AI Medical Compendium Journal:
Medical physics

Showing 91 to 100 of 732 articles

Computer-aided diagnosis of cystic lung diseases using CT scans and deep learning.

Medical physics
BACKGROUND: Auxiliary diagnosis of different types of cystic lung diseases (CLDs) is important in the clinic and is instrumental in facilitating early and specific treatments. Current clinical methods heavily depend on accumulated experience, restric...

Multi-modal segmentation with missing image data for automatic delineation of gross tumor volumes in head and neck cancers.

Medical physics
BACKGROUND: Head and neck (HN) gross tumor volume (GTV) auto-segmentation is challenging due to the morphological complexity and low image contrast of targets. Multi-modality images, including computed tomography (CT) and positron emission tomography...

Image-domain material decomposition for dual-energy CT using unsupervised learning with data-fidelity loss.

Medical physics
BACKGROUND: Dual-energy computed tomography (DECT) and material decomposition play vital roles in quantitative medical imaging. However, the decomposition process may suffer from significant noise amplification, leading to severely degraded image sig...

Semi-supervised learning framework with shape encoding for neonatal ventricular segmentation from 3D ultrasound.

Medical physics
BACKGROUND: Three-dimensional (3D) ultrasound (US) imaging has shown promise in non-invasive monitoring of changes in the lateral brain ventricles of neonates suffering from intraventricular hemorrhaging. Due to the poorly defined anatomical boundari...

Deep learning-based low-dose CT simulator for non-linear reconstruction methods.

Medical physics
BACKGROUND: Computer algorithms that simulate lower-doses computed tomography (CT) images from clinical-dose images are widely available. However, most operate in the projection domain and assume access to the reconstruction method. Access to commerc...

Neural network dose prediction for cervical brachytherapy: Overcoming data scarcity for applicator-specific models.

Medical physics
BACKGROUND: 3D neural network dose predictions are useful for automating brachytherapy (BT) treatment planning for cervical cancer. Cervical BT can be delivered with numerous applicators, which necessitates developing models that generalize to multip...

Improved brain metastases segmentation using generative adversarial network and conditional random field optimization mask R-CNN.

Medical physics
BACKGROUND: In radiotherapy, the delineation of the gross tumor volume (GTV) in brain metastases using computed tomography (CT) simulation localization is very important. However, despite the criticality of this process, a pronounced gap exists in th...

Enhancing voxel-based dosimetry accuracy with an unsupervised deep learning approach for hybrid medical image registration.

Medical physics
BACKGROUND: Deformable registration is required to generate a time-integrated activity (TIA) map which is essential for voxel-based dosimetry. The conventional iterative registration algorithm using anatomical images (e.g., computed tomography (CT)) ...

A machine learning-based approach to predict energy layer for each field in spot-scanning proton arc therapy for lung cancer: A feasibility study.

Medical physics
BACKGROUND: Determining the optimal energy layer (EL) for each field, under considering both dose constraints and delivery efficiency, is crucial to promoting the development of proton arc therapy (PAT) technology.