Intracranial Hemorrhage in Term and Late-Preterm Neonates: An Institutional Perspective

BACKGROUND AND PURPOSE

Distribution of intracranial hemorrhage in term and late-preterm neonates is relatively unexplored. This descriptive study examines the MR imaging-detectable spectrum of intracranial hemorrhage in this population and potential risk factors.

MATERIALS AND METHODS

Prevalence and distribution of intracranial hemorrhage in consecutive term/late-preterm neonates who underwent brain MR imaging between January 2011 to August 2018 were assessed. MRIs were analyzed to determine intracranial hemorrhage distribution (intraventricular, subarachnoid, subdural, intraparenchymal, and subpial/leptomeningeal), and chart review was performed for potential clinical risk factors.

RESULTS

Of 725 term/late-preterm neonates who underwent brain MR imaging, intracranial hemorrhage occurred in 63 (9%). Fifty-two (83%) had multicompartment intracranial hemorrhage. Intraventricular and subdural were the most common hemorrhage locations, found in 41 (65%) and 39 (62%) neonates, respectively. Intraparenchymal hemorrhage occurred in 33 (52%); subpial, in 19 (30%); subarachnoid, in 12 (19%); and epidural, in 2 (3%) neonates. Twenty infants (32%) were delivered via cesarean delivery, and 5 (8%), via instrumented delivery. Cortical vein thromboses were present in 34 (54%); periventricular or medullary vein thromboses, in 37 (59%); and cerebral venous sinus thrombosis, in 5 (8%). Thirty-seven (59%) had elevated markers of coagulopathy (international normalized ratio > 1.2, fibrinogen level < 234), 9 (14%) had a clinically meaningful elevation in the international normalized ratio (>1.4), and 3 (5%) had a clinically meaningful decrease in the fibrinogen level (<150). Three (5%) neonates had thrombocytopenia (platelet count < 100 × 103/μL).

CONCLUSIONS

While relatively infrequent, there was a wide distribution of intracranial hemorrhage in term and late-preterm infants; intraventricular and subdural hemorrhages were the most common types. We report a high prevalence of venous congestion or thromboses accompanying neonatal intracranial hemorrhage.

Radiomics Can Distinguish Pediatric Supratentorial Embryonal Tumors, High-Grade Gliomas, and Ependymomas

BACKGROUND AND PURPOSE

Pediatric supratentorial tumors such as embryonal tumors, high-grade gliomas, and ependymomas are difficult to distinguish by histopathology and imaging because of overlapping features. We applied machine learning to uncover MR imaging-based radiomics phenotypes that can differentiate these tumor types.

MATERIALS AND METHODS

Our retrospective cohort of 231 patients from 7 participating institutions had 50 embryonal tumors, 127 high-grade gliomas, and 54 ependymomas. For each tumor volume, we extracted 900 Image Biomarker Standardization Initiative-based PyRadiomics features from T2-weighted and gadolinium-enhanced T1-weighted images. A reduced feature set was obtained by sparse regression analysis and was used as input for 6 candidate classifier models. Training and test sets were randomly allocated from the total cohort in a 75:25 ratio.

RESULTS

The final classifier model for embryonal tumor-versus-high-grade gliomas identified 23 features with an area under the curve of 0.98; the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 0.85, 0.91, 0.79, 0.94, and 0.89, respectively. The classifier for embryonal tumor-versus-ependymomas identified 4 features with an area under the curve of 0.82; the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 0.93, 0.69, 0.76, 0.90, and 0.81, respectively. The classifier for high-grade gliomas-versus-ependymomas identified 35 features with an area under the curve of 0.96; the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 0.82, 0.94, 0.82, 0.94, and 0.91, respectively.

CONCLUSIONS

In this multi-institutional study, we identified distinct radiomic phenotypes that distinguish pediatric supratentorial tumors, high-grade gliomas, and ependymomas with high accuracy. Incorporation of this technique in diagnostic algorithms can improve diagnosis, risk stratification, and treatment planning.

