Transcranial MR Imaging-Guided Focused Ultrasound Interventions Using Deep Learning Synthesized CT.

BACKGROUND AND PURPOSE: Transcranial MR imaging-guided focused ultrasound is a promising novel technique to treat multiple disorders and diseases. Planning for transcranial MR imaging-guided focused ultrasound requires both a CT scan for skull density estimation and treatment-planning simulation and an MR imaging for target identification. It is desirable to simplify the clinical workflow of transcranial MR imaging-guided focused ultrasound treatment planning. The purpose of this study was to examine the feasibility of deep learning techniques to convert MR imaging ultrashort TE images directly to synthetic CT of the skull images for use in transcranial MR imaging-guided focused ultrasound treatment planning. MATERIALS AND METHODS: The U-Net neural network was trained and tested on data obtained from 41 subjects (mean age, 66.4 ± 11.0 years; 15 women). The derived neural network model was evaluated using a k-fold cross-validation method. Derived acoustic properties were verified by comparing the whole skull-density ratio from deep learning synthesized CT of the skull with the reference CT of the skull. In addition, acoustic and temperature simulations were performed using the deep learning CT to predict the target temperature rise during transcranial MR imaging-guided focused ultrasound. RESULTS: The derived deep learning model generates synthetic CT of the skull images that are highly comparable with the true CT of the skull images. Their intensities in Hounsfield units have a spatial correlation coefficient of 0.80 ± 0.08, a mean absolute error of 104.57 ± 21.33 HU, and a subject-wise correlation coefficient of 0.91. Furthermore, deep learning CT of the skull is reliable in the skull-density ratio estimation (r = 0.96). A simulation study showed that both the peak target temperatures and temperature distribution from deep learning CT are comparable with those of the reference CT. CONCLUSIONS: The deep learning method can be used to simplify workflow associated with transcranial MR imaging-guided focused ultrasound.

Synthetic MRI for Clinical Neuroimaging: Results of the Magnetic Resonance Image Compilation (MAGiC) Prospective, Multicenter, Multireader Trial.

BACKGROUND AND PURPOSE: Synthetic MR imaging enables reconstruction of various image contrasts from 1 scan, reducing scan times and potentially providing novel information. This study is the first large, prospective comparison of synthetic-versus-conventional MR imaging for routine neuroimaging. MATERIALS AND METHODS: A prospective multireader, multicase noninferiority trial of 1526 images read by 7 blinded neuroradiologists was performed with prospectively acquired synthetic and conventional brain MR imaging case-control pairs from 109 subjects (mean, 53.0 ± 18.5 years of age; range, 19-89 years of age) with neuroimaging indications. Each case included conventional T1- and T2-weighted, T1 and T2 FLAIR, and STIR and/or proton density and synthetic reconstructions from multiple-dynamic multiple-echo imaging. Images were randomized and independently assessed for diagnostic quality, morphologic legibility, radiologic findings indicative of diagnosis, and artifacts. RESULTS: Clinical MR imaging studies revealed 46 healthy and 63 pathologic cases. Overall diagnostic quality of synthetic MR images was noninferior to conventional imaging on a 5-level Likert scale (P < .001; mean synthetic-conventional, -0.335 ± 0.352; Δ = 0.5; lower limit of the 95% CI, -0.402). Legibility of synthetic and conventional morphology agreed in >95%, except in the posterior limb of the internal capsule for T1, T1 FLAIR, and proton-density views (all, >80%). Synthetic T2 FLAIR had more pronounced artifacts, including +24.1% of cases with flow artifacts and +17.6% cases with white noise artifacts. CONCLUSIONS: Overall synthetic MR imaging quality was similar to that of conventional proton-density, STIR, and T1- and T2-weighted contrast views across neurologic conditions. While artifacts were more common in synthetic T2 FLAIR, these were readily recognizable and did not mimic pathology but could necessitate additional conventional T2 FLAIR to confirm the diagnosis.

Intracranial Applications of MR Imaging-Guided Focused Ultrasound.

