Index by author

This study evaluates a convolutional neural network optimized for the detection and quantification of intraparenchymal, epidural/subdural, and subarachnoid hemorrhages on noncontrast CT with a 10,159-examination training cohort (512,598 images; 901/8.1% hemorrhages) and an 862-examination test cohort (23,668 images; 82/12% hemorrhages). Accuracy, area under the curve, sensitivity, specificity, positive predictive value, and negative predictive value for hemorrhage detection were 0.975, 0.983, 0.971, 0.975, 0.793, and 0.997 on training cohort cross-validation and 0.970, 0.981, 0.951, 0.973, 0.829, and 0.993 for the prospective test set.

This study evaluates a convolutional neural network optimized for the detection and quantification of intraparenchymal, epidural/subdural, and subarachnoid hemorrhages on noncontrast CT with a 10,159-examination training cohort (512,598 images; 901/8.1% hemorrhages) and an 862-examination test cohort (23,668 images; 82/12% hemorrhages). Accuracy, area under the curve, sensitivity, specificity, positive predictive value, and negative predictive value for hemorrhage detection were 0.975, 0.983, 0.971, 0.975, 0.793, and 0.997 on training cohort cross-validation and 0.970, 0.981, 0.951, 0.973, 0.829, and 0.993 for the prospective test set.

This study evaluates a convolutional neural network optimized for the detection and quantification of intraparenchymal, epidural/subdural, and subarachnoid hemorrhages on noncontrast CT with a 10,159-examination training cohort (512,598 images; 901/8.1% hemorrhages) and an 862-examination test cohort (23,668 images; 82/12% hemorrhages). Accuracy, area under the curve, sensitivity, specificity, positive predictive value, and negative predictive value for hemorrhage detection were 0.975, 0.983, 0.971, 0.975, 0.793, and 0.997 on training cohort cross-validation and 0.970, 0.981, 0.951, 0.973, 0.829, and 0.993 for the prospective test set.

Thirty-eight patients with clinical indications of infectious, degenerative, and neoplastic disease underwent an MR imaging of the spine. The SyntAc sequence, with an acquisition time of 5 minutes 40 seconds, was added to the usual imaging protocol consisting of conventional sagittal T1 TSE, T2 TSE, and STIR TSE. The image quality was rated as “good” for both synthetic and conventional images. Interreader agreement concerning lesion conspicuity was good with a Cohen kappa of 0.737. The authors conclude that the study shows that synthetic MR imaging is feasible in spine imaging and produces, in general, good image quality and diagnostic confidence.

Twenty children (30 imaging sessions, 60 MR scans) with Moyamoya disease underwent dual breath-hold hypercapnic challenge blood oxygen level–dependent MR imaging of cerebrovascular reactivity studies in the same MR imaging session. Within-day, within-subject repeatability of cerebrovascular reactivity estimates, derived from the blood oxygen level–dependent signal, was computed. Breath-hold hypercapnic challenge blood oxygen level–dependent MR imaging is a repeatable technique for the assessment of cerebrovascular reactivity in children with Moyamoya disease and is reliably interpretable for use in clinical practice.

This study evaluates a convolutional neural network optimized for the detection and quantification of intraparenchymal, epidural/subdural, and subarachnoid hemorrhages on noncontrast CT with a 10,159-examination training cohort (512,598 images; 901/8.1% hemorrhages) and an 862-examination test cohort (23,668 images; 82/12% hemorrhages). Accuracy, area under the curve, sensitivity, specificity, positive predictive value, and negative predictive value for hemorrhage detection were 0.975, 0.983, 0.971, 0.975, 0.793, and 0.997 on training cohort cross-validation and 0.970, 0.981, 0.951, 0.973, 0.829, and 0.993 for the prospective test set.

Retrospective review of high-resolution MRIs was performed in patients without prior microvascular decompression. 3D-CISS imaging without and with contrast for 81 patients with trigeminal neuralgia and 15 controls was intermixed and independently reviewed in a blinded fashion. Cisternal segments of both trigeminal nerves were assessed for the grade of neurovascular conflict, cross-sectional area, and degree of flattening. Contrast-enhanced CISS more than doubled the prevalence of the highest grade of neurovascular conflict (14.8% versus 33.3%) and yielded significantly lower cross-sectional area and greater degree of flattening for advanced-grade neurovascular conflict on the symptomatic side compared with non-contrast-enhanced CISS.

This study evaluates a convolutional neural network optimized for the detection and quantification of intraparenchymal, epidural/subdural, and subarachnoid hemorrhages on noncontrast CT with a 10,159-examination training cohort (512,598 images; 901/8.1% hemorrhages) and an 862-examination test cohort (23,668 images; 82/12% hemorrhages). Accuracy, area under the curve, sensitivity, specificity, positive predictive value, and negative predictive value for hemorrhage detection were 0.975, 0.983, 0.971, 0.975, 0.793, and 0.997 on training cohort cross-validation and 0.970, 0.981, 0.951, 0.973, 0.829, and 0.993 for the prospective test set.

The authors automated the identification of enlarged perivascular spaces in brain MR images using a custom quantitative program designed with Matlab. They quantified the densities of enlarged perivascular spaces for patients with mild cognitive impairment (n=14) and age-matched cognitively healthy controls (n=15) and compared them to determine whether the density of enlarged perivascular spaces can serve as an imaging surrogate for mild cognitive impairment diagnosis. The density of enlarged perivascular spaces was found to be significantly higher in those with mild cognitive impairment compared with age-matched healthy control subjects.