Radiomic Phenotypes Distinguish Atypical Teratoid/Rhabdoid Tumors from Medulloblastoma

BACKGROUND AND PURPOSE

Atypical teratoid/rhabdoid tumors and medulloblastomas have similar imaging and histologic features but distinctly different outcomes. We hypothesized that they could be distinguished by MR imaging-based radiomic phenotypes.

MATERIALS AND METHODS

We retrospectively assembled T2-weighted and gadolinium-enhanced T1-weighted images of 48 posterior fossa atypical teratoid/rhabdoid tumors and 96 match-paired medulloblastomas from 7 institutions. Using a holdout test set, we measured the performance of 6 candidate classifier models using 6 imaging features derived by sparse regression of 900 T2WI and 900 T1WI Imaging Biomarker Standardization Initiative-based radiomics features.

RESULTS

From the originally extracted 1800 total Imaging Biomarker Standardization Initiative-based features, sparse regression consistently reduced the feature set to 1 from T1WI and 5 from T2WI. Among classifier models, logistic regression performed with the highest AUC of 0.86, with sensitivity, specificity, accuracy, and F1 scores of 0.80, 0.82, 0.81, and 0.85, respectively. The top 3 important Imaging Biomarker Standardization Initiative features, by decreasing order of relative contribution, included voxel intensity at the 90th percentile, inverse difference moment normalized, and kurtosis-all from T2WI.

CONCLUSIONS

Six quantitative signatures of image intensity, texture, and morphology distinguish atypical teratoid/rhabdoid tumors from medulloblastomas with high prediction performance across different machine learning strategies. Use of this technique for preoperative diagnosis of atypical teratoid/rhabdoid tumors could significantly inform therapeutic strategies and patient care discussions.

Radiomics of Pediatric Low-Grade Gliomas: Toward a Pretherapeutic Differentiation of BRAF-Mutated and BRAF-Fused Tumors

BACKGROUND AND PURPOSE

B-Raf proto-oncogene, serine/threonine kinase (BRAF) status has important implications for prognosis and therapy of pediatric low-grade gliomas. Currently, BRAF status classification relies on biopsy. Our aim was to train and validate a radiomics approach to predict BRAF fusion and BRAF V600E mutation.

MATERIALS AND METHODS

In this bi-institutional retrospective study, FLAIR MR imaging datasets of 115 pediatric patients with low-grade gliomas from 2 children's hospitals acquired between January 2009 and January 2016 were included and analyzed. Radiomics features were extracted from tumor segmentations, and the predictive model was tested using independent training and testing datasets, with all available tumor types. The model was selected on the basis of a grid search on the number of trees, opting for the best split for a random forest. We used the area under the receiver operating characteristic curve to evaluate model performance.

RESULTS

The training cohort consisted of 94 pediatric patients with low-grade gliomas (mean age, 9.4 years; 45 boys), and the external validation cohort comprised 21 pediatric patients with low-grade gliomas (mean age, 8.37 years; 12 boys). A 4-fold cross-validation scheme predicted BRAF status with an area under the curve of 0.75 (SD, 0.12) (95% confidence interval, 0.62-0.89) on the internal validation cohort. By means of the optimal hyperparameters determined by 4-fold cross-validation, the area under the curve for the external validation was 0.85. Age and tumor location were significant predictors of BRAF status (P values = .04 and <.001, respectively). Sex was not a significant predictor (P value = .96).

CONCLUSIONS

Radiomics-based prediction of BRAF status in pediatric low-grade gliomas appears feasible in this bi-institutional exploratory study.

Deep Learning for Pediatric Posterior Fossa Tumor Detection and Classification: A Multi-Institutional Study

BACKGROUND AND PURPOSE

Posterior fossa tumors are the most common pediatric brain tumors. MR imaging is key to tumor detection, diagnosis, and therapy guidance. We sought to develop an MR imaging-based deep learning model for posterior fossa tumor detection and tumor pathology classification.

