American Journal of Neuroradiology最新文献

Orbital lesions are rare but serious. Their characterization remains challenging. Diagnosis is based on biopsy or surgery, which implies functional risks. It is necessary to develop noninvasive diagnostic tools. The goal of this study was to evaluate the diagnostic performance of dynamic contrast-enhanced MR imaging at 3T when distinguishing malignant from benign orbital tumors on a large prospective cohort.

This institutional review board–approved prospective single-center study enrolled participants presenting with an orbital lesion undergoing a 3T MR imaging before surgery from December 2015 to May 2021. Morphologic, diffusion-weighted, and dynamic contrast-enhanced MR images were assessed by 2 readers blinded to all data. Univariable and multivariable analyses were performed. To assess diagnostic performance, we used the following metrics: area under the curve, sensitivity, and specificity. Histologic analysis, obtained through biopsy or surgery, served as the criterion standard for determining the benign or malignant status of the tumor.

One hundred thirty-one subjects (66/131 [50%] women and 65/131 [50%] men; mean age, 52 [SD, 17.1] years; range, 19–88 years) were enrolled. Ninety of 131 (69%) had a benign lesion, and 41/131 (31%) had a malignant lesion. Univariable analysis showed a higher median of transfer constant from blood plasma to the interstitial environment (K^trans) and of transfer constant from the interstitial environment to the blood plasma (minute^–1) (Kep) and a higher interquartile range of K^trans in malignant-versus-benign lesions (1.1 minute^–1 versus 0.65 minute^–1, P = .03; 2.1 minute^–1 versus 1.1 minute^–1, P = .01; 0.81 minute^–1 versus 0.65 minute^–1, P = .009, respectively). The best-performing multivariable model in distinguishing malignant-versus-benign lesions included parameters from dynamic contrast-enhanced imaging, ADC, and morphology and reached an area under the curve of 0.81 (95% CI, 0.67–0.96), a sensitivity of 0.82 (95% CI, 0.55–1), and a specificity of 0.81 (95% CI, 0.65–0.96).

Dynamic contrast-enhanced MR imaging at 3T appears valuable when characterizing orbital lesions and provides complementary information to morphologic imaging and DWI.

Recent developments in deep learning methods offer a potential solution to the need for alternative imaging methods due to concerns about the toxicity of gadolinium-based contrast agents. The purpose of the study was to synthesize virtual gadolinium contrast-enhanced T1-weighted MR images from noncontrast multiparametric MR images in patients with primary brain tumors by using deep learning.

We trained and validated a deep learning network by using MR images from 335 subjects in the Brain Tumor Segmentation Challenge 2019 training data set. A held out set of 125 subjects from the Brain Tumor Segmentation Challenge 2019 validation data set was used to test the generalization of the model. A residual inception DenseNet network, called T1c-ET, was developed and trained to simultaneously synthesize virtual contrast-enhanced T1-weighted (vT1c) images and segment the enhancing portions of the tumor. Three expert neuroradiologists independently scored the synthesized vT1c images by using a 3-point Likert scale, evaluating image quality and contrast enhancement against ground truth T1c images (1 = poor, 2 = good, 3 = excellent).

The synthesized vT1c images achieved structural similarity index, peak signal-to-noise ratio, and normalized mean square error scores of 0.91, 64.35, and 0.03, respectively. There was moderate interobserver agreement between the 3 raters, regarding the algorithm’s performance in predicting contrast enhancement, with a Fleiss kappa value of 0.61. Our model was able to accurately predict contrast enhancement in 88.8% of the cases (scores of 2 to 3 on the 3-point scale).

We developed a novel deep learning architecture to synthesize virtual postcontrast enhancement by using only conventional noncontrast brain MR images. Our results demonstrate the potential of deep learning methods to reduce the need for gadolinium contrast in the evaluation of primary brain tumors.

CSF-venous fistulas (CVFs) associated with spontaneous intracranial hypotension (SIH) may have a transient appearance, relative to contrast arrival, which may influence the diagnostic performance of lateral decubitus CT myelography (CTM). We developed a dynamic CTM protocol using real-time bolus-tracking (dCTM-BT) to improve the temporal resolution and standardize the timing of CTM acquisitions post-intrathecal contrast administration. The purpose of our study was to evaluate the feasibility of the dCTM-BT technique and evaluate its diagnostic yield for CVF detection, stratified by brain MRI SIH findings.

