Purpose: To prospectively evaluate use of diffusion-weighted (DW) magnetic resonance (MR) images and apparent diffusion coefficient (ADC) maps for determination of the consistency of macroadenomas.
Materials And Methods: The study protocol was approved by the institutional ethics committee, and informed consent was obtained from all patients. Twenty-two patients with pituitary macroadenoma (10 men, 12 women; mean age, 54 years +/- 17.09 [standard deviation]; range, 21-75 years) were examined. All patients underwent MR examination, which included T1-weighted spin-echo and T2-weighted turbo spin-echo DW imaging with ADC mapping and contrast material-enhanced T1-weighted spin-echo imaging. Regions of interest (ROIs) were drawn in the macroadenomas and in normal white matter on DW images, ADC maps, and conventional MR images. Consistency of macroadenomas was evaluated at surgery and was classified as soft, intermediate, or hard. Histologic examination was performed on surgical specimens of macroadenomas. Mean ADC values, signal intensity (SI) ratios of tumor to white matter within ROIs on conventional and DW MR images, and degree of enhancement were compared with tumor consistency and with percentage of collagen content at histologic examination by using analysis of variance for linear trend.
Results: The mean value of ADC in the soft group was (0.663 +/- 0.109) x 10(-3) mm(2)/sec; in the intermediate group, (0.842 +/- 0.081) x 10(-3) mm(2)/sec; and in the hard group, (1.363 +/- 0.259) x 10(-3) mm(2)/sec. Statistical analysis revealed a significant correlation between tumor consistency and ADC values, DW image SI ratios, T2-weighted image SI ratios, and percentage of collagen content (P < .001, analysis of variance). No other statistically significant correlations were found.
Conclusion: Findings in this study suggest that DW MR images with ADC maps can provide information about the consistency of macroadenomas.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1148/radiol.2383042204 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Motion-Compensated Multishot Pancreatic Diffusion-Weighted Imaging With Deep Learning-Based Denoising.
Invest Radiol
January 2025
From the Department of Radiology, Stanford University, Stanford, CA (K.W., M.J.M., A.M.L., A.B.S., A.J.H., D.B.E., R.L.B.); Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA (K.W.); GE HealthCare, Houston, TX (X.W.); GE HealthCare, Boston, MA (A.G.); and GE HealthCare, Menlo Park, CA (P.L.).
Objectives: Pancreatic diffusion-weighted imaging (DWI) has numerous clinical applications, but conventional single-shot methods suffer from off resonance-induced artifacts like distortion and blurring while cardiovascular motion-induced phase inconsistency leads to quantitative errors and signal loss, limiting its utility. Multishot DWI (msDWI) offers reduced image distortion and blurring relative to single-shot methods but increases sensitivity to motion artifacts. Motion-compensated diffusion-encoding gradients (MCGs) reduce motion artifacts and could improve motion robustness of msDWI but come with the cost of extended echo time, further reducing signal.
View Article and Find Full Text PDFNNFit: A Self-Supervised Deep Learning Method for Accelerated Quantification of High- Resolution Short Echo Time MR Spectroscopy Datasets.
Radiol Artif Intell
January 2025
From the Department of Radiation Oncology (A.S.G., V.H., H.S.) and Department of Radiology and Imaging Sciences (B.D.W.), Emory University School of Medicine, 1701 Uppergate Dr, C5008 Winship Cancer Institute, Atlanta, GA 30322; Department of Radiology, University of Miami {School of Medicine?}, Miami, Fla (S.S., A.A.M.); Department of {Radiology?} Northwestern University {Feinberg School of Medicine?}, Chicago, Ill (L.A.D.C.); Department of Biostatistics and Bioinformatics, Emory University Rollins School of Public Health, Atlanta, Ga (Y.L.); Department of Psychology, Emory University, Atlanta, Ga (M.T.); and Department of Radiology, Duke University Medical Center, Durham, NC (B.J.S.).
Purpose To develop and evaluate the performance of NNFit, a self-supervised deep-learning method for quantification of high-resolution short echo-time (TE) echo-planar spectroscopic imaging (EPSI) datasets, with the goal of addressing the computational bottleneck of conventional spectral quantification methods in the clinical workflow. Materials and Methods This retrospective study included 89 short-TE whole-brain EPSI/GRAPPA scans from clinical trials for glioblastoma (Trial 1, May 2014-October 2018) and major-depressive-disorder (Trial 2, 2022- 2023). The training dataset included 685k spectra from 20 participants (60 scans) in Trial 1.
View Article and Find Full Text PDFThe Added Role of Diffusion-Weighted Magnetic Resonance Imaging in Staging Uterine Cervical Cancer.
Cureus
December 2024
Department of Radiology, University of Medicine and Pharmacy of Craiova, Craiova, ROU.
Background: Cervical cancer is considered one of the most common gynecological malignancies with an increased incidence in developing countries. Magnetic resonance imaging (MRI) plays a valuable role in staging cervical cancer and providing valuable information necessary for selecting the appropriate treatment plan, while closely correlating with the prognosis of the patient.
Objective: The aim of this study is to assess the diagnostic value of diffusion-weighted imaging (DWI) in the preoperative loco-regional staging of cervical carcinoma.
The Value of Using Quantitative MRI based on Synthetic Acquisition and Apparent Diffusion Coefficient to Monitor Multiple Sclerosis Lesion Activity.
Curr Med Imaging
January 2025
Department of Radiology and Medical Imaging, King Saud University Medical City, King Saud University, Riyadh, KSA.
Background: Multiple sclerosis (MS) is one of the most common disabling central nervous system diseases affecting young adults. Magnetic resonance imaging (MRI) is an essential tool for diagnosing and following up multiple sclerosis. Over the years, many MRI techniques have been developed to improve the sensitivity of MS disease detection.
View Article and Find Full Text PDFDWI in the Differentiation of Malignant and Benign Breast Lesions Presenting with Non-Mass Enhancement on CE-MRI.
Cancers (Basel)
December 2024
Department of Diagnostic and Interventional Radiology, University Hospital Split, Spinčićeva 1, 21000 Split, Croatia.
Article Synopsis
- The study examined if ADC map values of breast lesions with non-mass enhancement on MRI could distinguish between benign and malignant cases.
- A total of 136 patients underwent ultrasound-guided biopsies, and radiologists measured ADC values independently, comparing results to pathology reports.
- The whole tumor region of interest (ROI) showed the highest sensitivity at 91% for detecting benign lesions, while there were no significant ADC value differences between fatty and dense breast tissues.
Want 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!