MATERIALS AND METHODS

The study cohort comprised 617 children (median age, 92 months; 56% males) from 5 pediatric institutions with posterior fossa tumors: diffuse midline glioma of the pons (n = 122), medulloblastoma (n = 272), pilocytic astrocytoma (n = 135), and ependymoma (n = 88). There were 199 controls. Tumor histology served as ground truth except for diffuse midline glioma of the pons, which was primarily diagnosed by MR imaging. A modified ResNeXt-50-32x4d architecture served as the backbone for a multitask classifier model, using T2-weighted MRIs as input to detect the presence of tumor and predict tumor class. Deep learning model performance was compared against that of 4 radiologists.

RESULTS

Model tumor detection accuracy exceeded an AUROC of 0.99 and was similar to that of 4 radiologists. Model tumor classification accuracy was 92% with an F₁ score of 0.80. The model was most accurate at predicting diffuse midline glioma of the pons, followed by pilocytic astrocytoma and medulloblastoma. Ependymoma prediction was the least accurate. Tumor type classification accuracy and F₁ score were higher than those of 2 of the 4 radiologists.

CONCLUSIONS

We present a multi-institutional deep learning model for pediatric posterior fossa tumor detection and classification with the potential to augment and improve the accuracy of radiologic diagnosis.

Variable Refocusing Flip Angle Single-Shot Imaging for Sedation-Free Fast Brain MRI

BACKGROUND AND PURPOSE

Conventional single-shot FSE commonly used for fast MRI may be suboptimal for brain evaluation due to poor image contrast, SNR, or image blurring. We investigated the clinical performance of variable refocusing flip angle single-shot FSE, a variation of single-shot FSE with lower radiofrequency energy deposition and potentially faster acquisition time, as an alternative approach to fast brain MR imaging.

MATERIALS AND METHODS

We retrospectively compared half-Fourier single-shot FSE with half- and full-Fourier variable refocusing flip angle single-shot FSE in 30 children. Three readers reviewed images for motion artifacts, image sharpness at the brain-fluid interface, and image sharpness/tissue contrast at gray-white differentiation on a modified 5-point Likert scale. Two readers also evaluated full-Fourier variable refocusing flip angle single-shot FSE against T2-FSE for brain lesion detectability in 38 children.

RESULTS

Variable refocusing flip angle single-shot FSE sequences showed more motion artifacts (P < .001). Variable refocusing flip angle single-shot FSE sequences scored higher regarding image sharpness at brain-fluid interfaces (P < .001) and gray-white differentiation (P < .001). Acquisition times for half- and full-Fourier variable refocusing flip angle single-shot FSE were faster than for single-shot FSE (P < .001) with a 53% and 47% reduction, respectively. Intermodality agreement between full-Fourier variable refocusing flip angle single-shot FSE and T2-FSE findings was near-perfect (κ = 0.90, κ = 0.95), with an 8% discordance rate for ground truth lesion detection.

CONCLUSIONS

Variable refocusing flip angle single-shot FSE achieved 2× faster scan times than single-shot FSE with improved image sharpness at brain-fluid interfaces and gray-white differentiation. Such improvements are likely attributed to a combination of improved contrast, spatial resolution, SNR, and reduced T2-decay associated with blurring. While variable refocusing flip angle single-shot FSE may be a useful alternative to single-shot FSE and, potentially, T2-FSE when faster scan times are desired, motion artifacts were more common in variable refocusing flip angle single-shot FSE, and, thus, they remain an important consideration before clinical implementation.

MR Imaging-Based Radiomic Signatures of Distinct Molecular Subgroups of Medulloblastoma

BACKGROUND AND PURPOSE

Distinct molecular subgroups of pediatric medulloblastoma confer important differences in prognosis and therapy. Currently, tissue sampling is the only method to obtain information for classification. Our goal was to develop and validate radiomic and machine learning approaches for predicting molecular subgroups of pediatric medulloblastoma.

MATERIALS AND METHODS

In this multi-institutional retrospective study, we evaluated MR imaging datasets of 109 pediatric patients with medulloblastoma from 3 children's hospitals from January 2001 to January 2014. A computational framework was developed to extract MR imaging-based radiomic features from tumor segmentations, and we tested 2 predictive models: a double 10-fold cross-validation using a combined dataset consisting of all 3 patient cohorts and a 3-dataset cross-validation, in which training was performed on 2 cohorts and testing was performed on the third independent cohort. We used the Wilcoxon rank sum test for feature selection with assessment of area under the receiver operating characteristic curve to evaluate model performance.

