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ObjectivesReal-time assessment of treatment response in glioblastoma (GBM) patients on immune checkpoint blockade (ICB) remains challenging because inflammatory effects of therapy may mimic progressive disease, and the temporal evolution of these inflammatory findings is poorly understood. We compare GBM patient response during ICB as assessed with the Immunotherapy Response Assessment in Neuro-Oncology (iRANO) and the standard Response Assessment in Neuro-Oncology (RANO) radiological criteria.Methods49 GBM patients (seven newly diagnosed and 42 recurrent) treated with ICBs at a single institution were identified. Tumor burden was quantified on serial MR scans according to RANO criteria during ICB. Radiographic response assessment by iRANO and RANO were compared.Results82% (40/49) of patients received anti–PD-1, 16% (8/49) received anti-PD-L1, and 2% (1/49) received anti-PD-1 and anti-CTLA4 treatment. Change in tumor burden and best overall response ranged from −100 to +557% (median: +48%). 12% (6/49) of patients were classified as concordant non-progressors by both RANO and iRANO (best response: one CR, one PR, and four SD). Another12% (6/49) had discordant assessments: 15% (6/41) of RANO grade progressive disease (PD) patients had iRANO grade of progressive disease unconfirmed (PDU). The final classification of these discordant patients was pseudoprogression (PsP) in three of six, PD in two of six, and PDU in one of six who went off study before the iRANO assessment of PDU. iRANO delayed diagnosis of PD by 42 and 93 days in the two PD patients. 76% (37/49) patients were classified as concordant PD by both RANO and iRANO. 12% (6/49) of all patients were classified as PsP, starting at a median of 12 weeks (range, 4–30 weeks) after ICB initiation.ConclusionsStandard RANO and iRANO have high concordance for assessing PD in patients within 6 months of ICB initiation. iRANO was beneficial in 6% (3/49) cases later proven to be PsP, but delayed confirmation of PD by

Background Intraoperative magnetic resonance imaging (IO-MRI) provides real-time assessment of extent of resection of brain tumor. Development of new enhancement during IO-MRI can confound interpretation of residual enhancing tumor, although the incidence of this finding is unknown. Objective To determine the frequency of new enhancement during brain tumor resection on intraoperative 3 Tesla (3T) MRI. To optimize the postoperative imaging window after brain tumor resection using 1.5 and 3T MRI. Methods We retrospectively evaluated 64 IO-MRI performed for patients with enhancing brain lesions referred for biopsy or resection as well as a subset with an early postoperative MRI (EP-MRI) within 72 h of surgery (N = 42), and a subset with a late postoperative MRI (LP-MRI) performed between 120 h and 8 wk postsurgery (N = 34). Three radiologists assessed for new enhancement on IO-MRI, and change in enhancement on available EP-MRI and LP-MRI. Consensus was determined by majority response. Inter-rater agreement was assessed using percentage agreement. Results A total of 10 out of 64 (16%) of the IO-MRI demonstrated new enhancement. Seven of 10 patients with available EP-MRI demonstrated decreased/resolved enhancement. One out of 42 (2%) of the EP-MRI demonstrated new enhancement, which decreased on LP-MRI. Agreement was 74% for the assessment of new enhancement on IO-MRI and 81% for the assessment of new enhancement on the EP-MRI. Conclusion New enhancement occurs in intraoperative 3T MRI in 16% of patients after brain tumor resection, which decreases or resolves on subsequent MRI within 72 h of surgery. Our findings indicate the opportunity for further study to optimize the postoperative imaging window.

Background and objective Cerebrovascular diseases (CVDs) affect a large number of patients and often have devastating outcomes. The hallmarks of CVDs are the abnormalities formed on brain blood vessels, including protrusions, narrows, widening, and bifurcation of the blood vessels. CVDs are often diagnosed by digital subtraction angiography (DSA) yet the interpretation of DSA is challenging as one must carefully examine each brain blood vessel. The objective of this work is to develop a computerized analysis approach for automated segmentation of brain blood vessels. Methods In this work, we present a U-net based deep learning approach, combined with pre-processing, to track and segment brain blood vessels in DSA images. We compared the results given by the deep learning approach with manually marked ground truth using accuracy, sensitivity, specificity, and Dice coefficient. Results Our results showed that the proposed approach achieved an accuracy of 0.978, with a standard deviation of 0.00796, a sensitivity of 0.76 with a standard deviation of 0.096, a specificity of 0.994 with a standard deviation of 0.0036, and an average Dice coefficient was 0.8268 with a standard deviation of 0.052. Conclusions Our findings show that the deep learning approach can achieve satisfactory performance as a computer-aided analysis tool to assist clinicians in diagnosing CVDs.