Initially used in the treatment of prostate cancer and uterine fibroids, the role of focused ultrasound has expanded as transcranial acoustic wave distortion and other limitations have been overcome. Its utility relies on focal energy deposition via acoustic wave propagation. The duty cycle and intensity of focused ultrasound influence the rate of energy deposition and result in unique physiologic and biomechanical effects. Thermal ablation via high-intensity continuous exposure generates coagulative necrosis of tissues. High-intensity, pulsed application reduces temporally averaged energy deposition, resulting in mechanical effects, including reversible, localized BBB disruption, which enhances neurotherapeutic agent delivery. While the precise mechanisms remain unclear, low-intensity, pulsed exposures can influence neuronal activity with preservation of cytoarchitecture. Its noninvasive nature, high-resolution, radiation-free features allow focused ultrasound to compare favorably with other modalities. We discuss the physical characteristics of focused ultrasound devices, the biophysical mechanisms at the tissue level, and current and emerging applications.

A prospective longitudinal brain morphometry study of children with sickle cell disease.

BACKGROUND AND PURPOSE: Age-related changes in brain morphology are crucial to understanding the neurobiology of sickle cell disease. We hypothesized that the growth trajectories for total GM volume, total WM volume, and regional GM volumes are altered in children with sickle cell disease compared with controls. MATERIALS AND METHODS: We analyzed T1-weighted images of the brains of 28 children with sickle cell disease (mean baseline age, 98 months; female/male ratio, 15:13) and 28 healthy age- and sex-matched controls (mean baseline age, 99 months; female/male ratio, 16:12). The total number of MR imaging examinations was 141 (2-4 for each subject with sickle cell disease, 2-3 for each control subject). Total GM volume, total WM volume, and regional GM volumes were measured by using an automated method. We used the multilevel-model-for-change approach to model growth trajectories. RESULTS: Total GM volume in subjects with sickle cell disease decreased linearly at a rate of 411 mm(3) per month. For controls, the trajectory of total GM volume was quadratic; we did not observe a significant linear decline. For subjects with sickle cell disease, we found 35 brain structures that demonstrated age-related GM volume reduction. Total WM volume in subjects with sickle cell disease increased at a rate of 452 mm(3) per month, while the trajectory of controls was quadratic. CONCLUSIONS: There was a significant age-related decrease in total GM volume in children with sickle cell disease. The GM volume reduction was spatially distributed widely across the brain, primarily in the frontal, parietal, and occipital lobes. Total WM volume in subjects with sickle cell disease increased at a lower rate than for controls.

Diagnostic utility of diffusion tensor imaging in differentiating glioblastomas from brain metastases.

BACKGROUND AND PURPOSE: Differentiation of glioblastomas and solitary brain metastases is an important clinical problem because the treatment strategy can differ significantly. The purpose of this study was to investigate the potential added value of DTI metrics in differentiating glioblastomas from brain metastases. MATERIALS AND METHODS: One hundred twenty-eight patients with glioblastomas and 93 with brain metastases were retrospectively identified. Fractional anisotropy and mean diffusivity values were measured from the enhancing and peritumoral regions of the tumor. Two experienced neuroradiologists independently rated all cases by using conventional MR imaging and DTI. The diagnostic performances of the 2 raters and a DTI-based model were assessed individually and combined. RESULTS: The fractional anisotropy values from the enhancing region of glioblastomas were significantly higher than those of brain metastases (P < .01). There was no difference in mean diffusivity between the 2 tumor types. A classification model based on fractional anisotropy and mean diffusivity from the enhancing regions differentiated glioblastomas from brain metastases with an area under the receiver operating characteristic curve of 0.86, close to those obtained by 2 neuroradiologists using routine clinical images and DTI parameter maps (area under the curve = 0.90 and 0.85). The areas under the curve of the 2 radiologists were further improved to 0.96 and 0.93 by the addition of the DTI classification model. CONCLUSIONS: Classification models based on fractional anisotropy and mean diffusivity from the enhancing regions of the tumor can improve diagnostic performance in differentiating glioblastomas from brain metastases.

Cerebrovascular reactivity across the menstrual cycle in young healthy women.

