Exploring transformer reliability in clinically significant prostate cancer segmentation: A comprehensive in-depth investigation.

Despite the growing prominence of transformers in medical image segmentation, their application to clinically significant prostate cancer (csPCa) has been overlooked. Minimal attention has been paid to domain shift analysis and uncertainty assessment, critical for safely implementing computer-aided diagnosis (CAD) systems. Domain shift in medical imagery refers to differences between the data used to train a model and the data evaluated later, arising from variations in imaging equipment, protocols, patient populations, and acquisition noise.

Development of a novel computational technique to create DNA and cell geometrical models for Geant4-DNA.

Background: This study aimed to develop a novel human cell geometry for the Geant4-DNA simulation toolkit that explicitly incorporates all 23 chromosome pairs of the human cell. This approach contrasts with the existing, default human cell, geometrical model, which utilizes a continuous Hilbert curve.

Methods: A Python-based tool named "complexDNA" was developed to facilitate the design of both simple and complex DNA geometries.

Cross-modal tumor segmentation using generative blending augmentation and self-training.

Objectives: Data scarcity and domain shifts lead to biased training sets that do not accurately represent deployment conditions. A related practical problem is cross-modal image segmentation, where the objective is to segment unlabelled images using previously labelled datasets from other imaging modalities.

Methods: We propose a cross-modal segmentation method based on conventional image synthesis boosted by a new data augmentation technique called Generative Blending Augmentation (GBA).

Monte Carlo Dosimetry Validation for X-Ray Guided Endovascular Procedures.

Background: Endovascular treatment is continuously gaining ground in vascular surgery procedures. However, current patient radiation dose estimation does not take into account the exact patient morphology and organs' composition. Monte Carlo (MC) simulation can accurately estimate the dose by recreating the irradiation process generated during X-ray-guided interventions.

Fast dose calculation in x-ray guided interventions by using deep learning.

Patient dose estimation in x-ray-guided interventions is essential to prevent radiation-induced biological side effects. Current dose monitoring systems estimate the skin dose based in dose metrics such as the reference air kerma. However, these approximations do not take into account the exact patient morphology and organs composition.

Contour-guided deep learning based deformable image registration for dose monitoring during CBCT-guided radiotherapy of prostate cancer.

Purpose: To evaluate deep learning (DL)-based deformable image registration (DIR) for dose accumulation during radiotherapy of prostate cancer patients.

Methods And Materials: Data including 341 CBCTs (209 daily, 132 weekly) and 23 planning CTs from 23 patients was retrospectively analyzed. Anatomical deformation during treatment was estimated using free-form deformation (FFD) method from Elastix and DL-based VoxelMorph approaches.

Optimisation and validation of a co-manipulated robot for brachytherapy procedure.

Background: Low-dose rate brachytherapy is the referent treatment for early-stage prostate cancer and consists in manually inserting radioactive seeds within the organ to destroy tumorous cells. This treatment is inaccurate leading to side effects. Researchers developed robots to improve this technique.

Deep learning-based segmentation in prostate radiation therapy using Monte Carlo simulated cone-beam computed tomography.

Purpose: Segmenting organs in cone-beam CT (CBCT) images would allow to adapt the radiotherapy based on the organ deformations that may occur between treatment fractions. However, this is a difficult task because of the relative lack of contrast in CBCT images, leading to high inter-observer variability. Deformable image registration (DIR) and deep-learning based automatic segmentation approaches have shown interesting results for this task in the past years.

VMAT-Based Planning Allows Sparing of a Spatial Dose Pattern Associated with Radiation Pneumonitis in Patients Treated with Radiotherapy for a Locally Advanced Lung Cancer.

