Objective: To evaluate whether a frequency-selective non-linear blending (BC) technique can improve tissue contrast and infarct detection on non-enhanced brain CT (NECT) in postoperative Moyamoya (MMD) patients.
Materials And Methods: From January 2010 to December 2017, 33 children (13boys and 20girls; mean age 9.1±3.4 years) with MMD postoperatively underwent NECT followed by diffusion MRI. We compared the contrast-to-noise ratio (CNR) between gray matter (GM) and white matter (WM) in NECT and BC images and the CNR between the infarct lesion and adjacent normal-appearing brain in NECT and BC images using a paired t-test. We assessed image noise, GM-WM differentiation, artifacts, and overall quality using a Wilcoxon signed rank test. A McNemar two-tailed test was conducted to compare the diagnostic accuracy of infarct detection.
Results: The CNR between GM and WM and the CNR of the infarct was better in BC images than in NECT images (3.9±1.0 vs. 1.8±0.6, P<0.001 and 3.6±0.3 vs. 1.9±0.2, P<0.001), with no difference in overall image quality observed. The sensitivity and specificity of infarct detection were 55.0% and 76.9% using NECT, and 70.0% and 69.2% using BC technique. The diagnostic accuracy of NECT and BC technique was 63.6% (21/33) and 69.7% (23/33), respectively.
Conclusion: This study showed that the BC technique improved CNR and maintained image quality. This technique may also be used to identify ischemic brain changes in postoperative MMD patients by improving the CNR of the infarct lesion.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.neurad.2019.07.006 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Objectives The primary objective of this study is to describe and evaluate the diagnostic performance of the hyperdense right hemidiaphragm sign (HRHS) as a novel radiological indicator for diffuse fatty infiltration of the liver on non-enhanced CT (NECT) scans. This includes assessing its sensitivity, specificity, positive predictive value, and negative predictive value, and comparing these metrics with other established NECT signs. Methods This cross-sectional multicenter retrospective study included all patients over 12 years of age who underwent both abdominal MRI and NECT scans of the abdomen within a period not exceeding six months at two tertiary hospitals (The Royal Hospital and Armed Forces Hospital, Muscat, Sultanate of Oman) between January 2010 and December 2022.
View Article and Find Full Text PDFChange in Emergency Department Length of Stay Following Routine Adoption of Dual-Energy Computed Tomography to Differentiate Intracranial Hemorrhage from Calcification.
AJNR Am J Neuroradiol
December 2024
From the Departments of Radiology (C.A.P., A.D.S.), Brigham and Women's Hospital, Boston, MA, USA and (N.A.T.) University of California San Francisco, San Francisco California, CA, USA.
Background And Purpose: Dual energy computed tomography (DECT) is an advanced CT technique which has been shown to improve accuracy in distinguishing between intracranial hemorrhage and calcification, which is often challenging on conventional CT and therefore may warrant repeat imaging in the emergency department (ED) to document stability and exclude enlarging intracranial hemorrhage. We hypothesized that implementation of a DECT head protocol with fully automated post processing in the ED would decrease the need for repeat imaging and therefore reduce overall ED length of stay (LOS).
Materials And Methods: This is a retrospective study comparing ED length of stay over a one-year period before (7/1/2016-6/30/2017) and after (7/1/2018-6/30/2019) implementing a DECT head protocol, for patients scanned for headache, trauma or fall, who were found to have indeterminate intracranial hyperdensities on conventional images, and were subsequently discharged home from the ER (excluding patients who were admitted, taken to the OR, or left against medical advice).
Deep-learning-based method for the segmentation of ureter and renal pelvis on non-enhanced CT scans.
Sci Rep
August 2024
Department of Radiology, Second Hospital of Shandong University, Jinan, China.
This study aimed to develop a deep-learning (DL) based method for three-dimensional (3D) segmentation of the upper urinary tract (UUT), including ureter and renal pelvis, on non-enhanced computed tomography (NECT) scans. A total of 150 NECT scans with normal appearance of the left UUT were chosen for this study. The dataset was divided into training (n = 130) and validation sets (n = 20).
View Article and Find Full Text PDFA 3D Convolutional Neural Network Based on Non-enhanced Brain CT to Identify Patients with Brain Metastases.
J Imaging Inform Med
August 2024
Radiotherapy Department, Institut de Cancérologie de Strasbourg (ICANS), 67200, Strasbourg, France.
Article Synopsis
- Dedicated brain imaging for cancer patients is rarely recommended unless symptoms arise, and brain metastases are often difficult to identify on non-enhanced CT (NE-CT).
- This research aims to create a 3D Convolutional Neural Network (3D-CNN) using NE-CT scans to effectively distinguish between cancer patients with and without brain metastases.
- The study utilized a dataset of 200 patients (100 with and 100 without brain abnormalities), achieving high accuracy (ACC of 0.983) and sensitivity (0.983) in detecting brain metastases, indicating the potential for a reliable diagnostic tool with further validation needed.
Clinical feasibility of deep learning based synthetic contrast enhanced abdominal CT in patients undergoing non enhanced CT scans.
Sci Rep
July 2024
Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.
Our objective was to develop and evaluate the clinical feasibility of deep-learning-based synthetic contrast-enhanced computed tomography (DL-SynCCT) in patients designated for nonenhanced CT (NECT). We proposed a weakly supervised learning with the utilization of virtual non-contrast CT (VNC) for the development of DL-SynCCT. Training and internal validations were performed with 2202 pairs of retrospectively collected contrast-enhanced CT (CECT) images with the corresponding VNC images acquired from dual-energy CT.
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!