RESULTS

Of 590 MR imaging-derived radiomic features, including intensity-based histograms, tumor edge-sharpness, Gabor features, and local area integral invariant features, extracted from imaging-derived tumor segmentations, tumor edge-sharpness was most useful for predicting sonic hedgehog and group 4 tumors. Receiver operating characteristic analysis revealed superior performance of the double 10-fold cross-validation model for predicting sonic hedgehog, group 3, and group 4 tumors when using combined T1- and T2-weighted images (area under the curve = 0.79, 0.70, and 0.83, respectively). With the independent 3-dataset cross-validation strategy, select radiomic features were predictive of sonic hedgehog (area under the curve = 0.70-0.73) and group 4 (area under the curve = 0.76-0.80) medulloblastoma.

CONCLUSIONS

This study provides proof-of-concept results for the application of radiomic and machine learning approaches to a multi-institutional dataset for the prediction of medulloblastoma subgroups.

Brain Diffusion Abnormalities in Children with Tension-Type and Migraine-Type Headaches

BACKGROUND AND PURPOSE

Tension-type and migraine-type headaches are the most common chronic paroxysmal disorders of childhood. The goal of this study was to compare regional cerebral volumes and diffusion in tension-type and migraine-type headaches against published controls.

MATERIALS AND METHODS

Patients evaluated for tension-type or migraine-type headache without aura from May 2014 to July 2016 in a single center were retrospectively reviewed. Thirty-two patients with tension-type headache and 23 with migraine-type headache at an average of 4 months after diagnosis were enrolled. All patients underwent DWI at 3T before the start of pharmacotherapy. Using atlas-based DWI analysis, we determined regional volumetric and diffusion properties in the cerebral cortex, thalamus, caudate, putamen, globus pallidus, hippocampus, amygdala, nucleus accumbens, brain stem, and cerebral white matter. Multivariate analysis of covariance was used to test for differences between controls and patients with tension-type and migraine-type headaches.

RESULTS

There were no significant differences in regional brain volumes between the groups. Patients with tension-type and migraine-type headaches showed significantly increased ADC in the hippocampus and brain stem compared with controls. Additionally, only patients with migraine-type headache showed significantly increased ADC in the thalamus and a trend toward increased ADC in the amygdala compared with controls.

CONCLUSIONS

This study identifies early cerebral diffusion changes in patients with tension-type and migraine-type headaches compared with controls. The hypothesized mechanisms of nociception in migraine-type and tension-type headaches may explain the findings as a precursor to structural changes seen in adult patients with chronic headache.

Radiomics in Brain Tumor: Image Assessment, Quantitative Feature Descriptors, and Machine-Learning Approaches

Radiomics describes a broad set of computational methods that extract quantitative features from radiographic images. The resulting features can be used to inform imaging diagnosis, prognosis, and therapy response in oncology. However, major challenges remain for methodologic developments to optimize feature extraction and provide rapid information flow in clinical settings. Equally important, to be clinically useful, predictive radiomic properties must be clearly linked to meaningful biologic characteristics and qualitative imaging properties familiar to radiologists. Here we use a cross-disciplinary approach to highlight studies in radiomics. We review brain tumor radiologic studies (eg, imaging interpretation) through computational models (eg, computer vision and machine learning) that provide novel clinical insights. We outline current quantitative image feature extraction and prediction strategies with different levels of available clinical classes for supporting clinical decision-making. We further discuss machine-learning challenges and data opportunities to advance radiomic studies.

Gray Matter Growth Is Accompanied by Increasing Blood Flow and Decreasing Apparent Diffusion Coefficient during Childhood

BACKGROUND AND PURPOSE

Normal values of gray matter volume, cerebral blood flow, and water diffusion have not been established for healthy children. We sought to determine reference values for age-dependent changes of these parameters in healthy children.

