The aim of this study is to develop a magnetic resonance imaging (MRI)-based treatment planning procedure for intracranial lesions. The method relies on (a) distortion correction of raw magnetic resonance (MR) images by using an adaptive thresholding and iterative technique, (b) autosegmentation of head structures relevant to dosimetric calculations (scalp, bone and brain) using an atlas-based software and (c) conversion of MR images into computed tomography (CT)-like images by assigning bulk CT values to organ contours and dose calculations performed in Eclipse (Philips Medical Systems). Standard CT + MRI-based and MRI-only plans were compared by means of isodose distributions, dose volume histograms and several dosimetric parameters. The plans were also ranked by using a tumor control probability (TCP)-based technique for heterogeneous irradiation, which is independent of radiobiological parameters. For our 3 T Intera MRI scanner (Philips Medical Systems), we determined that the total maximum image distortion corresponding to a typical brain study was about 4 mm. The CT + MRI and MRI-only plans were found to be in good agreement for all patients investigated. Following our clinical criteria, the TCP-based ranking tool shows no significant difference between the two types of plans. This indicates that the proposed MRI-based treatment planning procedure is suitable for the radiotherapy of intracranial lesions.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1088/0031-9155/53/13/013 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Task activation results in regional C-lactate signal increase in the human brain.
J Cereb Blood Flow Metab
January 2025
Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.
Hyperpolarized-C magnetic resonance imaging (HP-C MRI) was used to image changes in C-lactate signal during a visual stimulus condition in comparison to an eyes-closed control condition. Whole-brain C-pyruvate, C-lactate and C-bicarbonate production was imaged in healthy volunteers (N = 6, ages 24-33) for the two conditions using two separate hyperpolarized C-pyruvate injections. BOLD-fMRI scans were used to delineate regions of functional activation.
View Article and Find Full Text PDFEnhanced brain tumor detection and segmentation using densely connected convolutional networks with stacking ensemble learning.
Comput Biol Med
January 2025
Emerging Technologies Research Lab (ETRL), College of Computer Science and Information Systems, Najran University, Najran, 61441, Saudi Arabia; Department of Computer Science, College of Computer Science and Information Systems, Najran University, Najran, 61441, Saudi Arabia. Electronic address:
- Brain tumors (BT), both benign and malignant, pose a substantial impact on human health and need precise and early detection for successful treatment. Analysing magnetic resonance imaging (MRI) image is a common method for BT diagnosis and segmentation, yet misdiagnoses yield effective medical responses, impacting patient survival rates. Recent technological advancements have popularized deep learning-based medical image analysis, leveraging transfer learning to reuse pre-trained models for various applications.
View Article and Find Full Text PDFMetAssimulo 2.0: a web app for simulating realistic 1D & 2D Metabolomic 1H NMR spectra.
Bioinformatics
January 2025
Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, W12 0NN, United Kingdom.
Unlabelled: Metabolomics extensively utilizes Nuclear Magnetic Resonance (NMR) spectroscopy due to its excellent reproducibility and high throughput. Both one-dimensional (1D) and two-dimensional (2D) NMR spectra provide crucial information for metabolite annotation and quantification, yet present complex overlapping patterns which may require sophisticated machine learning algorithms to decipher. Unfortunately, the limited availability of labeled spectra can hamper application of machine learning, especially deep learning algorithms which require large amounts of labelled data.
View Article and Find Full Text PDFSuperior performance in classification of breast cancer molecular subtype and histological factors by radiomics based on ultrafast MRI over standard MRI: evidence from a prospective study.
Radiol Med
January 2025
Medical Science Research Center, Korea University College of Medicine, Seoul, Republic of Korea.
Purpose: To compare the performance of ultrafast MRI with standard MRI in classifying histological factors and subtypes of invasive breast cancer among radiologists with varying experience.
Methods: From October 2021 to November 2022, this prospective study enrolled 225 participants with 233 breast cancers before treatment (NCT06104189 at clinicaltrials.gov).
New physiological insights using multi-TE ASL MRI measuring blood-brain barrier water exchange after caffeine intake.
MAGMA
January 2025
Imaging Physics, Fraunhofer Institute for Digital Medicine MEVIS, Max-von-Laue-Straße 2, 28359, Bremen, Germany.
Objectives: Caffeine, a known neurostimulant and adenosine antagonist, affects brain physiology by decreasing cerebral blood flow. It interacts with adenosine receptors to induce vasoconstriction, potentially disrupting brain homeostasis. However, the impact of caffeine on blood-brain barrier (BBB) permeability to water remains underexplored.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!