Impact of deep learning model uncertainty on manual corrections to MRI-based auto-segmentation in prostate cancer radiotherapy.

Background: Deep learning (DL)-based organ segmentation is increasingly used in radiotherapy. While methods exist to generate voxel-wise uncertainty maps from DL-based auto-segmentation models, these maps are rarely presented to clinicians.

Purpose: This study aimed to evaluate the impact of DL-generated uncertainty maps on experienced radiation oncologists during the manual correction of DL-based auto-segmentation for prostate radiotherapy.

Harmonizing organ-at-risk structure names using open-source large language models.

Background And Purpose: Standardized radiotherapy structure nomenclature is crucial for automation, inter-institutional collaborations, and large-scale deep learning studies in radiation oncology. Despite the availability of nomenclature guidelines (AAPM-TG-263), their implementation is lacking and still faces challenges. This study evaluated open-source large language models (LLMs) for automated organ-at-risk (OAR) renaming on a multi-institutional and multilingual dataset.

SynthRAD2025 Grand Challenge dataset: Generating synthetic CTs for radiotherapy from head to abdomen.

Purpose: Medical imaging is crucial in modern radiotherapy, aiding diagnosis, treatment planning, and monitoring. The development of synthetic imaging techniques, particularly synthetic computed tomography (sCT), continues to attract interest in radiotherapy. The SynthRAD2025 dataset and the accompanying SynthRAD2025 Grand Challenge aim to stimulate advancements in synthetic CT generation algorithms by providing a platform for comprehensive evaluation and benchmarking of synthetic CT generation algorithms based on cone-beam CTs (CBCT) and magnetic resonance images (MRI).

TrackRAD2025 challenge dataset: real-time tumor tracking for MRI-guided radiotherapy.

Purpose: Magnetic resonance imaging (MRI) to visualize anatomical motion is becoming increasingly important when treating cancer patients with radiotherapy. Hybrid MRI-linear accelerator (MRI-linac) systems allow real-time motion management during irradiation. This paper presents a multi-institutional real-time MRI time series dataset from different MRI-linac vendors.

Effect of data-driven motion correction for respiratory movement on lesion detectability in PET-CT: a phantom study.

Purpose: While data-driven motion correction (DDMC) techniques have proven to enhance the visibility of lesions affected by motion, their impact on overall detectability remains unclear. This study investigates whether DDMC improves lesion detectability in PET-CT using FDG-18F.

Method: A moving platform simulated respiratory motion in a NEMA-IEC body phantom with varying amplitudes (0, 7, 10, 20, 30 mm) and target-to-background ratios (2, 5, 10.

Deep learning-based auto-contouring of organs/structures-at-risk for pediatric upper abdominal radiotherapy.

Purposes: This study aimed to develop a computed tomography (CT)-based multi-organ segmentation model for delineating organs-at-risk (OARs) in pediatric upper abdominal tumors and evaluate its robustness across multiple datasets.

Materials And Methods: In-house postoperative CTs from pediatric patients with renal tumors and neuroblastoma (n = 189) and a public dataset (n = 189) with CTs covering thoracoabdominal regions were used. Seventeen OARs were delineated: nine by clinicians (Type 1) and eight using TotalSegmentator (Type 2).

Validation of SynthSeg segmentation performance on CT using paired MRI from radiotherapy patients.

Introduction: Manual segmentation of medical images is labor intensive and especially challenging for images with poor contrast or resolution. The presence of disease exacerbates this further, increasing the need for an automated solution. To this extent, SynthSeg is a robust deep learning model designed for automatic brain segmentation across various contrasts and resolutions.

Automatic segmentation for magnetic resonance imaging guided individual elective lymph node irradiation in head and neck cancer patients.

Background And Purpose: In head and neck squamous cell carcinoma (HNSCC) patients, the radiation dose to nearby organs at risk can be reduced by restricting elective neck irradiation from lymph node levels to individual lymph nodes. However, manual delineation of every individual lymph node is time-consuming and error prone. Therefore, automatic magnetic resonance imaging (MRI) segmentation of individual lymph nodes was developed and tested using a convolutional neural network (CNN).

