The purpose of the study was to prospectively evaluate a whole-body magnetic resonance (MR) imaging protocol to help depict metastases by using unenhanced T2-weighted and contrast material-enhanced T1-weighted real-time sequences during continuous table movement. The study was conducted after approval of the local institutional review board and written informed consent were obtained. In 11 patients with positron emission tomographic (PET) scans positive for tumors and known metastases, whole-body MR imaging, including T2- and T1-weighted sequences, was performed before and after contrast material administration. A high-precision laser position sensor was used to register the table position for off-line multiplanar reformations of the acquired transverse whole-body data sets. Seventy-three of 75 metastases detected by using PET/computed tomography were correctly diagnosed by using MR imaging. Metastases with a diameter exceeding 5 mm could be visualized in all anatomic regions.
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http://dx.doi.org/10.1148/radiol.2463062017 | DOI Listing |
J Imaging Inform Med
January 2025
Department of Radiology, Peking University People's Hospital, 11 Xizhimen Nandajie, Xicheng District, Beijing, 100044, P. R. China.
This study aims to develop an end-to-end deep learning (DL) model to predict neoadjuvant chemotherapy (NACT) response in osteosarcoma (OS) patients using routine magnetic resonance imaging (MRI). We retrospectively analyzed data from 112 patients with histologically confirmed OS who underwent NACT prior to surgery. Multi-sequence MRI data (including T2-weighted and contrast-enhanced T1-weighted images) and physician annotations were utilized to construct an end-to-end DL model.
View Article and Find Full Text PDFSci Rep
January 2025
Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai, China.
Prediction of isocitrate dehydrogenase (IDH) mutation status and epilepsy occurrence are important to glioma patients. Although machine learning models have been constructed for both issues, the correlation between them has not been explored. Our study aimed to exploit this correlation to improve the performance of both of the IDH mutation status identification and epilepsy diagnosis models in patients with glioma II-IV.
View Article and Find Full Text PDFNMR Biomed
March 2025
Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
In clinical practice, particularly in neurology assessments, imaging multiparametric MR images with a single-sequence scan is often limited by either insufficient imaging contrast or the constraints of accelerated imaging techniques. A novel single scan 3D imaging method, incorporating Wave-CAIPI and MULTIPLEX technologies and named WAMP, has been developed for rapid and comprehensive parametric imaging in clinical diagnostic applications. Featuring a hybrid design that includes wave encoding, the CAIPIRINHA sampling pattern, dual time of repetition (TR), dual flip angle (FA), multiecho, and optional flow modulation, the WAMP method captures information on RF B1t fields, proton density (PD), T1, susceptibility, and blood flow.
View Article and Find Full Text PDFFront Oncol
January 2025
The Second Clinical Medicine College, Jinan University, Shenzhen, China.
Introduction: Endolymphatic sac tumor (ELST) is a rare neoplasm that exhibits aggressive growth primarily in the endolymphatic capsule and can potentially affect nearby neurovascular structures. The diagnosis of ELST poses challenges due to its low prevalence, gradual progression, and nonspecific symptomatology. It is currently believed that prompt surgical intervention is recommended for endolymphatic sac tumors upon diagnosis.
View Article and Find Full Text PDFJ Magn Reson Imaging
January 2025
Department of Radiology, Peking University Third Hospital, Beijing, China.
Background: The spinal column is a frequent site for metastases, affecting over 30% of solid tumor patients. Identifying the primary tumor is essential for guiding clinical decisions but often requires resource-intensive diagnostics.
Purpose: To develop and validate artificial intelligence (AI) models using noncontrast MRI to identify primary sites of spinal metastases, aiming to enhance diagnostic efficiency.
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