Twenty children (30 imaging sessions, 60 MR scans) with Moyamoya disease underwent dual breath-hold hypercapnic challenge blood oxygen level–dependent MR imaging of cerebrovascular reactivity studies in the same MR imaging session. Within-day, within-subject repeatability of cerebrovascular reactivity estimates, derived from the blood oxygen level–dependent signal, was computed. Breath-hold hypercapnic challenge blood oxygen level–dependent MR imaging is a repeatable technique for the assessment of cerebrovascular reactivity in children with Moyamoya disease and is reliably interpretable for use in clinical practice.

This study evaluates a convolutional neural network optimized for the detection and quantification of intraparenchymal, epidural/subdural, and subarachnoid hemorrhages on noncontrast CT with a 10,159-examination training cohort (512,598 images; 901/8.1% hemorrhages) and an 862-examination test cohort (23,668 images; 82/12% hemorrhages). Accuracy, area under the curve, sensitivity, specificity, positive predictive value, and negative predictive value for hemorrhage detection were 0.975, 0.983, 0.971, 0.975, 0.793, and 0.997 on training cohort cross-validation and 0.970, 0.981, 0.951, 0.973, 0.829, and 0.993 for the prospective test set.

Twenty children (30 imaging sessions, 60 MR scans) with Moyamoya disease underwent dual breath-hold hypercapnic challenge blood oxygen level–dependent MR imaging of cerebrovascular reactivity studies in the same MR imaging session. Within-day, within-subject repeatability of cerebrovascular reactivity estimates, derived from the blood oxygen level–dependent signal, was computed. Breath-hold hypercapnic challenge blood oxygen level–dependent MR imaging is a repeatable technique for the assessment of cerebrovascular reactivity in children with Moyamoya disease and is reliably interpretable for use in clinical practice.

Thirty-eight patients with clinical indications of infectious, degenerative, and neoplastic disease underwent an MR imaging of the spine. The SyntAc sequence, with an acquisition time of 5 minutes 40 seconds, was added to the usual imaging protocol consisting of conventional sagittal T1 TSE, T2 TSE, and STIR TSE. The image quality was rated as “good” for both synthetic and conventional images. Interreader agreement concerning lesion conspicuity was good with a Cohen kappa of 0.737. The authors conclude that the study shows that synthetic MR imaging is feasible in spine imaging and produces, in general, good image quality and diagnostic confidence.

The authors automated the identification of enlarged perivascular spaces in brain MR images using a custom quantitative program designed with Matlab. They quantified the densities of enlarged perivascular spaces for patients with mild cognitive impairment (n=14) and age-matched cognitively healthy controls (n=15) and compared them to determine whether the density of enlarged perivascular spaces can serve as an imaging surrogate for mild cognitive impairment diagnosis. The density of enlarged perivascular spaces was found to be significantly higher in those with mild cognitive impairment compared with age-matched healthy control subjects.

Twenty children (30 imaging sessions, 60 MR scans) with Moyamoya disease underwent dual breath-hold hypercapnic challenge blood oxygen level–dependent MR imaging of cerebrovascular reactivity studies in the same MR imaging session. Within-day, within-subject repeatability of cerebrovascular reactivity estimates, derived from the blood oxygen level–dependent signal, was computed. Breath-hold hypercapnic challenge blood oxygen level–dependent MR imaging is a repeatable technique for the assessment of cerebrovascular reactivity in children with Moyamoya disease and is reliably interpretable for use in clinical practice.

Twenty children (30 imaging sessions, 60 MR scans) with Moyamoya disease underwent dual breath-hold hypercapnic challenge blood oxygen level–dependent MR imaging of cerebrovascular reactivity studies in the same MR imaging session. Within-day, within-subject repeatability of cerebrovascular reactivity estimates, derived from the blood oxygen level–dependent signal, was computed. Breath-hold hypercapnic challenge blood oxygen level–dependent MR imaging is a repeatable technique for the assessment of cerebrovascular reactivity in children with Moyamoya disease and is reliably interpretable for use in clinical practice.

This study evaluates a convolutional neural network optimized for the detection and quantification of intraparenchymal, epidural/subdural, and subarachnoid hemorrhages on noncontrast CT with a 10,159-examination training cohort (512,598 images; 901/8.1% hemorrhages) and an 862-examination test cohort (23,668 images; 82/12% hemorrhages). Accuracy, area under the curve, sensitivity, specificity, positive predictive value, and negative predictive value for hemorrhage detection were 0.975, 0.983, 0.971, 0.975, 0.793, and 0.997 on training cohort cross-validation and 0.970, 0.981, 0.951, 0.973, 0.829, and 0.993 for the prospective test set.

This study evaluates a convolutional neural network optimized for the detection and quantification of intraparenchymal, epidural/subdural, and subarachnoid hemorrhages on noncontrast CT with a 10,159-examination training cohort (512,598 images; 901/8.1% hemorrhages) and an 862-examination test cohort (23,668 images; 82/12% hemorrhages). Accuracy, area under the curve, sensitivity, specificity, positive predictive value, and negative predictive value for hemorrhage detection were 0.975, 0.983, 0.971, 0.975, 0.793, and 0.997 on training cohort cross-validation and 0.970, 0.981, 0.951, 0.973, 0.829, and 0.993 for the prospective test set.

This study evaluates a convolutional neural network optimized for the detection and quantification of intraparenchymal, epidural/subdural, and subarachnoid hemorrhages on noncontrast CT with a 10,159-examination training cohort (512,598 images; 901/8.1% hemorrhages) and an 862-examination test cohort (23,668 images; 82/12% hemorrhages). Accuracy, area under the curve, sensitivity, specificity, positive predictive value, and negative predictive value for hemorrhage detection were 0.975, 0.983, 0.971, 0.975, 0.793, and 0.997 on training cohort cross-validation and 0.970, 0.981, 0.951, 0.973, 0.829, and 0.993 for the prospective test set.