MATERIALS AND METHODS

We retrospectively reviewed MR imaging data from 100 healthy children. Using an atlas-based approach, age-related normal values for regional CBF, apparent diffusion coefficient, and volume were determined for the cerebral cortex, hippocampus, thalamus, caudate, putamen, globus pallidus, amygdala, and nucleus accumbens.

RESULTS

All gray matter structures grew rapidly before the age of 10 years and then plateaued or slightly declined thereafter. The ADC of all structures decreased with age, with the most rapid changes occurring prior to the age of 5 years. With the exception of the globus pallidus, CBF increased rather linearly with age.

CONCLUSIONS

Normal brain gray matter is characterized by rapid early volume growth and increasing CBF with concomitantly decreasing ADC. The extracted reference data that combine CBF and ADC parameters during brain growth may provide a useful resource when assessing pathologic changes in children.

Computational Identification of Tumor Anatomic Location Associated with Survival in 2 Large Cohorts of Human Primary Glioblastomas

BACKGROUND AND PURPOSE

Tumor location has been shown to be a significant prognostic factor in patients with glioblastoma. The purpose of this study was to characterize glioblastoma lesions by identifying MR imaging voxel-based tumor location features that are associated with tumor molecular profiles, patient characteristics, and clinical outcomes.

MATERIALS AND METHODS

Preoperative T1 anatomic MR images of 384 patients with glioblastomas were obtained from 2 independent cohorts (n = 253 from the Stanford University Medical Center for training and n = 131 from The Cancer Genome Atlas for validation). An automated computational image-analysis pipeline was developed to determine the anatomic locations of tumor in each patient. Voxel-based differences in tumor location between good (overall survival of >17 months) and poor (overall survival of <11 months) survival groups identified in the training cohort were used to classify patients in The Cancer Genome Atlas cohort into 2 brain-location groups, for which clinical features, messenger RNA expression, and copy number changes were compared to elucidate the biologic basis of tumors located in different brain regions.

RESULTS

Tumors in the right occipitotemporal periventricular white matter were significantly associated with poor survival in both training and test cohorts (both, log-rank P < .05) and had larger tumor volume compared with tumors in other locations. Tumors in the right periatrial location were associated with hypoxia pathway enrichment and PDGFRA amplification, making them potential targets for subgroup-specific therapies.

CONCLUSIONS

Voxel-based location in glioblastoma is associated with patient outcome and may have a potential role for guiding personalized treatment.

Hydrocephalus decreases arterial spin-labeled cerebral perfusion

BACKGROUND AND PURPOSE

Reduced cerebral perfusion has been observed with elevated intracranial pressure. We hypothesized that arterial spin-labeled CBF can be used as a marker for symptomatic hydrocephalus.

MATERIALS AND METHODS

We compared baseline arterial spin-labeled CBF in 19 children (median age, 6.5 years; range, 1-17 years) with new posterior fossa brain tumors and clinical signs of intracranial hypertension with arterial spin-labeled CBF in 16 age-matched controls and 4 patients with posterior fossa tumors without ventriculomegaly or signs of intracranial hypertension. Measurements were recorded in the cerebrum at the vertex, deep gray nuclei, and periventricular white matter and were assessed for a relationship to ventricular size. In 16 symptomatic patients, we compared cerebral perfusion before and after alleviation of hydrocephalus.

RESULTS

Patients with uncompensated hydrocephalus had lower arterial spin-labeled CBF than healthy controls for all brain regions interrogated (P < .001). No perfusion difference was seen between asymptomatic patients with posterior fossa tumors and healthy controls (P = 1.000). The median arterial spin-labeled CBF increased after alleviation of obstructive hydrocephalus (P < .002). The distance between the frontal horns inversely correlated with arterial spin-labeled CBF of the cerebrum (P = .036) but not the putamen (P = .156), thalamus (P = .111), or periventricular white matter (P = .121).

CONCLUSIONS

Arterial spin-labeled-CBF was reduced in children with uncompensated hydrocephalus and restored after its alleviation. Arterial spin-labeled-CBF perfusion MR imaging may serve a future role in the neurosurgical evaluation of hydrocephalus, as a potential noninvasive method to follow changes of intracranial pressure with time.

