Synthetic CT in Carbon Ion Radiotherapy of the Abdominal Site

Abstract

The generation of synthetic CT for carbon ion radiotherapy (CIRT) applications is challenging, since high accuracy is required in treatment planning and delivery, especially in an anatomical site as complex as the abdomen. Thirty-nine abdominal MRI-CT volume pairs were collected and a three-channel cGAN (accounting for air, bones, soft tissues) was used to generate sCTs. The network was tested on five held-out MRI volumes for two scenarios: (i) a CT-based segmentation of the MRI channels, to assess the quality of sCTs and (ii) an MRI manual segmentation, to simulate an MRI-only treatment scenario. The sCTs were evaluated by means of similarity metrics (e.g., mean absolute error, MAE) and geometrical criteria (e.g., dice coefficient). Recalculated CIRT plans were evaluated through dose volume histogram, gamma analysis and range shift analysis. The CT-based test set presented optimal MAE on bones (86.03 ± 10.76 HU), soft tissues (55.39 ± 3.41 HU) and air (54.42 ± 11.48 HU). Higher values were obtained from the MRI-only test set (MAEBONE = 154.87 ± 22.90 HU). The global gamma pass rate reached 94.88 ± 4.9% with 3%/3 mm, while the range shift reached a median (IQR) of 0.98 (3.64) mm. The three-channel cGAN can generate acceptable abdominal sCTs and allow for CIRT dose recalculations comparable to the clinical plans.

A review of the clinical introduction of 4D particle therapy research concepts

“…However, also the field of PT shows broad interest in implementing more online or hybrid approaches, especially including daily replanning and employing plan libraries [8] , [22] , [126] , [127] , [128] . Recently, several key players in the field of real-time adaptive PT joined forces [129] to tackle the remaining challenges and pave the way towards online adaptation in PT [130] , [131] , [132] , [133] , [134] , [135] , [136] . These challenges include aspects of dose accumulation, contour propagation, daily plan re-optimisation and approval as well as quality assurance of the online adaptive treatments [137] , [138] .…”

Virtual 4DCT generated from 4DMRI in gated particle therapy: phantom validation and application to lung cancer patients

“…Although deep learning-based strategies to generate synthetic CTs from MRI data are currently being investigated to derive the information required for treatment planning (Cusumano et al 2020, Boulanger et al 2021, these are not yet clinically implemented. This is mainly due to difficulties in their validation, and a limited number of studies is present in the literature for mobile organs because of anatomical complexity (Parrella et al 2023). In this context, the method originally proposed by Boye et al (2013), consisting in the generation of a virtual 4DCT via deformable image registration (DIR), can be more easily validated and has already shown its potential for proton and carbon ion therapy of thoraco-abdominal tumors (Bernatowicz et al 2016, Dolde et al 2019, Meschini et al 2020, Duetschler et al 2022, Meschini et al 2022a.…”

Automatic generation of subject-specific finite element models of the spine from magnetic resonance images

“…New techniques based on deep-learning are emerging that allow for the generation of synthetic CT (sCT) scans based on MR scans ( Florkow et al, 2020 ; Parrella et al, 2023 ; Zhao et al, 2023 ). These synthetic scans resemble CT images but are based on a set of MR sequences only.…”