Challenges and opportunities in the development and clinical implementation of artificial intelligence based synthetic computed tomography for magnetic resonance only radiotherapy

被引:5
|
作者
Villegas, Fernanda [1 ,2 ]
Dal Bello, Riccardo [3 ,4 ]
Alvarez-Andres, Emilie [5 ,6 ,7 ]
Dhont, Jennifer [8 ,9 ]
Janssen, Tomas [10 ]
Milan, Lisa [11 ]
Robert, Charlotte [12 ,13 ]
Salagean, Ghizela-Ana-Maria [14 ,15 ]
Tejedor, Natalia [16 ]
Trnkova, Petra [17 ]
Fusella, Marco [18 ]
Placidi, Lorenzo [19 ]
Cusumano, Davide [20 ]
机构
[1] Karolinska Inst, Dept Oncol Pathol, Solna, Sweden
[2] Karolinska Univ Hosp, Med Radiat Phys & Nucl Med, Radiotherapy Phys & Engn, Stockholm, Sweden
[3] Univ Hosp Zurich, Dept Radiat Oncol, Zurich, Switzerland
[4] Univ Zurich, Zurich, Switzerland
[5] TUD Dresden Univ Technol, Helmholtz Zentrum Dresden Rossendorf, OncoRay Natl Ctr Radiat Res Oncol, Med Fac, Dresden, Germany
[6] TUD Dresden Univ Technol, Univ Hosp Carl Gustav Carus, Helmholtz Zentrum Dresden Rossendorf, Dresden, Germany
[7] TUD Dresden Univ Technol, Fac Med Carl Gustav Carus, Dresden, Germany
[8] Univ Libre Bruxelles ULB, Hop Univ Bruxelles HUB, Inst Jules Bordet, Dept Med Phys, Brussels, Belgium
[9] Univ Libre Bruxelles ULB, Radiophys & MRI Phys Lab, Brussels, Belgium
[10] Netherlands Canc Inst, Dept Radiat Oncol, Amsterdam, Netherlands
[11] Ente Osped Cantonale, Med Phys Div, Imaging Inst Southern Switzerland IIMSI, Bellinzona, Switzerland
[12] Paris Saclay Univ, Mol Radiotherapy & Therapeut Innovat UMR 1030, ImmunoRadAI, Inst Gustave Roussy,Inserm, Villejuif, France
[13] Gustave Roussy, Dept Radiat Oncol, Villejuif, France
[14] Babes Bolyai Univ, Fac Phys, Cluj Napoca, Romania
[15] TopMed Med Ctr, Dept Radiat Oncol, Targu Mures, Romania
[16] Hosp Santa Creu i St Pau, Dept Med Phys & Radiat Protect, Barcelona, Spain
[17] Med Univ Vienna, Dept Radiat Oncol, Vienna, Austria
[18] Abano Terme Hosp, Dept Radiat Oncol, Abano Terme, Italy
[19] Fdn Policlin Univ Agostino Gemelli, Dept Diag Imaging Oncol Radiotherapy & Hematol, IRCCS, Rome, Italy
[20] Mater Olbia Hosp, Str Statale Orientale Sarda 125, Olbia, Sassari, Italy
关键词
MR-only radiotherapy; MR-only planning; Synthetic CT; Clinical implementation; Deep learning; Artificial intelligence; CONVOLUTIONAL NEURAL-NETWORK; DEEP LEARNING APPROACH; MR-LINAC SYSTEMS; CT GENERATION; GUIDED RADIOTHERAPY; ONLY PHOTON; IMAGES; HEAD; RECOMMENDATIONS; UNCERTAINTIES;
D O I
10.1016/j.radonc.2024.110387
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
Synthetic computed tomography (sCT) generated from magnetic resonance imaging (MRI) can serve as a substitute for planning CT in radiation therapy (RT), thereby removing registration uncertainties associated with multi -modality imaging pairing, reducing costs and patient radiation exposure. CE/FDA-approved sCT solutions are nowadays available for pelvis, brain, and head and neck, while more complex deep learning (DL) algorithms are under investigation for other anatomic sites. The main challenge in achieving a widespread clinical implementation of sCT lies in the absence of consensus on sCT commissioning and quality assurance (QA), resulting in variation of sCT approaches across different hospitals. To address this issue, a group of experts gathered at the ESTRO Physics Workshop 2022 to discuss the integration of sCT solutions into clinics and report the process and its outcomes. This position paper focuses on aspects of sCT development and commissioning, outlining key elements crucial for the safe implementation of an MRI-only RT workflow.
引用
收藏
页数:12
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