Recommended Resting-State fMRI Acquisition and Preprocessing Steps for Preoperative Mapping of Language and Motor and Visual Areas in Adult and Pediatric Patients with Brain Tumors and Epilepsy.

Resting-state (rs) fMRI has been shown to be useful for preoperative mapping of functional areas in patients with brain tumors and epilepsy. However, its lack of standardization limits its widespread use and hinders multicenter collaboration. The American Society of Functional Neuroradiology, American Society of Pediatric Neuroradiology, and the American Society of Neuroradiology Functional and Diffusion MR Imaging Study Group recommend specific rs-fMRI acquisition approaches and preprocessing steps that will further support rs-fMRI for future clinical use. A task force with expertise in fMRI from multiple institutions provided recommendations on the rs-fMRI steps needed for mapping of language, motor, and visual areas in adult and pediatric patients with brain tumor and epilepsy. These were based on an extensive literature review and expert consensus.Following rs-fMRI acquisition parameters are recommended: minimum 6-minute acquisition time; scan with eyes open with fixation; obtain rs-fMRI before both task-based fMRI and contrast administration; temporal resolution of ≤2 seconds; scanner field strength of 3T or higher. The following rs-fMRI preprocessing steps and parameters are recommended: motion correction (seed-based correlation analysis [SBC], independent component analysis [ICA]); despiking (SBC); volume censoring (SBC, ICA); nuisance regression of CSF and white matter signals (SBC); head motion regression (SBC, ICA); bandpass filtering (SBC, ICA); and spatial smoothing with a kernel size that is twice the effective voxel size (SBC, ICA).The consensus recommendations put forth for rs-fMRI acquisition and preprocessing steps will aid in standardization of practice and guide rs-fMRI program development across institutions. Standardized rs-fMRI protocols and processing pipelines are essential for multicenter trials and to implement rs-fMRI as part of standard clinical practice.

Ethical Considerations and Fairness in the Use of Artificial Intelligence for Neuroradiology.

In this review, concepts of algorithmic bias and fairness are defined qualitatively and mathematically. Illustrative examples are given of what can go wrong when unintended bias or unfairness in algorithmic development occurs. The importance of explainability, accountability, and transparency with respect to artificial intelligence algorithm development and clinical deployment is discussed. These are grounded in the concept of "primum no nocere" (first, do no harm). Steps to mitigate unfairness and bias in task definition, data collection, model definition, training, testing, deployment, and feedback are provided. Discussions on the implementation of fairness criteria that maximize benefit and minimize unfairness and harm to neuroradiology patients will be provided, including suggestions for neuroradiologists to consider as artificial intelligence algorithms gain acceptance into neuroradiology practice and become incorporated into routine clinical workflow.

Impact of SARS-CoV-2 Pandemic on "Stroke Code" Imaging Utilization and Yield.

BACKGROUND AND PURPOSE: Indirect consequences of the Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2) pandemic include those related to failure of patients to seek or receive timely medical attention for seemingly unrelated disease. We report our experience with stroke code imaging during the early pandemic months of 2020. MATERIALS AND METHODS: Retrospective review of stroke codes during the 2020 pandemic and both 2020 and matched 2019 prepandemic months was performed. Patient variables were age, sex, hospital location, and severity of symptoms based on the NIHSS. We reviewed the results of CT of the head, CTA, CTP, and MR imaging examinations and classified a case as imaging-positive if any of the imaging studies yielded a result that related to the clinical indication for the study. Both year-to-year and sequential comparisons were performed between pandemic and prepandemic months. RESULTS: A statistically significant decrease was observed in monthly stroke code volumes accompanied by a statistically significant increased proportion of positive imaging findings during the pandemic compared with the same months in the prior year (P < .001) and prepandemic months in the same year (P < .001). We also observed statistically significant increases in average NIHSS scores (P = .045 and P = .03) and the proportion of inpatient stroke codes (P = .003 and P = .03). CONCLUSIONS: During our pandemic period, there was a significantly decreased number of stroke codes but simultaneous increases in positivity rates, symptom severity, and inpatient codes. We postulate that this finding reflects the documented reluctance of patients to seek medical care during the pandemic, with the shift toward a greater proportion of inpatient stroke codes potentially reflecting the neurologic complications of the virus itself.