MRI surrogates for molecular subgroups of medulloblastoma

BACKGROUND AND PURPOSE

Recently identified molecular subgroups of medulloblastoma have shown potential for improved risk stratification. We hypothesized that distinct MR imaging features can predict these subgroups.

MATERIALS AND METHODS

All patients with a diagnosis of medulloblastoma at one institution, with both pretherapy MR imaging and surgical tissue, served as the discovery cohort (n = 47). MR imaging features were assessed by 3 blinded neuroradiologists. NanoString-based assay of tumor tissues was conducted to classify the tumors into the 4 established molecular subgroups (wingless, sonic hedgehog, group 3, and group 4). A second pediatric medulloblastoma cohort (n = 52) from an independent institution was used for validation of the MR imaging features predictive of the molecular subtypes.

RESULTS

Logistic regression analysis within the discovery cohort revealed tumor location (P < .001) and enhancement pattern (P = .001) to be significant predictors of medulloblastoma subgroups. Stereospecific computational analyses confirmed that group 3 and 4 tumors predominated within the midline fourth ventricle (100%, P = .007), wingless tumors were localized to the cerebellar peduncle/cerebellopontine angle cistern with a positive predictive value of 100% (95% CI, 30%-100%), and sonic hedgehog tumors arose in the cerebellar hemispheres with a positive predictive value of 100% (95% CI, 59%-100%). Midline group 4 tumors presented with minimal/no enhancement with a positive predictive value of 91% (95% CI, 59%-98%). When we used the MR imaging feature-based regression model, 66% of medulloblastomas were correctly predicted in the discovery cohort, and 65%, in the validation cohort.

CONCLUSIONS

Tumor location and enhancement pattern were predictive of molecular subgroups of pediatric medulloblastoma and may potentially serve as a surrogate for genomic testing.

Diffusion-weighted imaging with dual-echo echo-planar imaging for better sensitivity to acute stroke

BACKGROUND AND PURPOSE

Parallel imaging facilitates the acquisition of echo-planar images with a reduced TE, enabling the incorporation of an additional image at a later TE. Here we investigated the use of a parallel imaging-enhanced dual-echo EPI sequence to improve lesion conspicuity in diffusion-weighted imaging.

MATERIALS AND METHODS

Parallel imaging-enhanced dual-echo DWI data were acquired in 50 consecutive patients suspected of stroke at 1.5T. The dual-echo acquisition included 2 EPI for 1 diffusion-preparation period (echo 1 [TE = 48 ms] and echo 2 [TE = 105 ms]). Three neuroradiologists independently reviewed the 2 echoes by using the routine DWI of our institution as a reference. Images were graded on lesion conspicuity, diagnostic confidence, and image quality. The apparent diffusion coefficient map from echo 1 was used to validate the presence of acute infarction. Relaxivity maps calculated from the 2 echoes were evaluated for potential complementary information.

RESULTS

Echo 1 and 2 DWIs were rated as better than the reference DWI. While echo 1 had better image quality overall, echo 2 was unanimously favored over both echo 1 and the reference DWI for its high sensitivity in detecting acute infarcts.

CONCLUSIONS

Parallel imaging-enhanced dual-echo diffusion-weighted EPI is a useful method for evaluating lesions with reduced diffusivity. The long TE of echo 2 produced DWIs that exhibited superior lesion conspicuity compared with images acquired at a shorter TE. Echo 1 provided higher SNR ADC maps for specificity to acute infarction. The relaxivity maps may serve to complement information regarding blood products and mineralization.

Time-dependent structural changes of the dentatothalamic pathway in children treated for posterior fossa tumor

BACKGROUND AND PURPOSE

Injury to the dentatothalamic pathway that originates in the cerebellum has been suggested as a mechanism for neurologic complications in children treated for posterior fossa tumors. We hypothesized that time-dependent changes occur in the dentatothalamic pathway.