BACKGROUND AND OBJECTIVE: Brain metastases have been found to account for one-fourth of all cancer metastases seen in clinics. Magnetic resonance imaging (MRI) is widely used for detecting brain metastases. Accurate detection of the brain metastases is critical to design radiotherapy to treat the cancer and monitor their progression or response to the therapy and prognosis. However, finding metastases on brain MRI is very challenging as many metastases are small and manifest as objects of weak contrast on the images. In this work we present a deep learning approach integrated with a classification scheme to detect cancer metastases to the brain on MRI. MATERIALS AND METHODS: We retrospectively extracted 101 metastases patients, equal to 1535 metastases on 10192 slices of images in a total of 336 scans from our PACS and manually marked the lesions on T1-weighted contrast enhanced MRI as the ground-truth. We then randomly separated the cases into training, validation, and test sets for developing and optimizing the deep learning neural network. We designed a 2-step computer-aided detection (CAD) pipeline by first applying a fast region-based convolutional neural network method (R-CNN) to sequentially process each slice of an axial brain MRI to find abnormal hyper-intensity that may correspond to a brain metastasis and, second, applying a random under sampling boost (RUSBoost) classification method to reduce the false positive metastases. RESULTS: The computational pipeline was tested on real brain images. A sensitivity of 97.28% and false positive rate of 36.25 per scan over the images were achieved by using the proposed method. CONCLUSION: Our results demonstrated the deep learning-based method can detect metastases in very challenging cases and can serve as CAD tool to help radiologists interpret brain MRIs in a time-constrained environment.

Choosing the most appropriate diagnostic neuroimaging study for a pregnant woman involves assessing the pretest likelihood of serious treatable neurologic disease, the diagnostic utility of various available computed tomography (CT) and magnetic resonance (MR) modalities, and the risks of each. Of these three elements—pretest differential diagnosis, utility of MRI and CT, and risks of MR and CT—the risk component is perhaps the least well understood by most physicians. We provide a basic review of the intrinsic risks of MRI and CT, particularly radiation biology and radiation safety, as well as the risks pertaining to the use of contrast agents, to reduce provider confusion and anxiety and improve quality, safety, and efficiency of neuroimaging diagnosis in pregnant patients. We believe that a better understanding of the associated very low risks with mother and fetus will reassure the reader that CT remains the most appropriate tool for initial rapid diagnosis of acute neurological conditions in pregnancy and that in urgent situations CT should not be withheld or delayed due to exaggerated concern about radiation. Noncontrast MRI, while not without risk, is generally considered safe in pregnancy, as no evidence of fetal adverse effects has been demonstrated to date. Iodinated CT contrast agents are likely safer than gadolinium-based MRI contrast agents because of gadolinium accumulation in the amniotic fluid and fetal tissue, although no harmful effects of tissue gadolinium accumulation are known. In most but not all pregnant patients presenting with a new or worsening neurological abnormality, the risks intrinsic to the disease will outweigh the risks of imaging. In an individual patient, the pretest probability of serious treatable disease and acuity of presentation will usually suggest an optimal imaging strategy and choice of test. This optimal strategy will also depend on the immediate availability and level of sophistication of the scanners, software, technologists, and radiologists. As such, the standard of care for imaging in pregnancy requires direct consultation between the referring clinician and radiologist to determine the most appropriate strategy and brief documentation of the resulting consensus risk–benefit assessment.

Recent advances are allowing computed tomography (CT) and magnetic resonance imaging (MRI) to add diagnostic information derived from microscopic-scale brain structure, pathology, and physiology to the gross pathologic information that has been the core of brain imaging diagnosis since the 1980s. Physiologic imaging with MR perfusion weighted imaging has joined MR diffusion imaging as an essential components of stroke and brain tumor MRI. At the same time, the volume scanning revolution in CT technology has dramatically decreased the radiation doses required for CT perfusion imaging by allowing routine simultaneous CT perfusion and noninvasive dynamic bone subtracted CT angiography (CTA) without a significant increase in radiation dose over conventional head CT-CTA alone. Although ongoing research and clinical trials is needed to define more precisely how these techniques can best be exploited to improve clinical care and patient outcomes, in the acute stroke and subarachnoid hemorrhage populations the radiation risk associated with CT perfusion imaging is negligible and the physiologic information promises significant patient safety benefits.

