Apparent diffusion coefficient characteristics of parenchymal neuro-Behcet's disease

Aim To evaluate apparent diffusion coefficient (ADC) characteristic of parenchymal neuro-Behcet's disease (NBD). Methods We retrospectively reviewed cranial magnetic resonance imaging (MRI) examinations of NBD patients with acute or chronic parenchymal lesions. ADC measurements of the lesions and contralateral normal brain parenchyma were performed by a consensus of two radiologists. To compare the ADC value of the chronic and acute lesions, relative ADC values (rADC) were calculated. The ratio of the lesions' ADC to contralateral normal brain parenchyma ADC yielded a rADC value of the lesions. Contrast enhancement patterns and the locations of the lesions were also noted. Results A total of 24 NBD patients with 45 parenchymal lesions, 25 acute, and 20 chronic, were enrolled in the study. A significant difference was observed between the mean ADC value of the acute lesions (1074.48 +/- 138.31 m/s) and the mean ADC value of the contralateral normal brain parenchyma (841.20 +/- 142.96 m/s; P < 0.0001). A significant difference was observed between the mean ADC value of the chronic lesions (1069.95 +/- 143.95 m/s) and the mean ADC value of the contralateral normal brain parenchyma (793.90 +/- 96.71 m/s; P < 0.0001). No significant difference was observed between the mean rADC (1.35 +/- 0.20) and the mean rADC value of the chronic lesions (1.29 +/- 0.15; P = 0.22). Conclusions ADC measurements might provide substantial information about the histopathological aspect of parenchymal NBD lesions

Shear Wave Elastography of the Lumbar Multifidus Muscle in Patients With Unilateral Lumbar Disk Herniation

Objectives To assess lumbar multifidus muscle stiffness in patients with unilateral lumbar disk herniation (LDH) causing nerve root compression using shear wave elastography (SWE). Methods Thirty-three patients with unilateral subarticular LDH (L3-L4, L4-L5, and L5-S1) causing nerve root compression, diagnosed by magnetic resonance imaging, were enrolled in the study. Exclusion criteria were bilateral or multilevel LDH confirmed on magnetic resonance imaging, bilateral leg symptoms, and patients with a history of any spinal operation, malignancy, trauma, infection, spondylolisthesis, severe lateral recess stenosis, spinal canal stenosis, and substantial comorbidities. Two observers separately evaluated the multifidus muscle using SWE. Shear wave elastographic examinations of the muscle were performed slightly below the herniation using the spinous process of the vertebra as a landmark. The stiffness of the muscle between affected and normal sides was compared. Moreover, the correlation between the stiffness and duration of the symptoms and the correlation between the stiffness and severity of the nerve compression were also calculated. Results The mean stiffness values of the multifidus muscle on the affected side (mean +/- SD: observer 1, 14.08 +/- 3.57 kPa; observer 2, 13.70 +/- 4.05 kPa) were significantly lower compared to the contralateral side (observer 1, 18.81 +/- 3.95 kPa; observer 2, 18.28 +/- 4.12 kPa; P < .001). The muscle stiffness had a moderate negative correlation with the duration of the symptoms and the severity of the nerve compression (observer 1, r = -0.535; observer 2, r = -0.458; P < .001). Conclusions The multifidus muscle on the ipsilateral side of the LDH showed reduced stiffness values, and stiffness values were negatively correlated with the disease duration and severity of the nerve compression. Further studies might reveal the potential role of SWE of the multifidus muscle in determining clinical outcomes and assessing effectiveness treatment in patients with LDH

Assessment of the common carotid artery wall stiffness by Shear Wave Elastography in Behcet's disease

