An improved calibration procedure for accurate plastic scintillation dosimetry on an MR-linac.

Plastic scintillation dosimeters (PSDs) are highly suitable for real-time dosimetry on the MR-linac. For optimal performance, the primary signal (scintillation) needs to be separated from secondary optical effects (Cerenkov, fluorescence and optical fiber attenuation). This requires a spectral separation approach and careful calibration.

Multi-task Bayesian model combining FDG-PET/CT imaging and clinical data for interpretable high-grade prostate cancer prognosis.

We propose a fully automatic multi-task Bayesian model, named Bayesian Sequential Network (BSN), for predicting high-grade (Gleason   8) prostate cancer (PCa) prognosis using pre-prostatectomy FDG-PET/CT images and clinical data. BSN performs one classification task and five survival tasks: predicting lymph node invasion (LNI), biochemical recurrence-free survival (BCR-FS), metastasis-free survival, definitive androgen deprivation therapy-free survival, castration-resistant PCa-free survival, and PCa-specific survival (PCSS). Experiments are conducted using a dataset of 295 patients.

Epigenetic memory of radiotherapy in dermal fibroblasts impairs wound repair capacity in cancer survivors.

Radiotherapy (RT), a common cancer treatment, unintentionally harms surrounding tissues, including the skin, and hinders wound healing years after treatment. This study aims to understand the mechanisms behind these late-onset adverse effects. We compare skin biopsies from previously irradiated (RT) and non-irradiated (RT) sites in breast cancer survivors who underwent RT years ago.

An hetero-modal deep learning framework for medical image synthesis applied to contrast and non-contrast MRI.

Some pathologies such as cancer and dementia require multiple imaging modalities to fully diagnose and assess the extent of the disease. Magnetic resonance imaging offers this kind of polyvalence, but examinations take time and can require contrast agent injection. The flexible synthesis of these imaging sequences based on the available ones for a given patient could help reduce scan times or circumvent the need for contrast agent injection.

External validation of the Memorial Sloan Kettering Cancer Center preoperative nomogram predicting lymph node invasion in a cohort of high-grade prostate cancer patients.

Background: Commonly used preoperative nomograms predicting clinical and pathological outcomes in prostate cancer (PCa) patients have not been yet validated in high-grade only PCa patients. Our objective is to perform an external validation of the Memorial Sloan Kettering Cancer Center (MSKCC) preoperative nomogram as a predictor of lymph node invasion (LNI) in a cohort of high-grade PCa patients.

Methods: We included patients with high-grade PCa (Gleason ≥8) treated at our institution between 2011 and 2020 with radical prostatectomy and pelvic lymph node dissection without receiving neoadjuvant or adjuvant therapy.

Unsupervised MRI motion artifact disentanglement: introducing MAUDGAN.

This study developed an unsupervised motion artifact reduction method for magnetic resonance imaging (MRI) images of patients with brain tumors. The proposed novel design uses multi-parametric multicenter contrast-enhanced T1W (ceT1W) and T2-FLAIR MRI images.The proposed framework included two generators, two discriminators, and two feature extractor networks.

Commissioning and implementing a Quality Assurance program for dedicated radiation oncology MRI scanners.

Purpose: ACR and AAPM task group's guidelines addressing commissioning for dedicated MR simulators were recently published. The goal of the current paper is to present the authors' 2-year experience regarding the commissioning and introduction of a QA program based on these guidelines and an associated automated workflow.

Methods: All mandatory commissioning tests suggested by AAPM report 284 were performed and results are reported for two MRI scanners (MAGNETOM Sola and Aera).

MedFusionGAN: multimodal medical image fusion using an unsupervised deep generative adversarial network.

Purpose: This study proposed an end-to-end unsupervised medical fusion generative adversarial network, MedFusionGAN, to fuse computed tomography (CT) and high-resolution isotropic 3D T1-Gd Magnetic resonance imaging (MRI) image sequences to generate an image with CT bone structure and MRI soft tissue contrast to improve target delineation and to reduce the radiotherapy planning time.