We describe the imaging findings encountered in GBM patients receiving immune checkpoint blockade and assess the potential of quantitative MRI biomarkers to differentiate patients who derive therapeutic benefit from those who do not.A retrospective analysis was performed on longitudinal MRIs obtained on recurrent GBM patients enrolled on clinical trials. Among 10 patients with analyzable data, bidirectional diameters were measured on contrast enhanced T1 (pGd-T1WI) and volumes of interest (VOI) representing measurable abnormality suggestive of tumor were selected on pGdT1WI (pGdT1 VOI), FLAIR-T2WI (FLAIR VOI), and ADC maps. Intermediate ADC (IADC) VOI represented voxels within the FLAIR VOI having ADC in the range of highly cellular tumor (0.7-1.1 × 10Five patients were deemed to have received therapeutic benefit and the other five patients did not. The average time on trial for the benefit group was 194 days, as compared to 81 days for the no benefit group. IADC VOI correlated well with the presence or absence of clinical benefit in 10 patients. Furthermore, pGd VOI, FLAIR VOI, and RANO assessment correlated less well with response.MRI reveals an initial increase in volumes of abnormal tissue with contrast enhancement, edema, and intermediate ADC suggesting hypercellularity within the first 0-6 months of immunotherapy. Subsequent stabilization and improvement in IADC VOI appear to better predict ultimate therapeutic benefit from these agents than conventional imaging.

This chapter briefly introduces the clinical neuroimaging literature related to advanced magnetic resonance imaging (MRI) of brain tumors: DWI, DTI, MRS, and PWI. These techniques add information about brain tumor cellularity, tissue invasion, metabolism, and microvasculature. When appropriately integrated with anatomic MRI in a multidimensional brain tumor imaging protocol, these can contribute significantly to differential diagnosis, estimation of tumor grade, planning of biopsy, focal resection or ablation, and therapeutic monitoring. We emphasize the importance of meticulous attention to acquisition and postprocessing technique, inspection of source images as well as graphical data and color maps, correlation with serial advanced imaging follow-up, and integration of multiple advanced MRI data types with one another, with anatomic imaging, and with clinical history to allow reliable detection of artifacts and accurate interpretation.

Rationale and Objectives Quantitative measurement provides essential information about disease progression and treatment response in patients with glioblastoma multiforme (GBM). The goal of this article is to present and validate a software pipeline for semi-automatic GBM segmentation, called AFINITI (Assisted Follow-up in NeuroImaging of Therapeutic Intervention), using clinical data from GBM patients. Materials and Methods Our software adopts the current state-of-the-art tumor segmentation algorithms and combines them into one clinically usable pipeline. Both the advantages of the traditional voxel-based and the deformable shape-based segmentation are embedded into the software pipeline. The former provides an automatic tumor segmentation scheme based on T1- and T2-weighted magnetic resonance (MR) brain data, and the latter refines the segmentation results with minimal manual input. Results Twenty-six clinical MR brain images of GBM patients were processed and compared with manual results. The results can be visualized using the embedded graphic user interface. Conclusion Validation results using clinical GBM data showed high correlation between the AFINITI results and manual annotation. Compared to the voxel-wise segmentation, AFINITI yielded more accurate results in segmenting the enhanced GBM from multimodality MR imaging data. The proposed pipeline could be used as additional information to interpret MR brain images in neuroradiology.

Purpose To automatically differentiate radiation necrosis from recurrent tumor at high spatial resolution using multiparametric MRI features. Materials and Methods MRI data retrieved from 31 patients (15 recurrent tumor and 16 radiation necrosis) who underwent chemoradiation therapy after surgical resection included post-gadolinium T1, T2, fluid-attenuated inversion recovery, proton density, apparent diffusion coefficient (ADC), and perfusion-weighted imaging (PWI) -derived relative cerebral blood volume (rCBV), relative cerebral blood flow (rCBF), and mean transit time maps. After alignment to post contrast T1WI, an eight-dimensional feature vector was constructed. An one-class-support vector machine classifier was trained using a radiation necrosis training set. Classifier parameters were optimized based on the area under receiver operating characteristic (ROC) curve. The classifier was then tested on the full dataset. Results The sensitivity and specificity of optimized classifier for pseudoprogression was 89.91% and 93.72%, respectively. The area under ROC curve was 0.9439. The distribution of voxels classified as radiation necrosis was supported by the clinical interpretation of follow-up scans for both nonprogressing and progressing test cases. The ADC map derived from diffusion-weighted imaging and rCBV, rCBF derived from PWI were found to make a greater contribution to the discrimination than the conventional images. Conclusion Machine learning using multiparametric MRI features may be a promising approach to identify the distribution of radiation necrosis tissue in resected glioblastoma multiforme patients undergoing chemoradiation. J. Magn. Reson. Imaging 2011;33:296–305. © 2011 Wiley-Liss, Inc.