This study evaluated the relationship of cerebrovascular reactivity in young healthy women with changes in concentrations of circulating ovarian hormones throughout the menstrual cycle. Nineteen healthy nulliparous, right-handed, regularly menstruating women (age 23-25 years) underwent color-coded duplex sonography of the common (CCA), internal (ICA) and external (ECA) carotid arteries on both sides. Mean blood flow velocity values measured before and ten minutes after intravenous administration of 1000 mg acetazolamide (ACE) were assessed in relation to the serum concentration of estrogen and progesterone on days 5, 13 and 26 of the cycle. After ACE administration flow velocity in the right CCA and ICA increased by 23% and 35% on day 5, 12% and 31% on day 13 and 30% and 47% on day 26 respectively, and the changes were significantly larger on the right side (F=6.793 and F=4.098 respectively; both p<0.05). Changes in blood flow velocity in the right CCA and ICA after ACE injection were significantly associated with ovarian hormone concentrations (F=3.828, P=0.028 and F=3.671, P=0.032 respectively). We conclude that cerebrovascular reactivity changes across the menstrual cycle are associated with ovarian steroid hormone changes, and are asymmetric. The results imply that vasculature of the right hemisphere may undergo cyclic vasodilation across the menstrual cycle and this effect should be considered in studies of cerebrovascular reactivity in women with migraine and mood disorders.

Incidental findings in youths volunteering for brain MRI research.

BACKGROUND AND PURPOSE: MRIs are obtained in research in healthy and clinical populations, and incidental findings have been reported. Most studies have examined adults with variability in parameters of image acquisition and clinical measures available. We conducted a prospective study of youths and documented the frequency and concomitants of incidental findings. MATERIALS AND METHODS: Youths (n = 1400) with an age range from 8-23 years were imaged on the same 3T scanner, with a standard acquisition protocol providing 1.0 mm(3) isotropic resolution of anatomic scans. All scans were reviewed by an experienced board-certified neuroradiologist and were categorized into 3 groups: 1) normal: no incidental findings; 2) coincidental: incidental finding(s) were noted, further reviewed with an experienced pediatric neuroradiologist, but were of no clinical significance; 3) incidental findings that on further review were considered to have potential clinical significance and participants were referred for appropriate clinical follow-up. RESULTS: Overall, 148 incidental findings (10.6% of sample) were noted, and of these, 12 required clinical follow-up. Incidental findings were not related to age. However, whites had a higher incidence of pineal cysts, and males had a higher incidence of cavum septum pellucidum, which was associated with psychosis-related symptoms. CONCLUSIONS: Incidental findings, moderated by race and sex, occur in approximately one-tenth of participants volunteering for pediatric research, with few requiring follow-up. The incidence supports a 2-tiered approach of neuroradiologic reading and clinical input to determine the potential significance of incidental findings detected on research MR imaging scans.

Multiple sclerosis: identification of temporal changes in brain lesions with computer-assisted detection software.

Multiple sclerosis (MS) is a chronic disease with a progressing and evolving course. Serial imaging with MRI is the mainstay in monitoring and managing MS patients. In this work we demonstrate the performance of a locally developed computer-assisted detection (CAD) software used to track temporal changes in brain MS lesions. CAD tracks changes in T2-bright MS lesions between two time points on a 3D high-resolution isotropic FLAIR MR sequence of the brain acquired at 3 Tesla. The program consists of an image-processing pipeline, and displays scrollable difference maps used as an aid to the neuroradiologist for assessing lesional change. To assess the value of the software we have compared diagnostic accuracy and duration of interpretation of the CAD-assisted and routine clinical interpretations in 98 randomly chosen, paired MR examinations from 88 patients (68 women, 20 men, mean age 43.5, age range 21-75) with a diagnosis of definite MS. The ground truth was determined by a three-expert panel. In case-wise analysis, CAD interpretation showed higher sensitivity than a clinical report (87% vs 77%, respectively). Lesion-wise analysis demonstrated improved sensitivity of CAD over a routine clinical interpretation of 40%-48%. Mean software-assisted interpretation time was 2.7 min. Our study demonstrates the potential of including CAD software in the workflow of neuroradiology practice for the detection of MS lesional change. Automated quantification of temporal change in MS lesion load may also be used in clinical research, e.g., in drug trials.

A Bayesian diagnostic system to differentiate glioblastomas from solitary brain metastases.

