Deep learning based synthetic CT from cone beam CT generation for abdominal paediatric radiotherapy

Abstract

Objective: Adaptive radiotherapy workflows require images with the quality of computed tomography
(CT) for re-calculation and re-optimisation of radiation doses. In this work we aim to improve quality of
cone beam CT (CBCT) images for dose calculation using deep learning.
Approach: We propose a novel framework for CBCT-to-CT synthesis using cycle-consistent Generative Adversarial Networks (cycleGANs). The framework was tailored for paediatric abdominal patients, a
challenging application due to the inter-fractional variability in bowel filling and smaller patient numbers.
We introduced the concept of global residuals only learning to the networks and modified the cycleGAN
loss function to explicitly promote structural consistency between source and synthetic images. Finally, to
compensate for the anatomical variability and address the difficulties in collecting large datasets in the paediatric population, we applied a smart 2D slice selection based on the common field-of-view across
the dataset (abdomen). This acted as a weakly paired data approach that allowed us to take advantage of
scans from patients treated for a variety of malignancies (thoracic-abdominal-pelvic) for training
purposes. We first optimised the proposed framework and benchmarked its performance on a
development dataset. Later, a comprehensive quantitative evaluation was performed on an unseen dataset, which included calculating global image similarity metrics, segmentation-based measures and
proton therapy-specific metrics.
Main results: We found improved performance, compared to a baseline implementation, on imagesimilarity
metrics such as Mean Absolute Error calculated for a matched virtual CT (55.0±16.6 proposed, 58.9±16.8 baseline). There was also a higher level of structural agreement for gastrointestinal gas between source and synthetic images measured through dice similarity overlap (0.872±0.053 proposed, 0.846±0.052 baseline). Differences found in water-equivalent thickness metrics were also smaller for
our method (3.3±2.4% proposed, 3.7±2.8% baseline).
Significance: Our findings indicate that our innovations to the cycleGAN framework improved the quality
and structure consistency of the synthetic CTs generated.

Supervised deep learning-based synthetic computed tomography from kilovoltage cone-beam computed tomography images for adaptive radiation therapy in head and neck cancer

“…These anatomical variations could potentially impact the training of the U-Net algorithm, which relies on paired image data. Therefore, exploring unsupervised models such as GANs could enhance the model's performance, particularly when dealing with unpaired image data (intra-individual co-registration) [18,[24][25][26][27][28][29][30]. Furthermore, the process of rigidly registering the CT and CBCT images might not have been adequate in establishing the required image similarity for network training.…”

A Deep Learning Approach for the Fast Generation of Synthetic Computed Tomography from Low-Dose Cone Beam Computed Tomography Images on a Linear Accelerator Equipped with Artificial Intelligence

“…Providing ED information from CBCT images was a topic of research for several years, but only with the use of DL approaches was it possible to obtain levels of dose accuracy reliable for clinical use [18,19].…”

The Applications and Pitfalls of Cone-Beam Computed Tomography-Based Synthetic Computed Tomography for Adaptive Evaluation in Pencil-Beam Scanning Proton Therapy

“…They concluded that synthetic CT is a more efficient and accurate adaptive workflow in proton therapy. In addition, several publications [19][20][21][22][23][24][25] ventured into AI-based synthetic CT generation that is specifically geared toward proton therapy application. Landry et al [19] compared VMAT (Volumetric-Modulated Arc Therapy) and IMPT (Intensity-Modulated Proton Therapy) utilizing AI method (U-Net).…”