COVID-19 Severity and Stroke: Correlation of Imaging and Laboratory Markers.

BACKGROUND AND PURPOSE: Coronavirus disease 2019 (COVID-19) appears to be an independent risk factor for stroke. We hypothesize that patients who develop stroke while hospitalized for severe COVID-19 will have higher inflammatory markers and distinct stroke imaging patterns compared with patients positive for COVID-19 with out-of-hospital stroke onset and milder or no COVID-19 symptoms. MATERIALS AND METHODS: This is a retrospective case series of patients positive for COVID-19 on polymerase chain reaction testing with imaging-confirmed stroke treated within a large health care network in New York City and Long Island between March 14 and April 26, 2020. Clinical and laboratory data collected retrospectively included complete blood counts and creatinine, alanine aminotransferase, lactate dehydrogenase, C-reactive protein, ferritin, and D-dimer levels. All CT and MR imaging studies were independently reviewed by 2 neuroradiologists who recorded stroke subtype and patterns of infarction and intracranial hemorrhage. RESULTS: Compared with patients with COVID-19 with outside-of-hospital stroke onset and milder or no COVID-19 symptoms (n = 45, 52.3%), patients with stroke already hospitalized for severe COVID-19 (n = 41, 47.7%) had significantly more frequent infarctions (95.1% versus 73.3%, P = .006), with multivascular distributions (56.4% versus 33.3%, P = .022) and associated hemorrhage (31.7% versus 4.4%, P = .001). Patients with stroke admitted with more severe COVID-19 had significantly higher C-reactive protein and ferritin levels, elevated D-dimer levels, and more frequent lymphopenia and renal and hepatic injury (all, P < .003). CONCLUSIONS: Patients with stroke hospitalized with severe COVID-19 are characterized by higher inflammatory, coagulopathy, and tissue-damage biomarkers, supporting proposed pathogenic mechanisms of hyperinflammation activating a prothrombotic state. Cautious balancing of thrombosis and the risk of hemorrhagic transformation is warranted when considering anticoagulation.

Artificial Intelligence and Acute Stroke Imaging.

Artificial intelligence technology is a rapidly expanding field with many applications in acute stroke imaging, including ischemic and hemorrhage subtypes. Early identification of acute stroke is critical for initiating prompt intervention to reduce morbidity and mortality. Artificial intelligence can help with various aspects of the stroke treatment paradigm, including infarct or hemorrhage detection, segmentation, classification, large vessel occlusion detection, Alberta Stroke Program Early CT Score grading, and prognostication. In particular, emerging artificial intelligence techniques such as convolutional neural networks show promise in performing these imaging-based tasks efficiently and accurately. The purpose of this review is twofold: first, to describe AI methods and available public and commercial platforms in stroke imaging, and second, to summarize the literature of current artificial intelligence-driven applications for acute stroke triage, surveillance, and prediction.

Artificial Intelligence in Neuroradiology: Current Status and Future Directions.

Fueled by new techniques, computational tools, and broader availability of imaging data, artificial intelligence has the potential to transform the practice of neuroradiology. The recent exponential increase in publications related to artificial intelligence and the central focus on artificial intelligence at recent professional and scientific radiology meetings underscores the importance. There is growing momentum behind leveraging artificial intelligence techniques to improve workflow and diagnosis and treatment and to enhance the value of quantitative imaging techniques. This article explores the reasons why neuroradiologists should care about the investments in new artificial intelligence applications, highlights current activities and the roles neuroradiologists are playing, and renders a few predictions regarding the near future of artificial intelligence in neuroradiology.

Severity of intervertebral disc herniation regulates cytokine and chemokine levels in patients with chronic radicular back pain.