Assessment of Chemical and Biological Fungicides for the Control of Causing Wood Necrosis in Hazelnut.

Fungal trunk disease (FTD) poses a significant threat to hazelnut ( L.) production worldwide. In Chile, the fungus , from the Botryosphaeriaceae family, has been frequently identified causing this disease in the Maule and Ñuble Regions.

Generating synthetic computed tomography for radiotherapy: SynthRAD2023 challenge report.

Radiation therapy plays a crucial role in cancer treatment, necessitating precise delivery of radiation to tumors while sparing healthy tissues over multiple days. Computed tomography (CT) is integral for treatment planning, offering electron density data crucial for accurate dose calculations. However, accurately representing patient anatomy is challenging, especially in adaptive radiotherapy, where CT is not acquired daily.

Deep-learning-based joint rigid and deformable contour propagation for magnetic resonance imaging-guided prostate radiotherapy.

Background: Deep learning-based unsupervised image registration has recently been proposed, promising fast registration. However, it has yet to be adopted in the online adaptive magnetic resonance imaging-guided radiotherapy (MRgRT) workflow.

Purpose: In this paper, we design an unsupervised, joint rigid, and deformable registration framework for contour propagation in MRgRT of prostate cancer.

Generalizable synthetic MRI with physics-informed convolutional networks.

Background: Magnetic resonance imaging (MRI) provides state-of-the-art image quality for neuroimaging, consisting of multiple separately acquired contrasts. Synthetic MRI aims to accelerate examinations by synthesizing any desirable contrast from a single acquisition.

Purpose: We developed a physics-informed deep learning-based method to synthesize multiple brain MRI contrasts from a single 5-min acquisition and investigate its ability to generalize to arbitrary contrasts.

Accelerated respiratory-resolved 4D-MRI with separable spatio-temporal neural networks.

Background: Respiratory-resolved four-dimensional magnetic resonance imaging (4D-MRI) provides essential motion information for accurate radiation treatments of mobile tumors. However, obtaining high-quality 4D-MRI suffers from long acquisition and reconstruction times.

Purpose: To develop a deep learning architecture to quickly acquire and reconstruct high-quality 4D-MRI, enabling accurate motion quantification for MRI-guided radiotherapy (MRIgRT).

Exploring contrast generalisation in deep learning-based brain MRI-to-CT synthesis.

Background: Synthetic computed tomography (sCT) has been proposed and increasingly clinically adopted to enable magnetic resonance imaging (MRI)-based radiotherapy. Deep learning (DL) has recently demonstrated the ability to generate accurate sCT from fixed MRI acquisitions. However, MRI protocols may change over time or differ between centres resulting in low-quality sCT due to poor model generalisation.

SynthRAD2023 Grand Challenge dataset: Generating synthetic CT for radiotherapy.

Purpose: Medical imaging has become increasingly important in diagnosing and treating oncological patients, particularly in radiotherapy. Recent advances in synthetic computed tomography (sCT) generation have increased interest in public challenges to provide data and evaluation metrics for comparing different approaches openly. This paper describes a dataset of brain and pelvis computed tomography (CT) images with rigidly registered cone-beam CT (CBCT) and magnetic resonance imaging (MRI) images to facilitate the development and evaluation of sCT generation for radiotherapy planning.

Dosimetric feasibility of direct post-operative MR-Linac-based stereotactic radiosurgery for resection cavities of brain metastases.

Background: Post-operative radiosurgery (SRS) of brain metastases patients is typically planned on a post-recovery MRI, 2-4 weeks after resection. However, the intracranial metastasis may (re-)grow in this period. Planning SRS directly on the post-operative MRI enables shortening this time interval, anticipating the start of adjuvant systemic therapy, and so decreasing the chance of extracranial progression.

⊥-loss: A symmetric loss function for magnetic resonance imaging reconstruction and image registration with deep learning.

Convolutional neural networks (CNNs) are increasingly adopted in medical imaging, e.g., to reconstruct high-quality images from undersampled magnetic resonance imaging (MRI) acquisitions or estimate subject motion during an examination.