This paper aimed to construct a Bayesian network-based decision support system to differentiate glioblastomas from solitary metastases, based on multimodality MR examination. We enrolled 51 patients with solitary brain tumors (26 with glioblastomas and 25 with solitary brain metastases). These patients underwent contrast-enhanced T1-weighted magnetic resonance (MR) examination, diffusion tensor imaging (DTI), dynamic susceptibility contrast (DSC) MRI, and fluid-attenuated inversion recovery (FLAIR). We generated a set of MR biomarkers, including relative cerebral blood volume in the enhancing region, and fractional anisotropy measured in the immediate peritumoral area. We then generated a Bayesian network model to represent associations among these imaging-derived predictors, and the group membership variable, (glioblastoma or solitary metastasis). This Bayesian network can be used to classify new patients' tumors based on their MR appearance. The Bayesian network model accurately differentiated glioblastomas from solitary metastases. Prediction accuracy was 0.94 (sensitivity = 0.96, specificity = 0.92) based on leave-one-out cross-validation. The area under the receiver operating characteristic curve was 0.90. A Bayesian network-based decision support system accurately differentiates glioblastomas from solitary metastases, based on MR-derived biomarkers.

Sickle cell anemia: reference values of cerebral blood flow determined by continuous arterial spin labeling MRI.

Sickle cell anemia (SCA) is a chronic illness associated with progressive deterioration in patients' quality of life. The major complications of SCA are cerebrovascular accidents (CVA) such as asymptomatic cerebral infarct or overt stroke. The risk of CVA may be related to chronic disturbances in cerebral blood flow (CBF), but the thresholds of "normal" steady-state CBF are not well established. The reference tolerance limits of CBF can be useful to estimate the risk of CVA in asymptomatic children with SCA, who are negative for hyperemia or evidence of arterial narrowing. Continuous arterial spin labeling (CASL) MR perfusion allows for non-invasive quantification of global and regional CBF. To establish such reference tolerance limits we performed CASL MR examinations on a 3-Tesla MR scanner in a carefully selected cohort of 42 children with SCA (mean age, 8.1±3.3 years; range limits, 2.3-14.4 years; 24 females), who were not on chronic transfusion therapy, had no history of overt stroke or transient ischemic attack, were free of signs and symptoms of focal vascular territory ischemic brain injury, did not have intracranial arterial narrowing on MR angiography and were at low risk for stroke as determined by transcranial Doppler ultrasonography.

Vascular distribution of glioblastoma multiforme at diagnosis.

Treatment of high-grade gliomas with selective intra-arterial (IA) administration of chemotherapies has been proposed, and utilized as a therapeutic modality. This approach offers the conceptual benefit of providing maximal delivery of the agent to the tumor bed, while potentially reducing systemic exposure to the agent. This retrospective study was designed to determine the vascular distribution of glioblastoma multiforme (GBM) at the time of diagnosis in an effort to determine what proportion of patients would likely be candidates for this approach. The preoperative MRI scans of 50 patients with GBM were analyzed and compared to published normative data of intracranial vascular distribution. Vascular distribution was determined by analyzing post-gadolinium axial and coronal T1 images, axial T2 images, and axial T2 images with an additional 1 cm margin (T2 + 1 cm) added in all dimensions. T1 analysis demonstrated 60% of tumors in a single vascular distribution. T2 analysis of these tumors reduced that number to 34%. When the T2 + 1 cm margin was utilized, only 6% of tumors were in a single vascular distribution. 66% of tumors were limited to the anterior circulation on T1 imaging but only 34% on T2 + 1 cm imaging. 30% of tumors were also within the distribution of the anterior choroidal artery. These findings suggest that the use of selective IA administration of agents is necessarily limited to a fraction of presenting patients or will require administration via multiple cerebral arteries.

Differentiation between oligodendroglioma genotypes using dynamic susceptibility contrast perfusion-weighted imaging and proton MR spectroscopy.