OBJECTIVE: The contributions of intervertebral disc disease and subject-specific covariates to systemic inflammation in low back pain are unknown. We examined the effects of symptomatic disc herniation (DH) and MRI herniation severity on serum cytokine levels in clinical subjects. DESIGN: Cytokine levels from lumbar DH subjects (N = 78) were compared to control subjects (N = 57) accounting for effects of DH, age, body mass index (BMI) and gender. Effect of DH severity on cytokine levels was analyzed on subsets of subjects with acute or chronic pain. Serum cytokines were also analyzed in a subset of patients between pre- and 3 months post-surgery. RESULTS: Cytokine levels were elevated in the serum of patients with symptomatic DH, and the covariates age, BMI and gender significantly contributed to levels of some cytokines. Severity of herniation was a significant contributor to pain intensity (VAS), serum levels of HMGB1, PDGFbb, and IL-9. The relationship between DH severity and cytokine levels was confirmed in subjects with chronic, but not acute symptoms. Serum levels of macrophage migration inhibitory factor (MIF) decreased, whereas levels of CCL3, CCL11, CXCL1, and CXCL10 were significantly elevated post surgery. CONCLUSIONS: This study is the first to show that DH severity is coordinately associated with changes in serum levels of inflammatory cytokines in chronic pain subjects. HMGB1, PDGFbb and IL-9 are novel mediators of increasing DH severity, indicative of cellular damage, neuro-inflammation and angiogenesis. Resolution of inflammation was observed with decrease in MIF post surgery. However, elevated chemokine levels indicate ongoing remodeling and wound healing at 3-month time point.

From the Eye of the Storm: Multi-Institutional Practical Perspectives on Neuroradiology from the COVID-19 Outbreak in New York City.

During the Severe Acute Respiratory Syndrome Coronavirus 2 (COVID-19) pandemic, neuroradiology practices have experienced a paradigm shift in practice, which affected everything from staffing, workflow, work volumes, conferences, resident and fellowship education, and research. This article highlights adaptive strategies that were undertaken at the epicenter of the outbreak in New York City during the past 4-6 weeks, as experienced by 5 large neuroradiology academic departments.

Measuring Glymphatic Flow in Man Using Quantitative Contrast-Enhanced MRI.

On the basis of animal models, glymphatic flow disruption is hypothesized to be a factor in the development of Alzheimer's disease. We report the first quantitative study of glymphatic flow in man, combining intrathecal administration of gadobutrol with serial T1 mapping to produce contrast concentration maps up to 3 days postinjection, demonstrating performing a quantitative study using the techniques described feasibility and providing data on pharmacokinetics.

Hybrid 3D/2D Convolutional Neural Network for Hemorrhage Evaluation on Head CT.

BACKGROUND AND PURPOSE: Convolutional neural networks are a powerful technology for image recognition. This study evaluates a convolutional neural network optimized for the detection and quantification of intraparenchymal, epidural/subdural, and subarachnoid hemorrhages on noncontrast CT. MATERIALS AND METHODS: This study was performed in 2 phases. First, a training cohort of all NCCTs acquired at a single institution between January 1, 2017, and July 31, 2017, was used to develop and cross-validate a custom hybrid 3D/2D mask ROI-based convolutional neural network architecture for hemorrhage evaluation. Second, the trained network was applied prospectively to all NCCTs ordered from the emergency department between February 1, 2018, and February 28, 2018, in an automated inference pipeline. Hemorrhage-detection accuracy, area under the curve, sensitivity, specificity, positive predictive value, and negative predictive value were assessed for full and balanced datasets and were further stratified by hemorrhage type and size. Quantification was assessed by the Dice score coefficient and the Pearson correlation. RESULTS: 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) were used in this study. 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. Dice scores for intraparenchymal hemorrhage, epidural/subdural hemorrhage, and SAH were 0.931, 0.863, and 0.772, respectively. CONCLUSIONS: A customized deep learning tool is accurate in the detection and quantification of hemorrhage on NCCT. Demonstrated high performance on prospective NCCTs ordered from the emergency department suggests the clinical viability of the proposed deep learning tool.