MATERIALS AND METHODS

Diffusion tensor evaluation was performed in 14 children (median age, 4.1 years; age range, 1-20 years) who underwent serial MR imaging at 3T as part of routine follow-up after posterior fossa tumor resection with or without adjuvant therapy. Tensor metrics were obtained in the acute (≤1 week), subacute (1 to <6 months), and chronic (≥6 months) periods after surgery. We evaluated the following dentatothalamic constituents: bilateral dentate nuclei, cerebellar white matter, and superior cerebellar peduncles. Serial dentate nuclei volumes were also obtained and compared with the patient's baseline.

RESULTS

The most significant tensor changes to the superior cerebellar peduncles and cerebellar white matter occurred in the subacute period, regardless of the tumor pathology or therapy regimen, with signs of recovery in the chronic period. However, chronic volume loss and reduced mean diffusivity were observed in the dentate nuclei and did not reverse. This atrophy was associated with radiation therapy and symptoms of ataxia.

CONCLUSIONS

Longitudinal diffusion MR imaging in children treated for posterior fossa tumors showed time-dependent tensor changes in components of the dentatothalamic pathway that suggest evolution of structural damage with inflammation and recovery of tissue directionality. However, the dentate nuclei did not show tensor or volumetric recovery, suggesting that the injury may be chronic.

Improved T2* imaging without increase in scan time: SWI processing of 2D gradient echo

BACKGROUND AND PURPOSE

2D gradient-echo imaging is sensitive to T2* lesions (hemorrhages, mineralization, and vascular lesions), and susceptibility-weighted imaging is even more sensitive, but at the cost of additional scan time (SWI: 5-10 minutes; 2D gradient-echo: 2 minutes). The long acquisition time of SWI may pose challenges in motion-prone patients. We hypothesized that 2D SWI/phase unwrapped images processed from 2D gradient-echo imaging could improve T2* lesion detection.

MATERIALS AND METHODS

2D gradient-echo brain images of 50 consecutive pediatric patients (mean age, 8 years) acquired at 3T were retrospectively processed to generate 2D SWI/phase unwrapped images. The 2D gradient-echo and 2D SWI/phase unwrapped images were compared for various imaging parameters and were scored in a blinded fashion.

RESULTS

Of 50 patients, 2D gradient-echo imaging detected T2* lesions in 29 patients and had normal findings in 21 patients. 2D SWI was more sensitive than standard 2D gradient-echo imaging in detecting T2* lesions (P < .0001). 2D SWI/phase unwrapped imaging also improved delineation of normal venous structures and nonpathologic calcifications and helped distinguish calcifications from hemorrhage. A few pitfalls of 2D SWI/phase unwrapped imaging were noted, including worsened motion and dental artifacts and challenges in detecting T2* lesions adjacent to calvaria or robust deoxygenated veins.

CONCLUSIONS

2D SWI and associated phase unwrapped images processed from standard 2D gradient-echo images were more sensitive in detecting T2* lesions and delineating normal venous structures and nonpathologic mineralization, and they also helped distinguish calcification at no additional scan time. SWI processing of 2D gradient-echo images may be a useful adjunct in cases in which longer scan times of 3D SWI are difficult to implement.

Reduced cerebral arterial spin-labeled perfusion in children with neurofibromatosis type 1

BACKGROUND AND PURPOSE

Neurofibromatosis type 1 is associated with increased risk for stroke, cerebral vasculopathy, and neurocognitive deficits, but underlying hemodynamic changes in asymptomatic children remain poorly understood. We hypothesized that children with neurofibromatosis type 1 have decreased cerebral blood flow.

MATERIALS AND METHODS

Arterial spin-labeled CBF was measured in 14 children with neurofibromatosis type 1 (median age, 9.7 years; mean, 10.2 years; range, 22 months to 18 years) and compared with age-matched control subjects on 3T MR imaging. Three-dimensional pseudocontinuous spin-echo arterial spin-labeled technique was used. Measurements were obtained at cortical gray matter of bilateral cerebral hemispheres and centrum semiovale by use of the ROI method. Comparison by Mann-Whitney test was used, with Bonferroni-adjusted P values ≤.004 judged as significant.