BACKGROUND AND PURPOSE: Oligodendrogliomas with 1p/19q chromosome LOH are more sensitive to chemoradiation therapy than those with intact alleles. The usefulness of dynamic susceptibility contrast-PWI-guided ¹H-MRS in differentiating these 2 genotypes was tested in this study. MATERIALS AND METHODS: Forty patients with oligodendrogliomas, 1p/19q LOH (n = 23) and intact alleles (n = 17), underwent MR imaging and 2D-¹H-MRS. ¹H-MRS VOI was overlaid on FLAIR images to encompass the hyperintense abnormality on the largest cross-section of the neoplasm and then overlaid on CBV maps to coregister CBV maps with ¹H-MRS VOI. rCBVmax values were obtained by measuring the CBV from each of the selected ¹H-MRS voxels in the neoplasm and were normalized with respect to contralateral white matter. Metabolite ratios with respect to ipsilateral Cr were computed from the voxel corresponding to the rCBVmax value. Logistic regression and receiver operating characteristic analyses were performed to ascertain the best model to discriminate the 2 genotypes of oligodendrogliomas. Qualitative evaluation of conventional MR imaging characteristics (patterns of tumor border, signal intensity, contrast enhancement, and paramagnetic susceptibility effect) was also performed to distinguish the 2 groups of oligodendrogliomas. RESULTS: The incorporation of rCBVmax value and metabolite ratios (NAA/Cr, Cho/Cr, Glx/Cr, myo-inositol/Cr, and lipid + lactate/Cr) into the multivariate logistic regression model provided the best discriminatory classification with sensitivity (82.6%), specificity (64.7%), and accuracy (72%) in distinguishing 2 oligodendroglioma genotypes. Oligodendrogliomas with 1p/19q LOH were also more associated with paramagnetic susceptibility effect (P < .05). CONCLUSIONS: Our preliminary results indicate the potential of combing PWI and ¹H-MRS to distinguish oligodendroglial genotypes.

Survival analysis of patients with high-grade gliomas based on data mining of imaging variables.

BACKGROUND AND PURPOSE: The prediction of prognosis in HGGs is poor in the majority of patients. Our aim was to test whether multivariate prediction models constructed by machine-learning methods provide a more accurate predictor of prognosis in HGGs than histopathologic classification. The prediction of survival was based on DTI and rCBV measurements as an adjunct to conventional imaging. MATERIALS AND METHODS: The relationship of survival to 55 variables, including clinical parameters (age, sex), categoric or continuous tumor descriptors (eg, tumor location, extent of resection, multifocality, edema), and imaging characteristics in ROIs, was analyzed in a multivariate fashion by using data-mining techniques. A variable selection method was applied to identify the overall most important variables. The analysis was performed on 74 HGGs (18 anaplastic gliomas WHO grades III/IV and 56 GBMs or gliosarcomas WHO grades IV/IV). RESULTS: Five variables were identified as the most significant, including the extent of resection, mass effect, volume of enhancing tumor, maximum B0 intensity, and mean trace intensity in the nonenhancing/edematous region. These variables were used to construct a prediction model based on a J48 classification tree. The average classification accuracy, assessed by cross-validation, was 85.1%. Kaplan-Meier survival curves showed that the constructed prediction model classified malignant gliomas in a manner that better correlates with clinical outcome than standard histopathology. CONCLUSIONS: Prediction models based on data-mining algorithms can provide a more accurate predictor of prognosis in malignant gliomas than histopathologic classification alone.

Association of pulsatility index in the middle cerebral artery with intelligence quotient in children with sickle cell disease.

The aim of this study was to explore whether intellectual performance in children with Sickle Cell Disease and with low risk of stroke as determined with conventional transcranial Doppler ultrasonography (TCD) criteria was associated with hemodynamic parameters in imaging TCD, when controlling for hematological and socio-economical variables and presence of silent infarcts. We performed neuropsychological testing with Kaufman Brief Intelligence Test (K-BIT-IQ) and imaging TCD examinations to measure blood flow velocities and pulsatility indexes (PI) in the middle cerebral arteries (MCA) In 46 children with homozygous HbSS (mean age 108±34 months, range limits: 47-166 months; 24 females), without a history of stroke or transient ischemic attack, with no stenosis on magnetic resonance angiography and with velocities below 170 cm/s in screening conventional TCD. Mean K-BIT IQ Composite and Vocabulary scores (91±13 and 86±14 respectively) were significantly below the average scores of 100 for the age-matched population (one sample t-test=5.21, p<0.001). Using univariate and multivariate regression models, we found that lower PI in the right MCA was associated with lower K-BIT-IQ Composite and Vocabulary scores. Furthermore, we found that interhemispheric differences in PIs were even more strongly associated with neuropsychological performance, whereas flow velocities were not associated with the K-BIT-IQ score. Using a model of chronic anemia, we found that cognitive functioning was associated with cerebral hemodynamics.