Deep-Learning Convolutional Neural Networks Accurately Classify Genetic Mutations in Gliomas.

BACKGROUND AND PURPOSE: The World Health Organization has recently placed new emphasis on the integration of genetic information for gliomas. While tissue sampling remains the criterion standard, noninvasive imaging techniques may provide complimentary insight into clinically relevant genetic mutations. Our aim was to train a convolutional neural network to independently predict underlying molecular genetic mutation status in gliomas with high accuracy and identify the most predictive imaging features for each mutation. MATERIALS AND METHODS: MR imaging data and molecular information were retrospectively obtained from The Cancer Imaging Archives for 259 patients with either low- or high-grade gliomas. A convolutional neural network was trained to classify isocitrate dehydrogenase 1 (IDH1) mutation status, 1p/19q codeletion, and O6-methylguanine-DNA methyltransferase (MGMT) promotor methylation status. Principal component analysis of the final convolutional neural network layer was used to extract the key imaging features critical for successful classification. RESULTS: Classification had high accuracy: IDH1 mutation status, 94%; 1p/19q codeletion, 92%; and MGMT promotor methylation status, 83%. Each genetic category was also associated with distinctive imaging features such as definition of tumor margins, T1 and FLAIR suppression, extent of edema, extent of necrosis, and textural features. CONCLUSIONS: Our results indicate that for The Cancer Imaging Archives dataset, machine-learning approaches allow classification of individual genetic mutations of both low- and high-grade gliomas. We show that relevant MR imaging features acquired from an added dimensionality-reduction technique demonstrate that neural networks are capable of learning key imaging components without prior feature selection or human-directed training.

Dynamic Susceptibility Contrast-Enhanced MR Perfusion Imaging in Assessing Recurrent Glioblastoma Response to Superselective Intra-Arterial Bevacizumab Therapy.

BACKGROUND AND PURPOSE: Recurrent glioblastoma currently has no established standard of care. We evaluated the response of recurrent glioblastoma to superselective intra-arterial cerebral infusion of bevacizumab by using dynamic susceptibility contrast-enhanced MR perfusion imaging. We hypothesized that treatment response would be associated with decreased relative CBV and relative CBF. MATERIALS AND METHODS: Patients were accrued for this study from larger ongoing serial Phase I/II trials. Twenty-five patients (14 men, 11 women; median age, 55 years) were analyzed. Four distinct ROIs were chosen: 1) normal-appearing white matter on the contralateral side, 2) the location of the highest T1 enhancement in the lesion (maximum enhancing), 3) the location of highest relative CBV in the lesion (maximum relative CBV), and 4) nonenhancing T2 hyperintense signal abnormality surrounding the tumor (nonenhancing T2 hyperintensity). RESULTS: There was a statistically significant median percentage change of -32.34% (P = .001) in relative CBV in areas of maximum relative CBV following intra-arterial bevacizumab therapy. There was also a statistically significant median percentage decrease in relative CBF of -30.67 (P = .001) and -27.25 (P = .037) in areas of maximum relative CBV and maximum tumor enhancement, respectively. Last, a trend toward statistical significance for increasing relative CBV in nonenhancing T2 hyperintense areas (median percent change, 30.04; P = .069) was noted. CONCLUSIONS: Dynamic susceptibility contrast-enhanced MR perfusion imaging demonstrated a significant decrease in tumor perfusion metrics within recurrent glioblastomas in response to superselective intra-arterial cerebral infusion of bevacizumab; however, these changes did not correlate with time to progression or overall survival.

Traumatic brain injury imaging research roadmap.