Real-time 3D motion estimation from undersampled MRI using multi-resolution neural networks.

Purpose: To enable real-time adaptive magnetic resonance imaging-guided radiotherapy (MRIgRT) by obtaining time-resolved three-dimensional (3D) deformation vector fields (DVFs) with high spatiotemporal resolution and low latency (  ms). Theory and Methods: Respiratory-resolved -weighted 4D-MRI of 27 patients with lung cancer were acquired using a golden-angle radial stack-of-stars readout. A multiresolution convolutional neural network (CNN) called TEMPEST was trained on up to 32 retrospectively undersampled MRI of 17 patients, reconstructed with a nonuniform fast Fourier transform, to learn optical flow DVFs.

Influence of eye movement on lens dose and optic nerve target coverage during craniospinal irradiation.

Purpose: Optic nerves are part of the craniospinal irradiation (CSI) target volume. Modern radiotherapy techniques achieve highly conformal target doses while avoiding organs-at-risk such as the lens. The magnitude of eye movement and its influence on CSI target- and avoidance volumes are unclear.

Deep learning based synthetic-CT generation in radiotherapy and PET: A review.

Recently,deep learning (DL)-based methods for the generation of synthetic computed tomography (sCT) have received significant research attention as an alternative to classical ones. We present here a systematic review of these methods by grouping them into three categories, according to their clinical applications: (i) to replace computed tomography in magnetic resonance (MR) based treatment planning, (ii) facilitate cone-beam computed tomography based image-guided adaptive radiotherapy, and (iii) derive attenuation maps for the correction of positron emission tomography. Appropriate database searching was performed on journal articles published between January 2014 and December 2020.

Optimal Conditions for Diapause Survival of , an Egg Parasitoid for Biological Control of .

is a specialist egg parasitoid of the citrus longhorned beetle , a high-risk invasive pest of hardwood trees. The parasitoid overwinters as diapausing mature larvae within the host egg and emerges in early summer in synchrony with the egg-laying peak of . This study investigated the parasitoid's diapause survival in parasitized host eggs that either remained in potted trees under semi-natural conditions in southern France or were removed from the wood and held at four different humidities (44, 75, 85-93 and 100% RH) at 11 °C or four different temperature regimes (2, 5, 10 and 12.

A single neural network for cone-beam computed tomography-based radiotherapy of head-and-neck, lung and breast cancer.

Adaptive radiotherapy based on cone-beam computed tomography (CBCT) requires high CT number accuracy to ensure accurate dose calculations. Recently, deep learning has been proposed for fast CBCT artefact corrections on single anatomical sites. This study investigated the feasibility of applying a single convolutional network to facilitate dose calculation based on CBCT for head-and-neck, lung and breast cancer patients.

Deep learning-based synthetic CT generation for paediatric brain MR-only photon and proton radiotherapy.

Background And Purpose: To enable accurate magnetic resonance imaging (MRI)-based dose calculations, synthetic computed tomography (sCT) images need to be generated. We aim at assessing the feasibility of dose calculations from MRI acquired with a heterogeneous set of imaging protocol for paediatric patients affected by brain tumours.

Materials And Methods: Sixty paediatric patients undergoing brain radiotherapy were included.

Deep learning-based image reconstruction and motion estimation from undersampled radial k-space for real-time MRI-guided radiotherapy.

To enable magnetic resonance imaging (MRI)-guided radiotherapy with real-time adaptation, motion must be quickly estimated with low latency. The motion estimate is used to adapt the radiation beam to the current anatomy, yielding a more conformal dose distribution. As the MR acquisition is the largest component of latency, deep learning (DL) may reduce the total latency by enabling much higher undersampling factors compared to conventional reconstruction and motion estimation methods.

Clinical implementation of MRI-based organs-at-risk auto-segmentation with convolutional networks for prostate radiotherapy.

Background: Structure delineation is a necessary, yet time-consuming manual procedure in radiotherapy. Recently, convolutional neural networks have been proposed to speed-up and automatise this procedure, obtaining promising results. With the advent of magnetic resonance imaging (MRI)-guided radiotherapy, MR-based segmentation is becoming increasingly relevant.