Purpose: To determine the prevalence of a longitudinal "pseudoseptum" on T2-weighted MR images within the cervices of women who do not have a uterine anomaly.
Materials And Methods: We reviewed 317 consecutive female pelvic MR examinations performed at a single institution over a four-month period. All examinations included T2-weighted sequences in at least two orthogonal planes. Of the 317 studies, 57 were excluded due to prior radical hysterectomy. Axial and coronal T2-weighted images of the remaining 260 examinations were evaluated for the presence of a longitudinal low T2 signal intensity structure within the endocervical lumen that mimicked the appearance of a septum. Interpretations were performed independently by two MR radiologists and kappa analysis of interobserver agreement was performed.
Results: In 50 (19%) of the 260 women, both readers noted the presence of a pseudoseptum on at least one imaging plane. In 162 (62%), neither reader noted a pseudoseptum. Overall, there was 81% agreement between the readers. Kappa analysis yielded a value of 0.55, indicating a moderate degree of interobserver agreement beyond chance.
Conclusion: A pseudoseptum was depicted in 20% to 30% of women's cervices on T2-weighted imaging. We hypothesize that chance long-axis depiction of the endocervical folds can mimic a cervical septum. The presence of a pseudoseptum on MRI should be considered a normal finding and not a feature of a developmental anomaly of the uterus or cervix.
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http://dx.doi.org/10.1002/jmri.21116 | DOI Listing |
Insights Imaging
January 2025
Institute of Diagnostic and Interventional Radiology, University Hospital Zurich (USZ), Zurich, Switzerland.
Objectives: To determine whether deep learning-based reconstructions of zero-echo-time (ZTE-DL) sequences enhance image quality and bone visualization in cervical spine MRI compared to traditional zero-echo-time (ZTE) techniques, and to assess the added value of ZTE-DL sequences alongside standard cervical spine MRI for comprehensive pathology evaluation.
Methods: In this retrospective study, 52 patients underwent cervical spine MRI using ZTE, ZTE-DL, and T2-weighted 3D sequences on a 1.5-Tesla scanner.
Int J Gynecol Cancer
January 2025
Institute of Image-Guided Surgery, IHU Strasbourg, France; University of Strasbourg, ICube, Laboratory of Engineering, Computer Science and Imaging, Department of Robotics, Imaging, Teledetection and Healthcare Technologies, CNRS, UMR, Strasbourg, France.
Objective: Evaluation of prognostic factors is crucial in patients with endometrial cancer for optimal treatment planning and prognosis assessment. This study proposes a deep learning pipeline for tumor and uterus segmentation from magnetic resonance imaging (MRI) images to predict deep myometrial invasion and cervical stroma invasion and thus assist clinicians in pre-operative workups.
Methods: Two experts consensually reviewed the MRIs and assessed myometrial invasion and cervical stromal invasion as per the International Federation of Gynecology and Obstetrics staging classification, to compare the diagnostic performance of the model with the radiologic consensus.
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.
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