The past decade has seen impressive advances in the types of neuroimaging information that can be acquired in patients with traumatic brain injury. However, despite this increase in information, understanding of the contribution of this information to prognostic accuracy and treatment pathways for patients is limited. Available techniques often allow us to infer the presence of microscopic changes indicative of alterations in physiology and function in brain tissue. However, because histologic confirmation is typically lacking, conclusions reached by using these techniques remain solely inferential in almost all cases. Hence, a need exists for validation of these techniques by using data from large population samples that are obtained in a uniform manner, analyzed according to well-accepted procedures, and correlated with closely monitored clinical outcomes. At present, many of these approaches remain confined to population-based research rather than diagnosis at an individual level, particularly with regard to traumatic brain injury that is mild or moderate in degree. A need and a priority exist for patient-centered tools that will allow advanced neuroimaging tools to be brought into clinical settings. One barrier to developing these tools is a lack of an age-, sex-, and comorbidities-stratified, sequence-specific, reference imaging data base that could provide a clear understanding of normal variations across populations. Such a data base would provide researchers and clinicians with the information necessary to develop computational tools for the patient-based interpretation of advanced neuroimaging studies in the clinical setting. The recent "Joint ASNR-ACR HII-ASFNR TBI Workshop: Bringing Advanced Neuroimaging for Traumatic Brain Injury into the Clinic" on May 23, 2014, in Montreal, Quebec, Canada, brought together neuroradiologists, neurologists, psychiatrists, neuropsychologists, neuroimaging scientists, members of the National Institute of Neurologic Disorders and Stroke, industry representatives, and other traumatic brain injury stakeholders to attempt to reach consensus on issues related to and develop consensus recommendations in terms of creating both a well-characterized normative data base of comprehensive imaging and ancillary data to serve as a reference for tools that will allow interpretation of advanced neuroimaging tests at an individual level of a patient with traumatic brain injury. The workshop involved discussions concerning the following: 1) designation of the policies and infrastructure needed for a normative data base, 2) principles for characterizing normal control subjects, and 3) standardizing research neuroimaging protocols for traumatic brain injury. The present article summarizes these recommendations and examines practical steps to achieve them.

Meta-analysis of diffusion metrics for the prediction of tumor grade in gliomas.

BACKGROUND AND PURPOSE: Diffusion tensor metrics are potential in vivo quantitative neuroimaging biomarkers for the characterization of brain tumor subtype. This meta-analysis analyzes the ability of mean diffusivity and fractional anisotropy to distinguish low-grade from high-grade gliomas in the identifiable tumor core and the region of peripheral edema. MATERIALS AND METHODS: A meta-analysis of articles with mean diffusivity and fractional anisotropy data for World Health Organization low-grade (I, II) and high-grade (III, IV) gliomas, between 2000 and 2013, was performed. Pooled data were analyzed by using the odds ratio and mean difference. Receiver operating characteristic analysis was performed for patient-level data. RESULTS: The minimum mean diffusivity of high-grade gliomas was decreased compared with low-grade gliomas. High-grade gliomas had decreased average mean diffusivity values compared with low-grade gliomas in the tumor core and increased average mean diffusivity values in the peripheral region. High-grade gliomas had increased FA values compared with low-grade gliomas in the tumor core, decreased values in the peripheral region, and a decreased fractional anisotropy difference between the tumor core and peripheral region. CONCLUSIONS: The minimum mean diffusivity differs significantly with respect to the World Health Organization grade of gliomas. Statistically significant effects of tumor grade on average mean diffusivity and fractional anisotropy were observed, supporting the concept that high-grade tumors are more destructive and infiltrative than low-grade tumors. Considerable heterogeneity within the literature may be due to systematic factors in addition to underlying lesion heterogeneity.

Computer-assisted volumetric measurement of core infarct volume in pediatric patients: feasibility for clinical use and development of quantitative metrics for outcome prediction.