Surgical planning for deep brain stimulation implantation procedures requires T1-weighted imaging (T1WI) for stereotactic navigation. Because the subthalamic nucleus, the main target for deep brain stimulation, and other midbrain nuclei cannot be visualized on the stereotactic guidance T1WI, additional T2-weighted imaging (T2WI) is generally obtained and registered to the T1WI for surgical targeting. Surgical planning based on the registration of the 2 data sets is subject to error resulting from inconsistent geometric distortions and any subject movement between the 2 scans. In this article, we propose a new method to produce susceptibility-enhanced, contrast-optimized T1-weighted 3-dimensional spoiled gradient recalled acquisition in steady state images with enhanced contrast for midbrain nuclei within the volumetric T1WI data set itself, eliminating the need for additional T2WI. The scan parameters of 3-dimensional spoiled gradient recalled acquisition in steady state are chosen in a way that T1WI can be obtained from conventional magnitude reconstruction and images with improved contrast between midbrain nuclei and surrounding tissues can be produced from the same data by performing susceptibility-weighted imaging reconstruction on a chosen region of interest. In addition, our preliminary experience suggests that the resulting contrast between the midbrain nuclei is superior to the current state-of-the-art fast spin echo T2WI in depicting the subthalamic nucleus as distinct from the substantia nigra pars reticulata and clear depiction of the nucleus ventrointermedius externus of thalamus.

ADOLESCENT IDIOPATHIC SCOLIOSIS (AIS) is a complex three-dimensional spine deformity that most commonly occurs in adolescent girls during the pubertal period. The prevalence in Hong Kong is nearly 4%. Untreated or improperly treated scoliotic deformity may deteriorate progressively leading to significant cosmetic problems and functional disabilities. In severe cases, increased mortality rate can result from the associated early onset cardiopulmonary failure. No preventive treatment for idiopathic scoliosis is currently available, in part because the etiology of AIS is not well understood. Current treatments are pragmatic and involve long-term bracing (1,2) or invasive major surgery with instrumentation and spinal fusion (3), which is associated with many potential complications (4). Observation of subclinical neurological abnormalities in AIS patients has led to the proposal of a neurodevelopmental etiologic model for AIS (5–12). Longer latency in somatosensory-evoked potential (SSEP) has been demonstrated by Cheng et al (13,14) and Hausmann et al (15), suggesting disturbed somatosensory function and impaired balance control (16,17) in AIS subjects. As low-lying cerebellar tonsils have been documented in AIS patients (18–20), we hypothesize that other intracerebral structural abnormalities might be present that could contribute to inappropriate postural adjustment during the growth spurt and so explain the progressive spinal deformity observed in the early adolescence of AIS subjects. In the present study, regional brain volumes were assessed by three-dimensional (3D) MR technique. A whole brain parcellation method based on a hybrid volumetric and surface warping algorithm (21,22) was used to compare the morphologic brain data sets from AIS subjects with those acquired from age and sex-matched normal controls.

We hypothesized that ADC and FA of enhancing tumor (ET) and/or non-enhancing tumor (NT) adjacent to the operative resection margin before and during temozolomide and/or chemoradiation may allow prediction of chemoradiation response and patient survival.DTI was acquired in 37 patients with newly diagnosed HGG at two time points: after resection at the time of pre-RT planning MRI (Baseline) and after 30Gy of radiation therapy (mid-RT). ADC and FA at each time point and change in ADC and FA between the two time points were assessed by hot spot method in both residual ET and NT within 2cm of the resection margin and correlated with overall survival (OS) using receiver operating characteristics (ROC) area under curve (AUC) analysis and log-rank testing.At baseline NT ADC104×10(-5)mm(2)/s was strongly correlated with shorter 15 month OS (95% CI: 227-412 days vs 492-695 days) compared to patients with higher ADC (AUC 0.82). There was good separation between the groups and significance at log-rank testing (p=0.0002). Baseline NT FA0.257 also correlated with shorter OS (95% CI: 300-515 days vs 438-686 days), compared with patients with lower FA (AUC 0.74), but there was considerable overlap between the groups and non-significance at log-rank testing (p=0.089). Residual ET ADC increased significantly (p=0.0004) and FA decreased significantly (p=0.03) for all patients during early RT but the change in ADC and FA was less strongly correlated with OS than baseline NT metrics.Post-operative pre-radiation baseline low ADC in non-enhancing tumor at the resection margin correlates strongly with worse treatment response and decreased overall survival in a heterogeneous sample of high grade glioma patients treated with radiation and/or temozolamide chemoradiation.