Methods: We used a publicly available multicenter medical dataset (GLIS-RT, 230 patients) from the Cancer Imaging Archive. To improve the models generalization, we consider different imaging protocols and patients with various brain tumor types, including metastases.

MRI motion artifact reduction using a conditional diffusion probabilistic model (MAR-CDPM).

Background: High-resolution magnetic resonance imaging (MRI) with excellent soft-tissue contrast is a valuable tool utilized for diagnosis and prognosis. However, MRI sequences with long acquisition time are susceptible to motion artifacts, which can adversely affect the accuracy of post-processing algorithms.

Purpose: This study proposes a novel retrospective motion correction method named "motion artifact reduction using conditional diffusion probabilistic model" (MAR-CDPM).

Patient-specific geometrical distortion corrections of MRI images improve dosimetric planning accuracy of vestibular schwannoma treated with gamma knife stereotactic radiosurgery.

Purpose: To investigate the impact of MRI patient-specific geometrical distortion (PSD) on the quality of Gamma Knife stereotactic radiosurgery (GK-SRS) plans of the vestibular schwannoma (VS) tumors.

Methods And Materials: Three open access datasets including the MPI-Leipzig Mind-Brain-Body (318 patients), the slow event-related fMRI designs dataset (62 patients), and the VS dataset (242 patients) were used. We used first two datasets to train a 3D convolution network to predict the distortion map of third dataset that were then used to calculate and correct the PSD.

Shuffle-ResNet: Deep learning for predicting LGG IDH1 mutation from multicenter anatomical MRI sequences.

The world health organization recommended to incorporate gene information such as isocitrate dehydrogenase 1 (IDH1) mutation status to improve prognosis, diagnosis, and treatment of the central nervous system tumors. We proposed our Shuffle Residual Network (Shuffle-ResNet) to predict IDH1 gene mutation status of the low grade glioma (LGG) tumors from multicenter anatomical magnetic resonance imaging (MRI) sequences including T2-w, T2-FLAIR, T1-w, and T1-Gd.We used 105 patient's dataset available in The Cancer Genome Atlas LGG project where we split them into training and testing datasets.

Knowledge-based planning algorithm for lung SBRT with robust Bayesian stochastic frontier analysis and missing data management.

Purpose: A knowledge-based planning technique is developed based on Bayesian stochastic frontier analysis. A novel missing data management is applied in order to handle missing organs-at-risk and work with a complete dataset.

Methods: Geometric metrics are used to predict DVH metrics for lung SBRT with a retrospective database of 299 patients.

Direct in-water radiation dose measurements using Cherenkov emission corrected signals from polarization imaging for a clinical radiotherapy application.

Cherenkov emission (CE) is a visible blueish light emitted in water mediums irradiated by most radiotherapy treatment beams. However, CE is produced anisotropically which currently imposes a geometrical constraint uncertainty for dose measurements. In this work, polarization imaging is proposed and described as a method enabling precise 2D dose measurements using CE.

Accurate dose measurements using Cherenkov emission polarization imaging.

Purpose: Cherenkov radiation carries the potential of direct in-water dose measurements, but its precision is currently limited by a strong anisotropy. Taking advantage of polarization imaging, this work proposes a new approach for high-accuracy Cherenkov emission dose measurements.

Methods: Cherenkov radiation produced in a 15 × 15 × 20-cm water tank is imaged with a cooled charge-coupled device (CCD) camera from four polarizer transmission axes [0, 45, 90, 135°].

Advantages of TRUS-based delineation for high-dose-rate prostate brachytherapy planning.

Purpose: To evaluate the variability of prostate contours delineated on computed tomography (CT) and transrectal ultrasound (TRUS).

Material And Methods: A TRUS-based high-dose-rate (HDR) brachytherapy procedure was introduced in 2016 in our center. The first thirty patients were additionally imaged with CT immediately after the treatment.

On the proper use of structural similarity for the robust evaluation of medical image synthesis models.

Purpose: To propose good practices for using the structural similarity metric (SSIM) and reporting its value. SSIM is one of the most popular image quality metrics in use in the medical image synthesis community because of its alleged superiority over voxel-by-voxel measurements like the average error or the peak signal noise ratio (PSNR). It has seen massive adoption since its introduction, but its limitations are often overlooked.

