Publications by authors named "Maziar Irannejad"
Article Synopsis
- The study explores changing network layers in dose distribution prediction for prostate cancer treatment, rather than just expanding dimensions, to make calculations simpler and more accurate.
- A total of 137 patients were split into training and testing groups, using methods like mean absolute error (MAE) and structural similarity index to evaluate prediction accuracy.
- Results showed that the nested UNet architecture significantly reduced MAE in dose predictions and improved geometric similarity over the standard UNet, making it a more suitable option for accurate predictions.
Background: Considering the invasiveness of the biopsy method, we attempted to evaluate the ability of the gamma distribution model using magnetic resonance imaging images to stage and grade benign and malignant brain tumors.
Methods: A total of 42 patients with malignant brain tumors (including glioma, lymphoma, and choroid plexus papilloma) and 24 patients with benign brain tumors (meningioma) underwent diffusion-weighted imaging using five b-values ranging from 0 to 2000 s/mm2 with a 1.5 T scanner.
Purpose: The aim of this study is to reduce treatment planning time by predicting the intensity-modulated radiotherapy 3D dose distribution using deep learning for brain cancer patients. "For this purpose, two different approaches in dose prediction, i.e.
View Article and Find Full Text PDFBackground: Susceptibility-weighted imaging (SWI) is efficient in detecting multiple sclerosis (MS) plaques and evaluating the level of disease activity.
Purpose: To automatically detect active and inactive MS plaques in SWI images using a Bayesian approach.
Material And Methods: A 1.