Sickle cell disease in children: accuracy of imaging transcranial Doppler ultrasonography in detection of intracranial arterial stenosis.

This study aimed to determine the accuracy of imaging transcranial Doppler sonography in detection of intracranial arterial stenosis in children with sickle cell disease using three-dimensional MR angiography as a reference standard. Sixty-one children (mean age 102±39 months, 30 males), who had no history of overt stroke, and were classified as at lowest risk of stroke by mean flow velocity criterion <170 cm/s, underwent conventional and imaging transcranial Doppler ultrasonographic examinations. We employed the area under the receiver operating characteristic curve (AUC) to determine the accuracy of flow velocity measurements obtained with imaging ultrasonography with and without correction for the angle of insonation as well as with conventional ultrasonography. We also established the most efficacious velocity thresholds for detection of the stenosis. We found ten intracranial stenoses in six patients on MR angiography, but we calculated AUC only for detection of stenosis (n=6) of the left intracranial internal carotid artery. The accuracy of flow velocity with angle correction was lower than the accuracy of velocity without angle correction (AUC=0.73, 95% CI, 0.53-0.93 versus AUC=0.87, 95% CI, 0.74-1.00; p=0.017). The accuracy of flow velocity obtained with conventional ultrasonography (AUC=0.82, 95% CI, 0.67-0.97) was not different from the accuracy of flow velocities obtained with imaging ultrasonography. We found that the threshold of 165 cm/s of mean velocity without angle correction is associated with highest efficiency for imaging (92%) and conventional ultrasonography (90%). Velocity measurements without angle-correction provide good accuracy in detection of stenosis of the terminal internal carotid artery, whereas angle-corrected velocities have lower accuracy.

Vertebral and Basilar Arteries: Transcranial Color-Coded Duplex Ultrasonography versus Conventional TCD in Detection of Narrowings.

We prospectively compared the accuracies of conventional transcranial Doppler ultrasound (TCD) and transcranial color-coded duplex sonography (TCCS) in the diagnosis of narrowing of the basilar (BA) and vertebral arteries (VA). Fifty-six consecutive patients (mean age 55.8 years; 34 women) after subarachnoid hemorrhage (n=46), stroke or transient ischemic attack (n=5), and for other reasons (n=5) underwent on the same day TCD, TCCS and the intra-arterial digital subtraction angiography (DSA) - the reference standard. The accuracy of peak-systolic (VPS), mean (VM), and end-diastolic velocities (VED) in detection of any arterial narrowing was estimated using the receiver operator characteristic (ROC) curve methodology and the total area (Az) under the curve. Accuracy of TCCS in detection of VA narrowing based on VPS and VM measurements was significantly higher than accuracy of TCD (Az=0.65 for VPS and Az=0.62 for VM versus Az=0.51 and Az=0.50, respectively, p<0.05 for both). Accuracy of TCCS in detection of BA narrowing was also higher than accuracy of TCD based on VPS measurements (Az=0.69 versus Az=0.50, respectively), with a trend toward significant difference, p=0.085. The accuracy of TCCS is superior to accuracy of TCD in detection of narrowings of vertebral and basilar arteries, thus TCCS should be preferred in routine clinical practice.

A validation study of multicenter diffusion tensor imaging: reliability of fractional anisotropy and diffusivity values.