On the use of machine learning methods for mPSD calibration in HDR brachytherapy.

We sought to evaluate the feasibility of using machine learning (ML) algorithms for multipoint plastic scintillator detector (mPSD) calibration in high-dose-rate (HDR) brachytherapy. Dose measurements were conducted under HDR brachytherapy conditions. The dosimetry system consisted of an optimized 1-mm-core mPSD and a compact assembly of photomultiplier tubes coupled with dichroic mirrors and filters.

External validation of a hidden Markov model for gamma-based classification of anatomical changes in lung cancer patients using EPID dosimetry.

Purpose: To externally validate a hidden Markov model (HMM) for classifying gamma analysis results of in vivo electronic portal imaging device (EPID) measurements into different categories of anatomical change for lung cancer patients. Additionally, the relationship between HMM classification and deviations in dose-volume histogram (DVH) metrics was evaluated.

Methods: The HMM was developed at CHU de Québec (CHUQ), and trained on features extracted from gamma analysis maps of in vivo EPID measurements from 483 fractions (24 patients, treated with three-dimensional 3D-CRT or intensity modulated radiotherapy), using the EPID measurement of the first treatment fraction as reference.

Tomographic-based 3D scintillation dosimetry using a three-view plenoptic imaging system.

Purpose: To demonstrate the feasibility of a three-plenoptic camera projection, scintillation-based dosimetry system for measuring three-dimensional (3D) dose distributions of static photon radiation fields.

Methods: Static x-ray photon beams were delivered to a cubic plastic scintillator volume embedded within acrylic blocks. For each beam, three orthogonal projections of the scintillating light emission were recorded using a multifocus plenoptic camera.

Dosimetric performance of a multipoint plastic scintillator dosimeter as a tool for real-time source tracking in high dose rate Ir brachytherapy.

Purpose: This study aims to present the performance of a multipoint plastic scintillation detector (mPSD) as a tool for real-time dose measurements (covering three orders of magnitude in dose rate), source-position triangulation, and dwell time assessment in high dose rate (HDR) brachytherapy.

Methods: A previously characterized and optimized three-point sensor system was used for HDR brachytherapy measurements. The detector was composed of three scintillators: BCF60, BCF12, and BCF10.

Simulating imaging-based tomographic systems using optical design software for resolving 3D structures of translucent media.

Imaging-based tomography is emerging as the technique of choice for resolving 3D structures of translucent media, in particular for applications in external beam radiation therapy and combustion diagnostics. However, designing experimental prototypes is time-consuming and costly, and is carried out without the certainty of the imaging optics being optimal. In this paper, we present an optical-design-software-based method that enables end-to-end simulation imaging-based tomography systems.

Radiation Therapy-Induced Dysfunction in Cardiovascular Implantable Electronic Devices.

Purpose: The prevalence of patients with cardiovascular implantable electronic devices (CIEDs) who receive radiation treatment for cancer is increasing. External beam radiation therapy (RT) can affect the electronic components. This study aimed to evaluate the incidence and predictors of new onset CIED dysfunction in patients treated with RT.

Optimization of a multipoint plastic scintillator dosimeter for high dose rate brachytherapy.

Purpose: This study is devoted to optimizing and characterizing the response of a multipoint plastic scintillator detector (mPSD) for application to in vivo dosimetry in high dose rate (HDR) brachytherapy.

Methods: An exhaustive analysis was carried out in order to obtain an optimized mPSD design that maximizes the scintillation light collection produced by the interaction of ionizing photons. More than 20 prototypes of mPSD were built and tested in order to determine the appropriate order of scintillators relative to the photodetector (distal, center, or proximal) as well as their length as a function of the scintillation light emitted.

Stochastic frontier analysis as knowledge-based model to improve sparing of organs-at-risk for VMAT-treated prostate cancer.

Stochastic frontier analysis (SFA) is used as a novel knowledge-based technique in order to develop a predictive model of dosimetric features from significant geometric parameters describing a patient morphology. 406 patients treated with VMAT for prostate cancer were analyzed retrospectively. Cases were divided into three prescription-based groups.