885) surpassed Refusion (26.77 dB / 0.870), CycleGAN (25.82 dB / 0.881), and CSGAN (26.34 dB / 0.878). Moreover, it delivered high-fidelity reconstruction with an efficient 20-s inference time.

conclusionsThis study is the first to propose a sub-one-minute MRI imaging method for the Unity MR linear accelerator in an MR-guided online adaptive radiotherapy (ART) workflow. Experimental results confirm the method's significantly better reconstruction performance than state-of-the-art approaches. Total MRI imaging time dropped drastically from 3 min (full-duration) to 42 s (22 seconds for MR imaging plus 20 seconds for reconstruction). This reduction brings notable clinical benefits, including improved patient compliance and fewer intra-fractional anatomical variations.

Indexed as

Abdominal NeoplasmsAlgorithmsImage Processing, Computer-AssistedMagnetic Resonance ImagingRadiotherapy, Image-GuidedRadiotherapy Planning, Computer-AssistedHumansRadiotherapy DosageRadiotherapy, Intensity-ModulatedRetrospective Studiesabdominal imagingfast MRI reconstructionfrequency Schrödinger bridge diffusion (FSBD)MR‐guided online adaptive radiotherapy

Identifiers

PMID42855900
PMCPMC13653140

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.