Publications by authors named "Nicolette O' Connell"
Article Synopsis
- This study focuses on improving the delivery of cancer treatment by evaluating autosegmentation methods that outline key organs at risk (OARs) in head and neck cancer patients using low-resolution MRIs from a specific machine known as the MR-linac.
- Researchers investigated 20 autosegmentation approaches, including both population-based methods and deep learning techniques, comparing their effectiveness in accurately identifying OARs against established ground truth contours.
- Results showed varying performance across methods, with additional dosimetric analysis performed on the best and worst methods, highlighting the importance of accurate dose reconstruction for effective patient treatment.
Article Synopsis
- Adequate image registration of MRI scans is crucial in MR-guided adaptive radiotherapy for head and neck cancer, but geometric distortions pose a significant challenge.
- * This study systematically evaluated multiple deformable image registration (DIR) methods, comparing commercial and open-source techniques, to align diffusion-weighted imaging (DWI) and T2-weighted (T2W) MRI images from the same session in 20 HNC patients.
- * Results showed that ADMIRE and Elastix 23 methods outperformed others, significantly enhancing alignment accuracy for radiotherapy structures compared to non-registered images, with ADMIRE being notably faster and more effective.
Background: Quick magnetic resonance imaging (MRI) scans with low contrast-to-noise ratio are typically acquired for daily MRI-guided radiotherapy setup. However, for patients with head and neck (HN) cancer, these images are often insufficient for discriminating target volumes and organs at risk (OARs). In this study, we investigated a deep learning (DL) approach to generate high-quality synthetic images from low-quality images.
View Article and Find Full Text PDFBackground: Advanced clinical applications, such as dose accumulation and adaptive radiation therapy, require deformable image registration (DIR) algorithms capable of voxel-wise accurate mapping of treatment dose or functional imaging. By utilizing a multistage deformable phantom, the authors investigated scenarios where biomechanical refinement method (BM-DIR) may be better than the pure image intensity based deformable registration (IM-DIR).
Methods: The authors developed a biomechanical-model based DIR refinement method (BM-DIR) to refine the deformable vector field (DVF) from any initial intensity-based DIR (IM-DIR).
Purpose: The purpose of this study was to investigate the clinical-relevant discrepancy between doses warped by pure image based deformable image registration (IM-DIR) and by biomechanical model based DIR (BM-DIR) on intensity-homogeneous organs.
Methods And Materials: Ten patients (5Head&Neck, 5Prostate) were included. A research DIR tool (ADMRIE_v1.
Background And Purpose: To investigate potential associations between dose to heart (sub)structures and non-cancer death, in early stage non-small cell lung cancer (NSCLC) patients treated with stereotactic body radiation therapy (SBRT).
Methods: 803 patients with early stage NSCLC received SBRT with predominant schedules of 3×18Gy (59%) or 4×12Gy (19%). All patients were registered to an average anatomy, their planned dose deformed accordingly, and dosimetric parameters for heart substructures were obtained.