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

Beets-Tan RGH, Lambregts DMJ, Maas M, et al. Magnetic resonance imaging for clinical management of rectal cancer: Updated recommendations from the 2016 European Society of Gastrointestinal and Abdominal Radiology (ESGAR) consensus meeting. European radiology 2018;28(4):1465–1475.

Delli Pizzi A, Basilico R, Cianci R, et al. Rectal cancer MRI: protocols, signs and future perspectives radiologists should consider in everyday clinical practice. Insights into Imaging 2018;9(4):405–412.

Iannicelli E, Di Renzo S, Ferri M, et al. Accuracy of High-Resolution MRI with Lumen Distention in Rectal Cancer Staging and Circumferential Margin Involvement Prediction. Korean Journal of Radiology 2014;15(1).

Moreno CC, Sullivan PS, Kalb BT, et al. Magnetic resonance imaging of rectal cancer: staging and restaging evaluation. Abdominal Imaging 2015;40(7):2613–2629.

Benson AB, Venook AP, Al-Hawary MM, et al. Colon Cancer, Version 2.2021, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw 2021;19(3):329–359.

Horvat N, Carlos Tavares Rocha C, Clemente Oliveira B, Petkovska I, Gollub MJ. MRI of Rectal Cancer: Tumor Staging, Imaging Techniques, and Management. RadioGraphics 2019;39(2):367–387.

Rosenkrantz AB, Bennett GL, Doshi A, Deng F-M, Babb JS, Taneja SS. T2-weighted imaging of the prostate: Impact of the BLADE technique on image quality and tumor assessment. Abdominal Imaging 2014;40(3):552–559.

Hamilton J, Franson D, Seiberlich N. Recent advances in parallel imaging for MRI. Prog Nucl Magn Reson Spectrosc 2017;101:71–95.

Sprawls P. Magnetic resonance imaging: principles, methods, and techniques: Medical Physics Publishing Madison: 2000.

Google Scholar 

Chartrand G, Cheng PM, Vorontsov E, et al. Deep Learning: A Primer for Radiologists. Radiographics 2017;37(7):2113–2131.

Lebel RM. Performance characterization of a novel deep learning-based MR image reconstruction pipeline. arXiv preprint arXiv:200806559 2020.

Zerunian M, Pucciarelli F, Caruso D, et al. Artificial intelligence based image quality enhancement in liver MRI: a quantitative and qualitative evaluation. Radiol Med 2022;127(10):1098–1105.

Lee KL, Kessler DA, Dezonie S, et al. Assessment of deep learning-based reconstruction on T2-weighted and diffusion-weighted prostate MRI image quality. Eur J Radiol 2023;166:111017.

Zerunian M, Pucciarelli F, Caruso D, et al. Fast high-quality MRI protocol of the lumbar spine with deep learning-based algorithm: an image quality and scanning time comparison with standard protocol. Skeletal Radiol 2023.

Koch KM, Sherafati M, Arpinar VE, et al. Analysis and Evaluation of a Deep Learning Reconstruction Approach with Denoising for Orthopedic MRI. Radiology: Artificial Intelligence 2021;3(6).

Allen TJ, Henze Bancroft LC, Unal O, et al. Evaluation of a Deep Learning Reconstruction for High-Quality T2-Weighted Breast Magnetic Resonance Imaging. Tomography 2023;9(5):1949–1964.

Hahn S, Yi J, Lee HJ, et al. Image Quality and Diagnostic Performance of Accelerated Shoulder MRI With Deep Learning-Based Reconstruction. AJR American journal of roentgenology 2022;218(3):506–516.

Park JC, Park KJ, Park MY, Kim MH, Kim JK. Fast T2-Weighted Imaging With Deep Learning-Based Reconstruction: Evaluation of Image Quality and Diagnostic Performance in Patients Undergoing Radical Prostatectomy. Journal of magnetic resonance imaging : JMRI 2022;55(6):1735–1744.

Johnson PM, Lin DJ, Zbontar J, et al. Deep Learning Reconstruction Enables Prospectively Accelerated Clinical Knee MRI. Radiology 2023;307(2):e220425.

Kim B, Lee CM, Jang JK, Kim J, Lim SB, Kim AY. Deep learning-based imaging reconstruction for MRI after neoadjuvant chemoradiotherapy for rectal cancer: effects on image quality and assessment of treatment response. Abdom Radiol (NY) 2023;48(1):201–210.

Amin MB, Edge SB, Greene FL, et al. AJCC Cancer Staging Manual: 2017.

Kim M, Kim HS, Kim HJ, et al. Thin-Slice Pituitary MRI with Deep Learning-based Reconstruction: Diagnostic Performance in a Postoperative Setting. Radiology 2021;298(1):114–122.

Lauricella S, Caricato M, Masciana G, et al. Topographic lymph node staging system shows prognostic superiority compared to the 8th edition of AJCC TNM in gastric cancer. A western monocentric experience. Surg Oncol 2020;34:223–233.

Frankel WL, Jin M. Serosal surfaces, mucin pools, and deposits, oh my: challenges in staging colorectal carcinoma. Mod Pathol 2015;28 Suppl 1:S95-108.

Kim E, Kim K, Kim SH, et al. Impact of Mucin Proportion in the Pretreatment MRI on the Outcomes of Rectal Cancer Patients Undergoing Neoadjuvant Chemoradiotherapy. Cancer Res Treat 2019;51(3):1188–1197.

Brown G, Richards CJ, Bourne MW, et al. Morphologic predictors of lymph node status in rectal cancer with use of high-spatial-resolution MR imaging with histopathologic comparison. Radiology 2003;227(2):371–377.

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