ConfLUNet: Multiple sclerosis lesion instance segmentation in presence of confluent lesions.

Accurate lesion-level segmentation on MRI is critical for multiple sclerosis (MS) diagnosis, prognosis, and disease monitoring. However, current evaluation practices largely rely on semantic segmentation post-processed with connected components (CC), which cannot separate confluent lesions (aggregates of confluent lesion units, CLUs) due to reliance on spatial connectivity. To address this misalignment with clinical needs, we introduce formal definitions of CLUs and associated CLU-aware detection metrics, and include them in an exhaustive instance segmentation evaluation framework.

Leveraging Swin Transformer for enhanced diagnosis of Alzheimer's disease using multi-shell diffusion MRI.

Objective: This study aims to support early diagnosis of Alzheimer's disease and detection of amyloid accumulation by leveraging the microstructural information available in multi-shell diffusion MRI (dMRI) data, using a vision transformer-based deep learning framework.

Methods: We present a classification pipeline that employs the Swin Transformer, a hierarchical vision transformer model, on multi-shell dMRI data for the classification of Alzheimer's disease and amyloid presence. Key metrics from DTI and NODDI were extracted and projected onto 2D planes to enable transfer learning with ImageNet-pretrained models.

Optimal Fractionation Scheduling for Radiotherapy Treatments with Reinforcement Learning, Tumor Growth Modeling and Outcome Modeling.

Radiotherapy is a primary method for cancer treatment, wherein radiation doses are divided into multiple sessions or fractions to effectively target tumors and minimize damage to surrounding tissues. In this study, we leverage reinforcement learning (RL) to enhance treatment planning with the aim of improving the adaptability and robustness of RL agents given the inherent inaccuracies in tumor growth models. A 2D simulation model of tumor growth is employed, where tabular RL techniques are used to determine the optimal treatment strategies.

Advanced Analysis of OCT/OCTA Images for Accurately Differentiating Between Glaucoma and Healthy Eyes Using Deep Learning Techniques.

Purpose: To evaluate the discriminative power of optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA) images, identifying the best image combination for differentiating glaucoma from healthy eyes using deep learning (DL) with a convolutional neural network (CNN).

Methods: This cross-sectional study included 157 subjects contributing 1,106 eye scans. We used en-face images of the superficial and choroid layers for OCTA-based vessel density and OCT-based structural thickness of the macula (M) and optic disc (D).

Fast multi-compartment Microstructure Fingerprinting in brain white matter.

We proposed two deep neural network based methods to accelerate the estimation of microstructural features of crossing fascicles in the white matter. Both methods focus on the acceleration of a multi-dictionary matching problem, which is at the heart of Microstructure Fingerprinting, an extension of Magnetic Resonance Fingerprinting to diffusion MRI. The first acceleration method uses efficient sparse optimization and a dedicated feed-forward neural network to circumvent the inherent combinatorial complexity of the fingerprinting estimation.

Sparse Blind Spherical Deconvolution of diffusion weighted MRI.

Diffusion-weighted magnetic resonance imaging provides invaluable insights into neurological pathways. However, accurate and robust characterization of white matter fibers microstructure remains challenging. Widely used spherical deconvolution algorithms retrieve the fiber Orientation Distribution Function (ODF) by using an estimation of a response function, i.

Locus coeruleus features are linked to vagus nerve stimulation response in drug-resistant epilepsy.

The locus coeruleus-norepinephrine system is thought to be involved in the clinical effects of vagus nerve stimulation. This system is known to prevent seizure development and induce long-term plastic changes, particularly with the release of norepinephrine in the hippocampus. However, the requisites to become responder to the therapy and the mechanisms of action are still under investigation.

Baby HABIT-ILE intervention: study protocol of a randomised controlled trial in infants aged 6-18 months with unilateral cerebral palsy.

Introduction: Research using animal models suggests that intensive motor skill training in infants under 2 years old with cerebral palsy (CP) may significantly reduce, or even prevent, maladaptive neuroplastic changes following brain injury. However, the effects of such interventions to tentatively prevent secondary neurological damages have never been assessed in infants with CP. This study aims to determine the effect of the baby Hand and Arm Bimanual Intensive Therapy Including Lower Extremities (baby HABIT-ILE) in infants with unilateral CP, compared with a control intervention.

Unraveling multi-fixel microstructure with tractography and angular weighting.

Recent advances in MRI technology have enabled richer multi-shell sequences to be implemented in diffusion MRI, allowing the investigation of both the microscopic and macroscopic organization of the brain white matter and its complex network of neural fibers. The emergence of advanced diffusion models has enabled a more detailed analysis of brain microstructure by estimating the signal received from a voxel as the combination of responses from multiple fiber populations. However, disentangling the individual microstructural properties of different macroscopic white matter tracts where those pathways intersect remains a challenge.

Patient-specific three-dimensional image reconstruction from a single X-ray projection using a convolutional neural network for on-line radiotherapy applications.

Radiotherapy is commonly chosen to treat thoracic and abdominal cancers. However, irradiating mobile tumors accurately is extremely complex due to the organs' breathing-related movements. Different methods have been studied and developed to treat mobile tumors properly.