The goals of imaging for neurosurgical guidance are many. These often involve developing a differential diagnosis, predicting brain tumor grade, establishing optimal targets for biopsy, evaluating safety of resection, planning operative approach and extent of resection, and providing a baseline for postoperative and post-therapeutic monitoring. Cross-sectional imaging the brain and spinal cord relies primarily on computed X-ray tomography (CT) and magnetic resonance imaging (MRI), with MRI providing the most sensitive and specific results because of its superior tissue contrast, which allows more precise and accurate differentiation of the large spectrum of relevant tissues and pathologies. This chapter briefly introduces the basic techniques for acquiring MRI and CT images, illustrates common applications in neurosurgical guidance of brain tumor biopsy and resection, and outlines principles that guide image interpretation in these applications.

Background There is accumulating evidence that extent of resection (EOR) in intrinsic brain tumor surgery prolongs overall survival (OS) and progression-free survival (PFS). One of the strategies to increase EOR is the use of intraoperative MRI (ioMRI); however, considerable infrastructure investment is needed to establish and maintain a sophisticated ioMRI. We report the preliminary results of an extraoperative (eoMRI) protocol, with a focus on safety, feasibility, and EOR in intrinsic brain tumor surgery. Methods Ten patients underwent an eoMRI protocol consisting of surgical resection in a conventional operating room followed by an immediate MRI in a clinical MRI scanner while the patient was still under anesthesia. If findings of the MRI suggested residual safely resectable tumor, the patient was returned to the operating room. A retrospective volumetric analysis was undertaken to investigate the percentage of tumor resected after first resection and if applicable, after further resection. Results Six of 10 (60%) patients were thought to require no further resection after eoMRI. The EOR in these patients was 97.8% ± 1.8%. In the 4 patients who underwent further resection, the EOR during the original surgery was 88.5% ± 9.5% ( P = 0.04). There was an average of 10.1% more tumor removed between the first and second surgery. In 3 of 4 (75%) of patients who returned for further resection, gross total resection of tumor was achieved. Conclusion An eoMRI protocol appears to be a safe and practical method to ensure maximum safe resections in patients with brain tumors and can be performed readily in all centers with MRI capabilities.

Magnetic resonance imaging (MRI) is the imaging modality of choice by which to monitor patient gliomas and treatment effects, and has been applied to murine models of glioma. However, a major obstacle to the development of effective glioma therapeutics has been that widely used animal models of glioma have not accurately recapitulated the morphological heterogeneity and invasive nature of this very lethal human cancer. This deficiency is being alleviated somewhat as more representative models are being developed, but there is still a clear need for relevant yet practical models that are well-characterized in terms of their MRI features. Hence we sought to chronicle the MRI profile of a recently developed, comparatively straightforward human tumor stem cell (hTSC) derived glioma model in mice using conventional MRI methods. This model reproduces the salient features of gliomas in humans, including florid neoangiogenesis and aggressive invasion of normal brain. Accordingly, the variable, invasive morphology of hTSC gliomas visualized on MRI duplicated that seen in patients, and it differed considerably from the widely used U87 glioma model that does not invade normal brain. After several weeks of tumor growth the hTSC model exhibited an MRI contrast enhancing phenotype having variable intensity and an irregular shape, which mimicked the heterogeneous appearance observed with human glioma patients. The MRI findings reported here support the use of the hTSC glioma xenograft model combined with MRI, as a test platform for assessing candidate therapeutics for glioma, and for developing novel MR methods.