Aim: To evaluate endothelial dysfunction and subclinical atherosclerosis in Behcet's disease (BD) by measuring the common carotid artery (CCA) wall stiffness and carotid intima-media thickness (CIMT). Materials and methods: We prospectively evaluated CIMT and the CCA wall stillness of 34 BD patients and 28 age/sex-matched controls. CIMT measurements were performed from the posterior wall of the carotid artery approximately 10 mm proximal to the initiation of the carotid bulb using B-mode ultrasound. The stillness of the CCA was measured from the superficial wall of the CCA using shear wave elastography (SWE). SWE measurements were recorded as shear wave velocity (SWV) using m/s as a unit. Results: The mean right (0.5 +/- 0.11 mm) and left (05 +/- 0.14 nun) CIMT of the patients were significantly higher compared to the mean right (0.41 +/- 0.07 nun) and left (0.41 +/- 0.11 mm) CIMT of the healthy controls (p=0.001 and p= 0.003 respectively). The mean right (3.72 +/- 0.94 m/s) and left (3.5 +/- 0.72 m/s) CCA wall stillness of the patients were significantly higher compared to the mean right (2.42 +/- 0.49 m/s) and left (2.56 +/- 0.49 m/s) CCA wall stiffness of the controls (p<0.001 for both). Conclusions: SWE seems to be a promising modality to evaluate endothelial dysfunction in BD by interpreting the arterial stillness, and SWE might be an important adjunct to clinical and laboratory findings, and imaging modalities to assess cardiovascular risk in BD. Moreover, SWE evaluation of the arterial stillness might assist us to understand pathophysiological aspects of BD

Inter-vendor performance of deep learning in segmenting acute ischemic lesions on diffusion-weighted imaging: a multicenter study

Abstract There is little evidence on the applicability of deep learning (DL) in the segmentation of acute ischemic lesions on diffusion-weighted imaging (DWI) between magnetic resonance imaging (MRI) scanners of different manufacturers. We retrospectively included DWI data of patients with acute ischemic lesions from six centers. Dataset A (n = 2986) and B (n = 3951) included data from Siemens and GE MRI scanners, respectively. The datasets were split into the training (80%), validation (10%), and internal test (10%) sets, and six neuroradiologists created ground-truth masks. Models A and B were the proposed neural networks trained on datasets A and B. The models subsequently fine-tuned across the datasets using their validation data. Another radiologist performed the segmentation on the test sets for comparisons. The median Dice scores of models A and B were 0.858 and 0.857 for the internal tests, which were non-inferior to the radiologist’s performance, but demonstrated lower performance than the radiologist on the external tests. Fine-tuned models A and B achieved median Dice scores of 0.832 and 0.846, which were non-inferior to the radiologist's performance on the external tests. The present work shows that the inter-vendor operability of deep learning for the segmentation of ischemic lesions on DWI might be enhanced via transfer learning; thereby, their clinical applicability and generalizability could be improved

A joint convolutional-recurrent neural network with an attention mechanism for detecting intracranial hemorrhage on noncontrast head CT

To investigate the performance of a joint convolutional neural networks-recurrent neural networks (CNN-RNN) using an attention mechanism in identifying and classifying intracranial hemorrhage (ICH) on a large multi-center dataset; to test its performance in a prospective independent sample consisting of consecutive real-world patients. All consecutive patients who underwent emergency non-contrast-enhanced head CT in five different centers were retrospectively gathered. Five neuroradiologists created the ground-truth labels. The development dataset was divided into the training and validation set. After the development phase, we integrated the deep learning model into an independent center's PACS environment for over six months for assessing the performance in a real clinical setting. Three radiologists created the ground-truth labels of the testing set with a majority voting. A total of 55,179 head CT scans of 48,070 patients, 28,253 men (58.77%), with a mean age of 53.84 +/- 17.64 years (range 18-89) were enrolled in the study. The validation sample comprised 5211 head CT scans, with 991 being annotated as ICH-positive. The model's binary accuracy, sensitivity, and specificity on the validation set were 99.41%, 99.70%, and 98.91, respectively. During the prospective implementation, the model yielded an accuracy of 96.02% on 452 head CT scans with an average prediction time of 45 +/- 8 s. The joint CNN-RNN model with an attention mechanism yielded excellent diagnostic accuracy in assessing ICH and its subtypes on a large-scale sample. The model was seamlessly integrated into the radiology workflow. Though slightly decreased performance, it provided decisions on the sample of consecutive real-world patients within a minute