Magnetic Resonance Imaging Simulation of the Skull Base, Oral Cavity, Pharynx, and Larynx: Advanced Imaging Protocol for a Radiation Oncology Center
West, N. A.; McCullum, L.; Cheruvelil, J. T.; Dinh, V. T.; Ding, Y.; Wang, J.; Shah, K.; Learned, K.; Chen, M.; Phan, J.; Lee, A.; Garden, A.; Rosenthal, D.; Fuller, C. D.
Show abstract
Head and neck radiation oncology, with its complex locality and epidemiology, requires various imaging techniques to achieve sufficient visualization. Through collaboration with medical physicists, radiation therapists, radiation oncologists, and neuroradiologists, our group clinically implemented a standard set of magnetic resonance imaging (MRI) sequences on an MR-simulation scanner for three anatomical regions: skull base, oral cavity, and larynx/pharynx. A total of 41 patients with head and neck cancer and 3 volunteers were scanned on either a 1.5T or 3T MR-simulation scanner. Sequences described include Dixon-based approaches, SPAIR-based approaches, short T2-based approaches, and quantitative imaging methods. We describe and outline the steps to optimize these notable MRI sequences to aid in successful implementation in a radiation oncology clinic.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Precise dose verification in proton therapy using Positron Emission Tomography. 95%
- Patient-derived PixelPrint phantoms for evaluating clinical imaging performance of a deep learning CT reconstruction algorithm 95%
- High precision fluorescence tomography-guided system for pre-clinical radiation research: system design and validation 94%
Similar papers in this journal
- Quality Assurance Assessment of Intra-Acquisition Diffusion-Weighted and T2-Weighted Magnetic Resonance Imaging Registration and Contour Propagation for Head and Neck Cancer Radiotherapy 94%
- Evaluation of inverse treatment planning for Gamma Knife radiosurgery using fMRI brain activation maps as organs at risk 93%
- Cell lines of the same anatomic site and histologic type show large variability in intrinsic radiosensitivity and relative biological effectiveness to protons and carbon ions 92%
Similar papers in this journal
- Comprehensive Quantitative Evaluation of Inter-observer Delineation Performance of MR-guided Delineation of Oropharyngeal Gross Tumor Volumes and High-risk Clinical Target Therapy: An R-IDEAL Stage 0 Prospective Study 96%
- Initial Feasibility and Clinical Implementation of Daily MR-guided Adaptive Head and Neck Cancer Radiotherapy on a 1.5T MR-Linac System: Prospective R-IDEAL 2a/2b Systematic Clinical Evaluation of Technical Innovation 95%
- Cluster-Based Toxicity Estimation of Osteoradionecrosis via Unsupervised Machine Learning: Moving Beyond Single Dose-Parameter Normal Tissue Complication Probability by Using Whole Dose-Volume Histograms for Cohort Risk Stratification 94%
Similar papers in this journal
- Morphological changes after cranial fractionated photon radiotherapy: localized loss of white matter and grey matter volume with increasing dose 94%
- Development of a High-Performance Multiparametric MRI Oropharyngeal Primary Tumor Auto-Segmentation Deep Learning Model and Investigation of Input Channel Effects: Results from a Prospective Imaging Registry 94%
- Detection of Alteration in Carotid Artery Volumetry Using Standard-of-care Computed Tomography Surveillance Scans Following Unilateral Radiation Therapy for Early-stage Tonsillar Squamous Cell Carcinoma Survivors: A Cross-Sectional Internally-Matched Carotid Isodose Analysis 93%
Similar papers in this journal
- Weekly Intra-Treatment Diffusion Weighted Imaging Dataset for Head and Neck Cancer Patients Undergoing MR-linac Treatment 96%
- Large-scale crowdsourced radiotherapy segmentations across a variety of cancer anatomic sites: Interobserver expert/non-expert and multi-observer composite tumor and normal tissue delineation annotations from a prospective educational challenge 95%
- Segmentation of vestibular schwannoma from MRI — An open annotated dataset and baseline algorithm 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.