While the prognosis of patients with glioblastoma (GBM) remains poor despite recent therapeutic advances, variable survival times suggest wide variation in tumor biology and an opportunity for stratified intervention. We used volumetric analysis and morphometrics to measure the spatial relationship between subventricular zone (SVZ) proximity and survival in a cohort of 39 newly diagnosed GBM patients. We collected T2-weighted and gadolinium-enhanced T1-weighted magnetic resonance images (MRI) at pre-operative, post-operative, pre-radiation therapy, and post-radiation therapy time points, measured tumor volumes and distances to the SVZ, and collected clinical data. Univariate and multivariate Cox regression showed that tumors involving the SVZ and tumor growth rate during radiation therapy were independent predictors of shorter progression-free and overall survival. These results suggest that GBMs in close proximity to the ependymal surface of the ventricles convey a worse prognosis-an observation that may be useful for stratifying treatment. Electronic supplementary material The online version of this article (doi:10.1007/s11060-010-0477-1) contains supplementary material, which is available to authorized users.

We report the unusually rapid and spontaneous normalisation of low diffusivity that accompanied resolution of acute neurological deficits in a stroke patient who underwent two magnetic resonance imaging examinations within 24 h of symptom onset. Diffusion weighted imaging obtained within hours of onset of left sided weakness demonstrated a focal right capsular area of low diffusivity that resolved within 24 h, coinciding with resolution of the patient’s symptoms.

Brain tumor segmentation is an important image processing step in diagnosis, treatment planning, and follow-up studies of Glioblastoma (GBM). However it is still a challenging task due to varying in size, shape, location, and image intensities within and around the tumor. In this paper, we propose a new brain tumor segmentation method for T1-weighted MR brain images based on an improved level set method using prior information as a constraint, called Prior Information Constrained Evolution (PICE). A new energy function in PICE incorporating the tumor intensity prior is designed to match brain tumor more accurately. The advantage of PICE has been illustrated by comparing with the traditional level set method in 3-D. In addition, we also illustrate that PICE can be easily applied to 4-D images, which facilitates follow-up studies of brain tumor treatments. Using longitudinal GBM data from five patients we showed the advantages of the proposed algorithm.

We demonstrate the use of coherent anti-Stokes Raman scattering (CARS) microscopy to image brain structure and pathology ex vivo. Although non-invasive clinical brain imaging with CT, MRI and PET has transformed the diagnosis of neurologic disease, definitive pre-operative distinction of neoplastic and benign pathologies remains elusive. Definitive diagnosis still requires brain biopsy in a significant number of cases. CARS microscopy, a nonlinear, vibrationally-sensitive technique, is capable of high-sensitivity chemically-selective three-dimensional imaging without exogenous labeling agents. Like MRI, CARS can be tuned to provide a wide variety of possible tissue contrasts, but with sub-cellular spatial resolution and near real time temporal resolution. These attributes make CARS an ideal technique for fast, minimally invasive, non-destructive, molecularly specific intraoperative optical diagnosis of brain lesions. This promises significant clinical benefit to neurosurgical patients by providing definitive diagnosis of neoplasia prior to tissue biopsy or resection. CARS imaging can augment the diagnostic accuracy of traditional frozen section histopathology in needle biopsy and dynamically define the margins of tumor resection during brain surgery. This report illustrates the feasibility of in vivo CARS vibrational histology as a clinical tool for neuropathological diagnosis by demonstrating the use of CARS microscopy in identifying normal brain structures and primary glioma in fresh unfixed and unstained ex vivo brain tissue.

We present a method for tissue classification based on diffusion-weighted imaging (DWI)/diffusion tensor imaging (DTI) data. Our motivation is that independent tissue segmentation based on DWI/DTI images provides complementary information to the tissue segmentation result using structural MRI data alone. The basis idea is to classify the brain into two compartments by utilizing the tissue contrast exiting in a single channel, e.g., apparent diffusion coefficient (ADC) image can be used to separate CSF and non-CSF, and the fractional anisotropy (FA) image can be used to separate WM from non-WM tissues. Other channels such as eigen values of the tensor, relative anisotropy (RA), and volume ratio (VR) can also be used to separate tissues. We employ the STAPLE algorithm to combine these two-class maps to obtain a complete segmentation of CSF, GM, and WM.

Background The determination of the form of prion disease and early diagnosis are important for prognostic, public health, and epidemiologic reasons. Objective To describe a patient with sporadic Creutzfeldt-Jakob disease (sCJD) who had a clinical history and initial electroencephalogram and magnetic resonance imaging findings consistent with variant CJD (vCJD). Results Results of a repeated electroencephalogram were suggestive of sCJD, and a subsequent brain biopsy confirmed this diagnosis. Conclusions This case cautions against relying solely on T2- and diffusion-weighted pulvinar hyperintensity and clinical features to differentiate between vCJD and sCJD, and further supports established diagnostic criteria for vCJD.