Accelerated T2W Imaging with Deep Learning Reconstruction in Staging Rectal Cancer: A Preliminary Study

Lan Zhu; Bowen Shi; Bei Ding; Yihan Xia; Kangning Wang; Weiming Feng; Jiankun Dai; Tianyong Xu; Baisong Wang; Fei Yuan; Hailin Shen; Haipeng Dong; Huan Zhang

doi:10.1007/s10278-024-01345-x

Accelerated T2W Imaging with Deep Learning Reconstruction in Staging Rectal Cancer: A Preliminary Study

J Imaging Inform Med. 2024 Dec 11. doi: 10.1007/s10278-024-01345-x. Online ahead of print.

Authors

Lan Zhu^#¹, Bowen Shi^#¹, Bei Ding¹, Yihan Xia¹, Kangning Wang¹, Weiming Feng¹, Jiankun Dai², Tianyong Xu², Baisong Wang³, Fei Yuan⁴, Hailin Shen⁵, Haipeng Dong⁶, Huan Zhang⁷

Affiliations

¹ Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University of Medicine, No.197 Ruijin Er Road, Shanghai, 200025, China.
² Department of MR, GE Healthcare, Beijing, China.
³ Department of Biomedical Statistics, Shanghai Jiaotong University School of Medicine, Shanghai, China.
⁴ Department of Pathology, Ruijin Hospital, Shanghai Jiao Tong University of Medicine, No.197 Ruijin Er Road, Shanghai, 200025, China.
⁵ Department of Radiology, Suzhou Kowloon Hospital, Shanghai Jiao Tong University of Medicine, No.118 Wansheng Street, Suzhou Industrial Park, Suzhou City, 215028, Jiangsu Province, China. hailinshen@163.com.
⁶ Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University of Medicine, No.197 Ruijin Er Road, Shanghai, 200025, China. dhp40427@rjh.com.cn.
⁷ Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University of Medicine, No.197 Ruijin Er Road, Shanghai, 200025, China. huanzhangy@163.com.

^# Contributed equally.

PMID: 39663320
DOI: 10.1007/s10278-024-01345-x

Abstract

Deep learning reconstruction (DLR) has exhibited potential in saving scan time. There is limited research on the evaluation of accelerated acquisition with DLR in staging rectal cancers. Our first objective was to explore the best DLR level in saving time through phantom experiments. Resolution and number of excitations (NEX) adjusted for different scan time, image quality of conventionally reconstructed T2W images were measured and compared with images reconstructed with different DLR level. The second objective was to explore the feasibility of accelerated T2W imaging with DLR in image quality and diagnostic performance for rectal cancer patients. 52 patients were prospectively enrolled to undergo accelerated acquisition reconstructed with highly-denoised DLR (DLR_H_40sec) and conventional reconstruction (ConR_2min). The image quality and diagnostic performance were evaluated by observers with varying experience and compared between protocols using κ statistics and area under the receiver operating characteristic curve (AUC). The phantom experiments demonstrated that DLR_H could achieve superior signal-to-noise ratio (SNR), detail conspicuity, sharpness, and less distortion within the least scan time. The DLR_H_40sec images exhibited higher sharpness and SNR than ConR_2min. The agreements with pathological TN-stages were improved using DLR_H_40sec images compared to ConR_2min (T: 0.846vs. 0.771, 0.825vs. 0.700, and 0.697vs. 0.512; N: 0.527vs. 0.521, 0.421vs. 0.348 and 0.517vs. 0.363 for junior, intermediate, and senior observes, respectively). Comparable AUCs to identify T3-4 and N1-2 tumors were achieved using DLR_H_40sec and ConR_2min images (P > 0.05). Consequently, with 2/3-time reduction, DLR_H_40sec images showed improved image quality and comparable TN-staging performance to conventional T2W imaging for rectal cancer patients.

Keywords: Deep Learning Reconstruction; Image Quality; Rectal cancer; T2WI.

Abstract

Grants and funding