BACKGROUND AND PURPOSE: DTI is increasingly being used as a measure to study tissue damage in several neurologic diseases. Our aim was to investigate the comparability of DTI measures between different MR imaging magnets and platforms. MATERIALS AND METHODS: Two healthy volunteers underwent DTI on five 3T MR imaging scanners (3 Trios and 2 Signas) by using a matched 33 noncollinear diffusion-direction pulse sequence. Within each subject, a total of 16 white matter (corpus callosum, periventricular, and deep white matter) and gray matter (cortical and deep gray) ROIs were drawn on a single image set and then were coregistered to the other images. Mean FA, ADC, and longitudinal and transverse diffusivities were calculated within each ROI. Concordance correlations were derived by comparing ROI DTI values among each of the 5 magnets. RESULTS: Mean concordance for FA was 0.96; for both longitudinal and transverse diffusivities, it was 0.93; and for ADC, it was 0.88. Mean scan-rescan concordance was 0.96-0.97 for all DTI measures. Concordance correlations within platforms were, in general, better than those between platforms for all DTI measures (mean concordance of 0.96). CONCLUSIONS: We found that a 3T magnet and high-angular-resolution pulse sequence yielded comparable DTI measurements across different MR imaging magnets and platforms. Our results indicate that FA is the most comparable measure across magnets, followed by individual diffusivities. The comparability of DTI measures between different magnets supports the feasibility of multicentered clinical trials by using DTI as an outcome measure.

Sickle cell disease: reference values and interhemispheric differences of nonimaging transcranial Doppler blood flow parameters.

BACKGROUND AND PURPOSE: TCD screening is widely used to identify children with SCD at high risk of stroke. Those with high mean flow velocities in major brain arteries have increased risk of stroke. Thus, our aim was to establish reference values of interhemispheric differences and ratios of blood flow Doppler parameters in the tICA, MCA, and ACA as determined by conventional TCD in children with sickle cell anemia. MATERIALS AND METHODS: Reference limits of blood flow parameters were established on the basis of a consecutive cohort of 56 children (mean age, 100 ± 40 months; range, 29-180 months; 30 females) free of neurologic deficits and intracranial stenosis detectable by MRA, with blood flow velocities <170 cm/s by conventional TCD. Reference limits were estimated by using tolerance intervals, within which are included with a probability of .90 of all possible data values from 95% of a population. RESULTS: Average peak systolic velocities were significantly higher in the right hemisphere in the MCA and ACA (185 ± 28 cm/s versus 179 ± 27 and 152 ± 30 cm/s versus 143 ± 34 cm/s respectively). Reference limits for left-to-right differences in the mean flow velocities were the following: -43 to 33 cm/s for the MCA; -49 to 38 cm/s for the ACA, and -38 to 34 cm/s for the tICA, respectively. Respective reference limits for left-to-right velocity ratios were the following: 0.72 to 1.25 cm/s for the MCA; 0.62 to 1.39 cm/s for the ACA, and 0.69 to 1.27 cm/s for the tICA. Flow velocities in major arteries were inversely related to age and Hct or Hgb. CONCLUSIONS: The study provides reference intervals of TCD flow velocities and their interhemispheric differences and ratios that may be helpful in identification of intracranial arterial stenosis in children with SCD undergoing sonographic screening for stroke prevention.

Differentiation between glioblastomas, solitary brain metastases, and primary cerebral lymphomas using diffusion tensor and dynamic susceptibility contrast-enhanced MR imaging.

BACKGROUND AND PURPOSE: Glioblastomas, brain metastases, and PCLs may have similar enhancement patterns on MR imaging, making the differential diagnosis difficult or even impossible. The purpose of this study was to determine whether a combination of DTI and DSC can assist in the differentiation of glioblastomas, solitary brain metastases, and PCLs. MATERIALS AND METHODS: Twenty-six glioblastomas, 25 brain metastases, and 16 PCLs were retrospectively identified. DTI metrics, including FA, ADC, CL, CP, CS, and rCBV were measured from the enhancing, immediate peritumoral and distant peritumoral regions. A 2-level decision tree was designed, and a multivariate logistic regression analysis was used at each level to determine the best model for classification. RESULTS: From the enhancing region, significantly elevated FA, CL, and CP and decreased CS values were observed in glioblastomas compared with brain metastases and PCLs (P < .001), whereas ADC, rCBV, and rCBV(max) values of glioblastomas were significantly higher than those of PCLs (P < .01). The best model to distinguish glioblastomas from nonglioblastomas consisted of ADC, CS (or FA) from the enhancing region, and rCBV from the immediate peritumoral region, resulting in AUC = 0.938. The best predictor to differentiate PCLs from brain metastases comprised ADC from the enhancing region and CP from the immediate peritumoral region with AUC = 0.909. CONCLUSIONS: The combination of DTI metrics and rCBV measurement can help in the differentiation of glioblastomas from brain metastases and PCLs.