Introduction: In patients treated with radiotherapy for locally advanced lung cancer, respect for dose constraints to organs at risk (OAR) insufficiently protects patients from acute pulmonary toxicity (APT), such toxicities being associated with a potential impact on the treatment’s completion and the patient’s quality of life. Dosimetric planning does not take into account regional lung functionality. An APT prediction model combining usual dosimetry features with the mean dose (DMeanPmap) received by a voxel-based volume (Pmap) localized in the posterior right lung has been previously developed.

Organ-based estimation and minimization of clinician's X-ray dose.

Purpose: Hybrid surgeries, allowing real-time visualization of patient inner anatomy, are possible through the use of intraoperative X-ray imaging. However, the intensive use of X-rays can have undesired consequences for the clinicians or the patient in the operating room (OR).

Methods: In this paper, we provide a tool to visualize the X-rays and to optimally place protective shields in the hybrid operating room to reduce the clinician's dose according to their most sensitive body parts.

Standardization and Validation of Brachytherapy Seeds' Modelling Using GATE and GGEMS Monte Carlo Toolkits.

This study aims to validate GATE and GGEMS simulation toolkits for brachytherapy applications and to provide accurate models for six commercial brachytherapy seeds, which will be freely available for research purposes. The AAPM TG-43 guidelines were used for the validation of two Low Dose Rate (LDR), three High Dose Rate (HDR), and one Pulsed Dose Rate (PDR) brachytherapy seeds. Each seed was represented as a 3D model and then simulated in GATE to produce one single Phase-Space (PHSP) per seed.

Technical note: Surface imaging for real-time patient positioning in external radiation therapy.

Purpose: In the last few years, there has been a growing interest in surface imaging for patient positioning in external radiation therapy. The aim of this study is to evaluate the accuracy of daily patient positioning using the Azure Kinect surface imaging.

Methods: A total of 50 fractions in 10 patients including lung, pelvic, and head and neck tumors were analyzed in real time.

Development and prospective validation of a spatial dose pattern based model predicting acute pulmonary toxicity in patients treated with volumetric arc-therapy for locally advanced lung cancer.

Introduction: (Chemo)-radiotherapy is the standard treatment for patients with locally advanced lung cancer (LALC) not accessible to surgery. Despite strict application of dose constraints, acute toxicities such as acute pulmonary toxicity (APT) remain frequent, and may impact treatment's compliance and patients' quality of life. Previously, on a population treated with intensity-modulated photon therapy or passive scattering proton therapy, spatial dose patterns associated with APT were identified in the lower lungs, especially in the posterior right lung.

Radiomics Analysis of 3D Dose Distributions to Predict Toxicity of Radiotherapy for Cervical Cancer.

Standard treatment for locally advanced cervical cancer (LACC) is chemoradiotherapy followed by brachytherapy. Despite radiation therapy advances, the toxicity rate remains significant. In this study, we compared the prediction of toxicity events after radiotherapy for locally advanced cervical cancer (LACC), based on either dose-volume histogram (DVH) parameters or the use of a radiomics approach applied to dose maps at the voxel level.

Dosimetric Validation of a GAN-Based Pseudo-CT Generation for MRI-Only Stereotactic Brain Radiotherapy.

Purpose: Stereotactic radiotherapy (SRT) has become widely accepted as a treatment of choice for patients with a small number of brain metastases that are of an acceptable size, allowing for better target dose conformity, resulting in high local control rates and better sparing of organs at risk. An MRI-only workflow could reduce the risk of misalignment between magnetic resonance imaging (MRI) brain studies and computed tomography (CT) scanning for SRT planning, while shortening delays in planning. Given the absence of a calibrated electronic density in MRI, we aimed to assess the equivalence of synthetic CTs generated by a generative adversarial network (GAN) for planning in the brain SRT setting.

Advanced Monte Carlo simulations of emission tomography imaging systems with GATE.

Built on top of the Geant4 toolkit, GATE is collaboratively developed for more than 15 years to design Monte Carlo simulations of nuclear-based imaging systems. It is, in particular, used by researchers and industrials to design, optimize, understand and create innovative emission tomography systems. In this paper, we reviewed the recent developments that have been proposed to simulate modern detectors and provide a comprehensive report on imaging systems that have been simulated and evaluated in GATE.

