RESUMEN
BACKGROUND: Proton therapy is a form of radiotherapy commonly used to treat various cancers. Due to its high conformality, minor variations in patient anatomy can lead to significant alterations in dose distribution, making adaptation crucial. While cone-beam computed tomography (CBCT) is a well-established technique for adaptive radiation therapy (ART), it cannot be directly used for adaptive proton therapy (APT) treatments because the stopping power ratio (SPR) cannot be estimated from CBCT images. PURPOSE: To address this limitation, Deep Learning methods have been suggested for converting pseudo-CT (pCT) images from CBCT images. In spite of convolutional neural networks (CNNs) have shown consistent improvement in pCT literature, there is still a need for further enhancements to make them suitable for clinical applications. METHODS: The authors introduce the 3D vision transformer (ViT) block, studying its performance at various stages of the proposed architectures. Additionally, they conduct a retrospective analysis of a dataset that includes 259 image pairs from 59 patients who underwent treatment for head and neck cancer. The dataset is partitioned into 80% for training, 10% for validation, and 10% for testing purposes. RESULTS: The SPR maps obtained from the pCT using the proposed method present an absolute relative error of less than 5% from those computed from the planning CT, thus improving the results of CBCT. CONCLUSIONS: We introduce an enhanced ViT3D architecture for pCT image generation from CBCT images, reducing SPR error within clinical margins for APT workflows. The new method minimizes bias compared to CT-based SPR estimation and dose calculation, signaling a promising direction for future research in this field. However, further research is needed to assess the robustness and generalizability across different medical imaging applications.
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Objective: We present the evolution of medical imaging software and its impact on the medical imaging community through the study of four open-source image analysis software platforms: 3D Slicer, FreeSurfer, FSL, and SPM. Materials and methods: We have studied the impact of these software tools over time, measured by the number of scientific citations. Additionally, we have also studied the source code evolution by measuring the lines of code and the tarball size of the stable releases and the changes in programming languages. Results and discussion: The rising number of related scientific publications confirms the popularity of these software tools in the research community, albeit some differences can be observed in the popularity of the tools. Moreover, we demonstrate that source code has evolved to modernize and optimize, at least partially thanks to the collaboration and code sharing with the user community. Furthermore, this evolution reveals an increased use of higher-level programming languages and meta-languages. Conclusions: The study of four open-source packages has revealed certain patterns in the evolution of medical imaging software and their impact on the medical image community. Further analyses and complementary metrics are suggested.
RESUMEN
We introduce a new calibration method for dual energy CT (DECT) based on material decomposition (MD) maps, specifically iodine and water MD maps. The aim of this method is to provide the first DECT calibration based on MD maps. The experiments were carried out using a general electric (GE) revolution CT scanner with ultra-fast kV switching and used a density phantom by GAMMEX for calibration and evaluation. The calibration process involves several steps. First, we tested the ability of MD values to reproduce Hounsfield unit (HU) values of single energy CT (SECT) acquisitions and it was found that the errors were below 1%, validating their use for HU reproduction. Next, the different definitions of computedZvalues were compared and the robustness of the approach based on the materials' composition was confirmed. Finally, the calibration method was compared with a previous method by Bourqueet al, providing a similar level of accuracy and superior performance in terms of precision. Overall, this novel DECT calibration method offers improved accuracy and reliability in determining tissue-specific physical properties. The resulting maps can be valuable for proton therapy treatments, where precise dose calculations and accurate tissue differentiation are crucial for optimal treatment planning and delivery.