Hand-Arm Bimanual Intensive Therapy Including Lower Extremities (HABIT-ILE) in adults with chronic stroke: protocol of a randomised controlled trial.

Introduction: Stroke causes multiple deficits including motor, sensitive and cognitive impairments, affecting also individual's social participation and independence in activities of daily living (ADL) impacting their quality of life. It has been widely recommended to use goal-oriented interventions with a high amount of task-specific repetitions. These interventions are generally focused only on the upper or lower extremities separately, despite the impairments are observed at the whole-body level and ADL are both frequently bimanual and may require moving around.

BMAT: An open-source BIDS managing and analysis tool.

Magnetic Resonance Imaging (MRI) is an established technique to study in vivo neurological disorders such as Multiple Sclerosis (MS). To avoid errors on MRI data organization and automated processing, a standard called Brain Imaging Data Structure (BIDS) has been recently proposed. The BIDS standard eases data sharing and processing within or between centers by providing guidelines for their description and organization.

Real-time image-guided treatment of mobile tumors in proton therapy by a library of treatment plans: a simulation study.

Purpose: To improve target coverage and reduce the dose in the surrounding organs-at-risks (OARs), we developed an image-guided treatment method based on a precomputed library of treatment plans controlled and delivered in real-time.

Methods: A library of treatment plans is constructed by optimizing a plan for each breathing phase of a four dimensional computed tomography (4DCT). Treatments are delivered by simulation on a continuous sequence of synthetic computed tomographies (CTs) generated from real magnetic resonance imaging (MRI) sequences.

Artificial intelligence and thyroid disease management: considerations for thyroid function tests.

Artificial intelligence (AI) is transforming healthcare and offers new tools in clinical research, personalized medicine, and medical diagnostics. Thyroid function tests represent an important asset for physicians in the diagnosis and monitoring of pathologies. Artificial intelligence tools can clearly assist physicians and specialists in laboratory medicine to optimize test prescription, tests interpretation, decision making, process optimization, and assay design.

Artificial intelligence and machine learning for medical imaging: A technology review.

Artificial intelligence (AI) has recently become a very popular buzzword, as a consequence of disruptive technical advances and impressive experimental results, notably in the field of image analysis and processing. In medicine, specialties where images are central, like radiology, pathology or oncology, have seized the opportunity and considerable efforts in research and development have been deployed to transfer the potential of AI to clinical applications. With AI becoming a more mainstream tool for typical medical imaging analysis tasks, such as diagnosis, segmentation, or classification, the key for a safe and efficient use of clinical AI applications relies, in part, on informed practitioners.

Improved 3D U-Net robustness against JPEG 2000 compression for male pelvic organ segmentation in radiotherapy.

Automation of organ segmentation, via convolutional neural networks (CNNs), is key to facilitate the work of medical practitioners by ensuring that the adequate radiation dose is delivered to the target area while avoiding harmful exposure of healthy organs. The issue with CNNs is that they require large amounts of data transfer and storage which makes the use of image compression a necessity. Compression will affect image quality which in turn affects the segmentation process.

Reinforcement Learning for Radiotherapy Dose Fractioning Automation.

External beam radiotherapy cancer treatment aims to deliver dose fractions to slowly destroy a tumor while avoiding severe side effects in surrounding healthy tissues. To automate the dose fraction schedules, this paper investigates how deep reinforcement learning approaches (based on deep Q network and deep deterministic policy gradient) can learn from a model of a mixture of tumor and healthy cells. A 2D tumor growth simulation is used to simulate radiation effects on tissues and thus training an agent to automatically optimize dose fractionation.

Domain adversarial networks and intensity-based data augmentation for male pelvic organ segmentation in cone beam CT.

In radiation therapy, a CT image is used to manually delineate the organs and plan the treatment. During the treatment, a cone beam CT (CBCT) is often acquired to monitor the anatomical modifications. For this purpose, automatic organ segmentation on CBCT is a crucial step.

openPR - A computational tool for CT conversion assessment with proton radiography.

Purpose: One of the main sources of uncertainty in proton therapy is the conversion of the Hounsfield Units of the planning CT to (relative) proton stopping powers. Proton radiography provides range error maps but these can be affected by other sources of errors as well as the CT conversion (e.g.

Continuous real time 3D motion reproduction using dynamic MRI and precomputed 4DCT deformation fields.

Radiotherapy of mobile tumors requires specific imaging tools and models to reduce the impact of motion on the treatment. Online continuous nonionizing imaging has become possible with the recent development of magnetic resonance imaging devices combined with linear accelerators. This opens the way to new guided treatment methods based on the real-time tracking of anatomical motion.

Sparse deconvolution of proton radiography data to estimate water equivalent thickness maps.

Purpose: In proton therapy, the conversion of the planning computed tomography (CT) into proton stopping powers is tainted by uncertainties which may jeopardize dose conformity. Proton radiography provides a direct information on the energy reduction of protons in the patient. However, it is currently limited by the degradation ("blurring") of the one-dimensional depth-dose deposition profiles which constitute the pixels.