AI Medical Compendium Journal:
Medical physics

Showing 41 to 50 of 732 articles

Breast radiotherapy planning: A decision-making framework using deep learning.

Medical physics
BACKGROUND: Effective breast cancer treatment planning requires balancing tumor control while minimizing radiation exposure to healthy tissues. Choosing between intensity-modulated radiation therapy (IMRT) and three-dimensional conformal radiation th...

Deep learning based super-resolution for CBCT dose reduction in radiotherapy.

Medical physics
BACKGROUND: Cone-beam computed tomography (CBCT) is a crucial daily imaging modality in image-guided and adaptive radiotherapy. However, the use of ionizing radiation in CBCT imaging increases the risk of secondary cancers, which is particularly conc...

Rapid in vivo EPID image prediction using a combination of analytically calculated attenuation and AI predicted scatter.

Medical physics
BACKGROUND: The electronic portal imaging device (EPID) can be used in vivo, to detect on-treatment errors by evaluating radiation exiting a patient. To detect deviations from the planning intent, image predictions need to be modeled based on the pat...

Strip and boundary detection multi-task learning network for segmentation of meibomian glands.

Medical physics
BACKGROUND: Automatic segmentation of meibomian glands in near-infrared meibography images is basis of morphological parameter analysis, which plays a crucial role in facilitating the diagnosis of meibomian gland dysfunction (MGD). The special strip ...

Cross-shaped windows transformer with self-supervised pretraining for clinically significant prostate cancer detection in bi-parametric MRI.

Medical physics
BACKGROUND: Bi-parametric magnetic resonance imaging (bpMRI) has demonstrated promising results in prostate cancer (PCa) detection. Vision transformers have achieved competitive performance compared to convolutional neural network (CNN) in deep learn...

CT ventilation images produced by a 3D neural network show improvement over the Jacobian and HU DIR-based methods to predict quantized lung function.

Medical physics
BACKGROUND: Radiation-induced pneumonitis affects up to 33% of non-small cell lung cancer (NSCLC) patients, with fatal pneumonitis occurring in 2% of patients. Pneumonitis risk is related to the dose and volume of lung irradiated. Clinical radiothera...

Generalizability of lesion detection and segmentation when ScaleNAS is trained on a large multi-organ dataset and validated in the liver.

Medical physics
BACKGROUND: Tumor assessment through imaging is crucial for diagnosing and treating cancer. Lesions in the liver, a common site for metastatic disease, are particularly challenging to accurately detect and segment. This labor-intensive task is subjec...

A hybrid network for fiber orientation distribution reconstruction employing multi-scale information.

Medical physics
BACKGROUND: Accurate fiber orientation distribution (FOD) is crucial for resolving complex neural fiber structures. However, existing reconstruction methods often fail to integrate both global and local FOD information, as well as the directional inf...

MMD-Net: Image domain multi-material decomposition network for dual-energy CT imaging.

Medical physics
BACKGROUND: Multi-material decomposition is an interesting topic in dual-energy CT (DECT) imaging; however, the accuracy and performance may be limited using the conventional algorithms.

4DCT image artifact detection using deep learning.

Medical physics
BACKGROUND: Four-dimensional computed tomography (4DCT) is an es sential tool in radiation therapy. However, the 4D acquisition process may cause motion artifacts which can obscure anatomy and distort functional measurements from CT scans.