AI-accelerated compressed sensing and deep learning reconstruction for colon MRI: prospective validation of image quality and preliminary assessment of staging accuracy versus contrast-enhanced CT
Abstract
Objectives: To prospectively validate an AI-accelerated MRI protocol combining L₁-regularized compressed sensing and deep learning reconstruction for colon cancer staging, compared with contrast-enhanced CT (CECT).
Materials and methods: This single-center study (August 2022–April 2024) enrolled 132 patients with pathologically confirmed colon cancer who underwent both 3.0-T non-contrast MRI and CECT within 1 week. Patients unable to complete an MRI were excluded. MRI sequences included T1- and T2-weighted imaging and diffusion-weighted imaging. Image quality was assessed using a 4-point Likert scale, alongside quantitative analysis of contrast-to-noise ratio (CNR) and edge rise distance (ERD). Surgical pathology correlation was available for 33 cases. Five radiologists independently reviewed the images.
Results: Among 153 matched examination pairs from 132 patients (mean age, 58 ± 11 years), AI-MRI outperformed CECT in both qualitative and quantitative measures. MRI yielded higher image quality scores (4 [IQR 3–4] vs. 3 [IQR 3–3]), improved CNR (5.5 [IQR 4.2–7.0] vs. 1.3 [IQR 0.7–2.2]), and reduced ERD (1.6 mm [IQR 1.2–2.8] vs. 3.7 mm [IQR 2.8–5.0]). MRI provided superior peritoneal sharpness (75.8%) and colonic wall visualization (100%), and superior or equivalent tumor conspicuity (92.2%) and nodal assessment (86.9%), whereas CECT demonstrated better artifact control (45.1%). In preliminary analyses, MRI achieved higher T-staging accuracy (78.8% vs. 30.3%) and N-staging specificity (93.3% vs. 13.3%).
Conclusion: The AI-accelerated MRI protocol significantly outperformed CECT in image quality and preliminary staging accuracy for colon cancer evaluation while eliminating radiation exposure.
Key Points: Question Can an AI-accelerated MRI protocol provide superior image quality and staging performance compared with contrast-enhanced CT, which has limited diagnostic accuracy for colon cancer staging?
Findings AI-accelerated MRI using L₁-regularized compressed sensing and deep learning reconstruction achieved superior image quality and higher preliminary staging accuracy than contrast-enhanced CT.
Critical Relevance Statement AI-accelerated MRI improves image quality and staging confidence without radiation exposure, supporting its potential to enhance preoperative assessment and clinical decision-making in colon cancer.
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BibTeX
@article{guan2026aiaccelerated,
title = {{AI-accelerated compressed sensing and deep learning reconstruction for colon MRI: prospective validation of image quality and preliminary assessment of staging accuracy versus contrast-enhanced CT}},
author = {Zhen Guan and Nan Sun and Tian-Jing Chang and Xiao Zhang and Sha Liu and Zi-Ran Li and Jun Yang and Rui-Jia Sun and Xiao-Yan Zhang and Ying-Shi Sun},
journal = {Insights into Imaging},
year = {2026},
month = nov,
doi = {10.1186/s13244-026-02445-8},
url = {https://link.springer.com/article/10.1186/s13244-026-02445-8},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - AI-accelerated compressed sensing and deep learning reconstruction for colon MRI: prospective validation of image quality and preliminary assessment of staging accuracy versus contrast-enhanced CT
AU - Zhen Guan
AU - Nan Sun
AU - Tian-Jing Chang
AU - Xiao Zhang
AU - Sha Liu
AU - Zi-Ran Li
AU - Jun Yang
AU - Rui-Jia Sun
AU - Xiao-Yan Zhang
AU - Ying-Shi Sun
PY - 2026
DA - 2026/11/03
JO - Insights into Imaging
DO - 10.1186/s13244-026-02445-8
UR - https://link.springer.com/article/10.1186/s13244-026-02445-8
AB - Objectives: To prospectively validate an AI-accelerated MRI protocol combining L₁-regularized compressed sensing and deep learning reconstruction for colon cancer staging, compared with contrast-enhanced CT (CECT). Materials and methods: This single-center study (August 2022–April 2024) enrolled 132 patients with pathologically confirmed colon cancer who underwent both 3.0-T non-contrast MRI and CECT within 1 week. Patients unable to complete an MRI were excluded. MRI sequences included T1- and T2-weighted imaging and diffusion-weighted imaging. Image quality was assessed using a 4-point Likert scale, alongside quantitative analysis of contrast-to-noise ratio (CNR) and edge rise distance (ERD). Surgical pathology correlation was available for 33 cases. Five radiologists independently reviewed the images. Results: Among 153 matched examination pairs from 132 patients (mean age, 58 ± 11 years), AI-MRI outperformed CECT in both qualitative and quantitative measures. MRI yielded higher image quality scores (4 [IQR 3–4] vs. 3 [IQR 3–3]), improved CNR (5.5 [IQR 4.2–7.0] vs. 1.3 [IQR 0.7–2.2]), and reduced ERD (1.6 mm [IQR 1.2–2.8] vs. 3.7 mm [IQR 2.8–5.0]). MRI provided superior peritoneal sharpness (75.8%) and colonic wall visualization (100%), and superior or equivalent tumor conspicuity (92.2%) and nodal assessment (86.9%), whereas CECT demonstrated better artifact control (45.1%). In preliminary analyses, MRI achieved higher T-staging accuracy (78.8% vs. 30.3%) and N-staging specificity (93.3% vs. 13.3%). Conclusion: The AI-accelerated MRI protocol significantly outperformed CECT in image quality and preliminary staging accuracy for colon cancer evaluation while eliminating radiation exposure. Key Points: Question Can an AI-accelerated MRI protocol provide superior image quality and staging performance compared with contrast-enhanced CT, which has limited diagnostic accuracy for colon cancer staging? Findings AI-accelerated MRI using L₁-regularized compressed sensing and deep learning reconstruction achieved superior image quality and higher preliminary staging accuracy than contrast-enhanced CT. Critical Relevance Statement AI-accelerated MRI improves image quality and staging confidence without radiation exposure, supporting its potential to enhance preoperative assessment and clinical decision-making in colon cancer.
SN - 1869-4101
ER -