RESULTS

We identified 7 of 12 areas with significantly diminished arterial spin-labeled CBF in patients with neurofibromatosis type 1 compared with control subjects. These areas included the anterior cingulate gyrus (P = .001), medial frontal cortex (P = .004), centrum semiovale (P = .004), temporo-occipital cortex (P = .002), thalamus (P = .001), posterior cingulate gyrus (P = .002), and occipital cortex (P = .001). Among patients with neurofibromatosis type 1, there were no significant differences in these regions on the basis of the presence of neurofibromatosis type 1 spots or neurocognitive deficits.

CONCLUSIONS

Reduced cerebral perfusion was seen in children with neurofibromatosis type 1, particularly in the posterior circulation and the vascular borderzones of the middle and posterior cerebral arteries.

Arterial spin-labeled perfusion of pediatric brain tumors

BACKGROUND AND PURPOSE

Pediatric brain tumors have diverse pathologic features, which poses diagnostic challenges. Although perfusion evaluation of adult tumors is well established, hemodynamic properties are not well characterized in children. Our goal was to apply arterial spin-labeling perfusion for various pathologic types of pediatric brain tumors and evaluate the role of arterial spin-labeling in the prediction of tumor grade.

MATERIALS AND METHODS

Arterial spin-labeling perfusion of 54 children (mean age, 7.5 years; 33 boys and 21 girls) with treatment-naive brain tumors was retrospectively evaluated. The 3D pseudocontinuous spin-echo arterial spin-labeling technique was acquired at 3T MR imaging. Maximal relative tumor blood flow was obtained by use of the ROI method and was compared with tumor histologic features and grade.

RESULTS

Tumors consisted of astrocytic (20), embryonal (11), ependymal (3), mixed neuronal-glial (8), choroid plexus (5), craniopharyngioma (4), and other pathologic types (3). The maximal relative tumor blood flow of high-grade tumors (grades III and IV) was significantly higher than that of low-grade tumors (grades I and II) (P < .001). There was a wider relative tumor blood flow range among high-grade tumors (2.14 ± 1.78) compared with low-grade tumors (0.60 ± 0.29) (P < .001). Across the cohort, relative tumor blood flow did not distinguish individual histology; however, among posterior fossa tumors, relative tumor blood flow was significantly higher for medulloblastoma compared with pilocytic astrocytoma (P = .014).

CONCLUSIONS

Characteristic arterial spin-labeling perfusion patterns were seen among diverse pathologic types of brain tumors in children. Arterial spin-labeling perfusion can be used to distinguish high-grade and low-grade tumors.

Diffusion-weighted MRI: distinction of skull base chordoma from chondrosarcoma

BACKGROUND AND PURPOSE

Chordoma and chondrosarcoma of the skull base are rare tumors with overlapping presentations and anatomic imaging features but different prognoses. We hypothesized that these tumors might be distinguished by using diffusion-weighted MR imaging.

MATERIALS AND METHODS

We retrospectively reviewed 19 patients with pathologically confirmed chordoma or chondrosarcoma who underwent both conventional and diffusion-weighted MR imaging. Differences in distributions of ADC were assessed by the Kruskal-Wallis test. Associations between histopathologic diagnosis and conventional MR imaging features (T2 signal intensity, contrast enhancement, and tumor location) were assessed with the Fisher exact test.

RESULTS

Chondrosarcoma was associated with the highest mean ADC value (2051 ± 261 × 10(-6) mm(2)/s) and was significantly different from classic chordoma (1474 ± 117 × 10(-6) mm(2)/s) and poorly differentiated chordoma (875 ± 100 × 10(-6) mm(2)/s) (P < .001). Poorly differentiated chordoma was characterized by low T2 signal intensity (P = .001), but other conventional MR imaging features of enhancement and/or lesion location did not reliably distinguish these tumor types.

CONCLUSIONS

Diffusion-weighted MR imaging may be useful in assessing clival tumors, particularly in differentiating chordoma from chondrosarcoma. A prospective study of a larger cohort will be required to determine the value of ADC in predicting histopathologic diagnosis.