BACKGROUND AND PURPOSE: Infarct volume may predict clinical outcome in acute stroke, but manual segmentation techniques limit its routine use. We hypothesized that computer-assisted volumetric analysis to quantify acute infarct volume will show no difference compared with manual segmentation but will show increased speed of performance and will correlate with outcome. MATERIALS AND METHODS: Patients with acute stroke younger than 18 years were included. Infarct volume on diffusion-weighted imaging was quantified by using computer-assisted volumetric and manual techniques. The Pediatric Stroke Outcome Measure scored clinical outcome. Computer-assisted volumetric and manual techniques were compared with correlation coefficients. Linear regression analysis compared Pediatric Stroke Outcome Measure with core infarct volume and percentage volume of brain infarction. RESULTS: Twenty-three patients were analyzed (mean age, 4.6 years). Mean infarct volume from computer-assisted volumetric and manual approaches was 65.6 and 63.7 mL, respectively (P = .56). Concordance correlation between methods was 0.980, and between users, 0.968. The mean times for segmentation between computer-assisted volumetric and manual techniques were <1 minute and 7.3 minutes (P < .001). The mean infarct volumes for good and poor outcome groups were 7.4 and 75.7 mL (P < .007). The mean percentages of infarcted brain parenchyma for good and poor outcome groups were 0.6% and 10.4% (P < .006). Volumes of 32 mL and 3% for infarcted brain were associated with poor outcome in all patients. CONCLUSIONS: Computer-assisted volumetric quantification of infarct volume is reproducible, is significantly faster than manual techniques, and may have important applications for future clinical workflow. Core infarct volumes and infarct percentage correlated with outcome severity.

Semiautomated volumetric measurement on postcontrast MR imaging for analysis of recurrent and residual disease in glioblastoma multiforme.

BACKGROUND AND PURPOSE: A limitation in postoperative monitoring of patients with glioblastoma is the lack of objective measures to quantify residual and recurrent disease. Automated computer-assisted volumetric analysis of contrast-enhancing tissue represents a potential tool to aid the radiologist in following these patients. In this study, we hypothesize that computer-assisted volumetry will show increased precision and speed over conventional 1D and 2D techniques in assessing residual and/or recurrent tumor. MATERIALS AND METHODS: This retrospective study included patients with native glioblastomas with MR imaging performed at 24-48 hours following resection and 2-4 months postoperatively. 1D and 2D measurements were performed by 2 neuroradiologists with Certificates of Added Qualification. Volumetry was performed by using manual segmentation and computer-assisted volumetry, which combines region-based active contours and a level set approach. Tumor response was assessed by using established 1D, 2D, and volumetric standards. Manual and computer-assisted volumetry segmentation times were compared. Interobserver correlation was determined among 1D, 2D, and volumetric techniques. RESULTS: Twenty-nine patients were analyzed. Discrepancy in disease status between 1D and 2D compared with computer-assisted volumetry was 10.3% (3/29) and 17.2% (5/29), respectively. The mean time for segmentation between manual and computer-assisted volumetry techniques was 9.7 minutes and <1 minute, respectively (P < .01). Interobserver correlation was highest for volumetric measurements (0.995; 95% CI, 0.990-0.997) compared with 1D (0.826; 95% CI, 0.695-0.904) and 2D (0.905; 95% CI, 0.828-0.948) measurements. CONCLUSIONS: Computer-assisted volumetry provides a reproducible and faster volumetric assessment of enhancing tumor burden, which has implications for monitoring disease progression and quantification of tumor burden in treatment trials.

Quality of extracranial carotid evaluation with 256-section CT.