Surface imaging for real-time patient respiratory function assessment in intensive care.

Purpose: Monitoring of physiological parameters is a major concern in Intensive Care Units (ICU) given their role in the assessment of vital organ function. Within this context, one issue is the lack of efficient noncontact techniques for respiratory monitoring. In this paper, we present a novel noncontact solution for real-time respiratory monitoring and function assessment of ICU patients.

DVH-Based Inverse Planning Using Monte Carlo Dosimetry for LDR Prostate Brachytherapy.

Purpose: Inverse planning is an integral part of modern low-dose-rate brachytherapy. Current clinical planning systems do not exploit the total dose information and largely use the American Association of Physicists in Medicine TG-43 dosimetry formalism to ensure clinically acceptable planning times. Thus, suboptimal plans may be derived as a result of TG-43-related dose overestimation and nonconformity with dose distribution requirements.

Prostate Volume Segmentation in TRUS Using Hybrid Edge-Bhattacharyya Active Surfaces.

Objective: We present a new hybrid edge and region-based parametric deformable model, or active surface, for prostate volume segmentation in transrectal ultrasound (TRUS) images.

Methods: Our contribution is threefold. First, we develop a new edge detector derived from the radial bas-relief approach, allowing for better scalar prostate edge detection in low contrast configurations.

CASToR: a generic data organization and processing code framework for multi-modal and multi-dimensional tomographic reconstruction.

In tomographic medical imaging (PET, SPECT, CT), differences in data acquisition and organization are a major hurdle for the development of tomographic reconstruction software. The implementation of a given reconstruction algorithm is usually limited to a specific set of conditions, depending on the modality, the purpose of the study, the input data, or on the characteristics of the reconstruction algorithm itself. It causes restricted or limited use of algorithms, differences in implementation, code duplication, impractical code development, and difficulties for comparing different methods.

The MINDVIEW project: First results.

We present the first results of the MINDVIEW project. An innovative imaging system for the human brain examination, allowing simultaneous acquisition of PET/MRI images, has been designed and constructed. It consists of a high sensitivity and high resolution PET scanner integrated in a novel, head-dedicated, radio frequency coil for a 3T MRI scanner.

Fully automatic deformable registration of pretreatment MRI/CT for image-guided prostate radiotherapy planning.

Purpose: In prostate radiotherapy, dose distribution may be calculated on CT images, while the MRI can be used to enhance soft tissue visualization. Therefore, a registration between MR and CT images could improve the overall treatment planning process, by improving visualization with a demonstrated interobserver delineation variability when segmenting the prostate, which in turn can lead to a more precise planning. This registration must compensate for prostate deformations caused by changes in size and form between the acquisitions of both modalities.

A framework based on hidden Markov trees for multimodal PET/CT image co-segmentation.

Purpose: The purpose of this study was to investigate the use of a probabilistic quad-tree graph (hidden Markov tree, HMT) to provide fast computation, robustness and an interpretational framework for multimodality image processing and to evaluate this framework for single gross tumor target (GTV) delineation from both positron emission tomography (PET) and computed tomography (CT) images.

Methods: We exploited joint statistical dependencies between hidden states to handle the data stack using multi-observation, multi-resolution of HMT and Bayesian inference. This framework was applied to segmentation of lung tumors in PET/CT datasets taking into consideration simultaneously the CT and the PET image information.

Accelerated GPU based SPECT Monte Carlo simulations.

Monte Carlo (MC) modelling is widely used in the field of single photon emission computed tomography (SPECT) as it is a reliable technique to simulate very high quality scans. This technique provides very accurate modelling of the radiation transport and particle interactions in a heterogeneous medium. Various MC codes exist for nuclear medicine imaging simulations.