Patients with suspected SIH without extradural fluid collection on spine MRI who underwent dCTM-BT were retrospectively reviewed. CT bolus monitoring was performed at the upper thoracic level. Following the visualization of dense intrathecal contrast, at least 3 CTM acquisitions of the spine were obtained and reviewed by 2 neuroradiologists. The Bern SIH score was calculated on the brain MRI. The diagnostic yield for CVF detection was evaluated, stratified by Bern score categories and a receiver operating characteristic (ROC) analysis.

Out of 48 patients, 23 (48%) had a CVF on dCTM-BT, located at T1–5 (n = 4), T6–12 (n = 18), L1 (n = 1), with 70% on the right. CVF was identified in 22/22 (100%) of patients who had a high Bern score, 1/7 (14%) of those who had an intermediate score, and 0/19 (0%) of those who had a low score. The area under the ROC curve was 0.99 (95% CI, 0.98–1.00). The optimal cutoff was a Bern score of ≥5 (96% sensitivity, 100% specificity).

dCTM-BT is feasible and has excellent diagnostic performance for CVF identification/localization. The Bern score is strongly associated with CVF detection and may help inform who will benefit from dCTM-BT.

Up to 30% of children with cleft palate will develop a severe speech disorder known as velopharyngeal insufficiency. Management of velopharyngeal insufficiency typically involves structural and functional assessment of the velum and pharynx by endoscopy and/or videofluoroscopy. These methods cannot provide direct evaluation of underlying velopharyngeal musculature. MR imaging offers an ideal imaging method, providing noninvasive, high-contrast, high-resolution imaging of soft-tissue anatomy. Furthermore, focused-speech MR imaging techniques can evaluate the function of the velum and pharynx during sustained speech production, providing critical physiologic information that supplements anatomic findings. The use of MR imaging for velopharyngeal evaluation is relatively novel, with limited literature describing its use in clinical radiology. Here we provide a practical approach to perform and interpret velopharyngeal MR imaging examinations. This article discusses the velopharyngeal MR imaging protocol, methods for interpreting velopharyngeal anatomy, and examples illustrating its clinical applications. This knowledge will provide radiologists with a new, noninvasive tool to offer to referring specialists.

Flow diversion treatment repairs aneurysms by altering the hemodynamics of the aneurysmal sac and providing a scaffold for endothelial cell adhesion. The purpose of this study was to investigate the correlation of flow diverter (FD) malapposition at the aneurysm neck with incomplete occlusion of small intracranial aneurysms (IAs) and investigate other factors that are possibly related to incomplete occlusion.

From January 2019 to June 2022, the clinical and imaging data for 153 patients (175 aneurysms) with unruptured small IAs treated with flow diversion were retrospectively analyzed. FD apposition at the aneurysm neck was evaluated by high-resolution conebeam CT (HR-CBCT), and the complete occlusion rate for aneurysms was judged according to the latest follow-up conventional angiography findings (≥6 months). Multivariate logistic regression analysis was used to determine factors associated with incomplete aneurysm occlusion.

In total, 159 FDs were implanted in 153 patients. HR-CBCT performed after the deployment revealed FD malapposition at the aneurysm neck in 18 cases. According to the latest follow-up angiograms (average: 9.47 ± 3.35 months), the complete aneurysm occlusion rate was 66.9%. The complete occlusion rates for incomplete and complete stent apposition at the neck were 38.9% (7/18) and 70.1% (110/157), respectively. The results of regression analysis showed that an aneurysm sac with branch vessels (OR, 2.937; P = .018), incomplete stent apposition at the aneurysm neck (OR, 3.561; P = .023), and a large aneurysm diameter (OR, 1.533; P = .028) were positive predictors of incomplete aneurysm occlusion.

An aneurysm sac with branch vessels, a large aneurysm diameter, and malapposition at the aneurysm neck significantly affect aneurysm repair after FD stent-only treatment for small IAs.

Photon-counting detector CT myelography is a recently described technique that has several advantages for the detection of CSF-venous fistulas, one of which is improved spatial resolution. To maximally leverage the high spatial resolution of photon-counting detector CT, a sharp kernel and a thin section reconstruction are needed. Sharp kernels and thin slices often result in increased noise, degrading image quality. Here, we describe a novel deep-learning-based algorithm used to denoise photon-counting detector CT myelographic images, allowing the sharpest and thinnest quantitative reconstruction available on the scanner to be used to enhance diagnostic image quality. Currently, the algorithm requires 4–6 hours to create diagnostic, denoised images. This algorithm has the potential to increase the sensitivity of photon-counting detector CT myelography for detecting CSF-venous fistulas, and the technique may be valuable for institutions attempting to optimize photon-counting detector CT myelography imaging protocols.