BACKGROUND AND PURPOSE: To date, no systematic evaluation of image quality has been performed on the 256-section multidetector CT scanner for extracranial carotid evaluations. We evaluated image quality, patient dose, and examination time and compared these parameters with a 64-section multidetector CT. MATERIALS AND METHODS: We reviewed extracranial CTA scans obtained on a 256-detector CT scanner in 50 consecutive patients. Image quality was analyzed for artifacts and vessel contrast opacification from the aortic arch to the skull base, dose to patient, and scan time. Results were compared with a control group of 50 patients imaged on a 64-section CT scanner. A Fisher exact test was used to analyze both groups with respect to vessel contrast opacification and presence of artifacts, and a Student t test was used to assess differences in patient dose between the 2 groups. RESULTS: Quantitative and qualitative evaluations revealed >95% acceptable vessel opacification at all levels measured on the 256-section scanner. Scan time was improved (4 seconds on 256-channel). There were fewer artifacts related to metallic streak on the 256-channel CTA study, and DLP was lower on the 256-channel CTA (113.9 versus 159.8 mGy). CONCLUSIONS: The 256-channel CTA imaging protocol for carotid arteries yielded similar vessel contrast opacification compared with the 64-channel CTA but with fewer metallic artifacts, a modest decrease in scan time, similar image quality, and a statistically significant reduction in radiation dose of 10%.

Proton MR spectroscopy in a 1T open MR imaging system.

(1)H-MR spectroscopy is an established noninvasive MR imaging technique that can be helpful in the diagnosis of brain lesions and in treatment planning. Claustrophobia and body habitus preclude some patients from routine MR imaging in a closed-bore system. The development of (1)H-MR spectroscopy for use in an open MR imaging system would enable a more complete characterization of brain lesions in these patients.

MR imaging of psychosurgery: rostral atrophy following stereotacic subcaudate tractotomy.

There are few reports of MR imaging findings following psychosurgery. Here, we report the findings of 3T MR imaging of the sequelae of stereotactic subcaudate tractotomy (SST). Rostral atrophy is noted on conventional imaging. Diffusion tensor (DT) tractography demonstrated no communicating white matter tracts between the inferior frontal lobes, which appeared normally as thick fibre bundles in age-matched controls. DT tractography provides a unique tool for the evaluation of sequelae of ablative psychosurgical procedures.

Detection of intracranial hemorrhage: comparison between gradient-echo images and b(0) images obtained from diffusion-weighted echo-planar sequences.

BACKGROUND AND PURPOSE: Diffusion-weighted MR imaging (DWI) is commonly used as the initial and sole imaging examination for the detection of acute cerebral infarction, yet it remains controversial whether MR can detect hyperacute (<24 h) hemorrhage. Hemorrhage is best detected with gradient-echo (GRE) T2*-weighted sequences, because of their magnetic susceptibility effects. DWI uses a spin-echo echo-planar technique (EPI) that is more sensitive than spin-echo T2-weighted imaging to susceptibility effects. Our aim was to determine whether the b(0) image from the DWI-EPI sequence is as sensitive as GRE in detecting hemorrhagic lesions on imaging studies performed to identify acute infarction or hemorrhage. METHODS: All MR studies performed for clinically suspected or radiographically confirmed acute infarction or hemorrhage from 2/1/98 to 8/15/99 were retrospectively interpreted by one neuroradiologist in a blinded fashion. The sensitivity of hemorrhage detection, conspicuity of lesions, and diagnostic certainty were compared between the b(0) EPI and GRE sequences. RESULTS: We found 101 acute infarcts, of which 13 were hemorrhagic, as evidenced by the presence of hypointensity within the infarction on the GRE sequence. This finding served as the reference standard for detection of hemorrhage. Hemorrhage was diagnosed with confidence in only seven cases (54%) on b(0) images; 22 acute hematomas were hypointense on GRE images whereas 19 were hypointense on b(0) images (86%); 17 chronic hematomas were depicted on GRE images and 12 on b(0) scans (63%). Punctate hemorrhages and linear cortical staining were detected on 37 GRE studies but on only four b(0) studies. Hemorrhage was always more conspicuous on the GRE sequences. CONCLUSION: b(0) images from a DWI sequence failed to detect minimally hemorrhagic infarctions and small chronic hemorrhages associated with microangiopathy. GRE scans were more sensitive than b(0) images in the detection of these hemorrhages and should be included in emergency brain MR studies for acute infarction, especially when thrombolytic therapy is contemplated.