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Multiple system atrophy (MSA) is a fatal neurodegenerative disease of unknown etiology characterized by widespread aggregation of the protein alpha-synuclein in neurons and glia. Its orphan status, biological relationship to Parkinson’s disease (PD), and rapid progression have sparked interest in drug development. One significant obstacle to therapeutics is disease heterogeneity. Here, we share our process of developing a clinical trial-ready cohort of MSA patients (69 patients in 2 years) within an outpatient clinical setting, and recruiting 20 of these patients into a longitudinal “n-of-few” clinical trial paradigm. First, we deeply phenotype our patients with clinical scales (UMSARS, BARS, MoCA, NMSS, and UPSIT) and tests designed to establish early differential diagnosis (including volumetric MRI, FDG-PET, MIBG scan, polysomnography, genetic testing, autonomic function tests, skin biopsy) or disease activity (PBR06-TSPO). Second, we longitudinally collect biospecimens (blood, CSF, stool) and clinical, biometric, and imaging data to generate antecedent disease-progression scores. Third, in our Mass General Brigham SCiN study (stemcellsinneurodegeneration), we generate induced pluripotent stem cell (iPSC) models from our patients, matched to biospecimens, including postmortem brain. We present 38 iPSC lines derived from MSA patients and relevant disease controls (spinocerebellar ataxia and PD, including alpha-synuclein triplication cases), 22 matched to whole-genome sequenced postmortem brain. iPSC models may facilitate matching patients to appropriate therapies, particularly in heterogeneous diseases for which patient-specific biology may elude animal models. We anticipate that deeply phenotyped and genotyped patient cohorts matched to cellular models will increase the likelihood of success in clinical trials for MSA.

Differentiating multiple system atrophy (MSA) from related neurodegenerative movement disorders (NMD) is challenging. MRI is widely available and automated decision-tree analysis is simple, transparent, and resistant to overfitting. Using a retrospective cohort of heterogeneous clinical MRIs broadly sourced from a tertiary hospital system, we aimed to develop readily translatable and fully automated volumetric diagnostic decision-trees to facilitate early and accurate differential diagnosis of NMDs. 3DT1 MRI from 171 NMD patients (72 MSA, 49 PSP, 50 PD) and 171 matched healthy subjects were automatically segmented using Freesurfer6.0 with brainstem module. Decision trees employing substructure volumes and a novel volumetric pons-to-midbrain ratio (3D-PMR) were produced and tenfold cross-validation performed. The optimal tree separating NMD from healthy subjects selected cerebellar white matter, thalamus, putamen, striatum, and midbrain volumes as nodes. Its sensitivity was 84%, specificity 94%, accuracy 84%, and kappa 0.69 in cross-validation. The optimal tree restricted to NMD patients selected 3D-PMR, thalamus, superior cerebellar peduncle (SCP), midbrain, pons, and putamen as nodes. It yielded sensitivities/specificities of 94/84% for MSA, 72/96% for PSP, and 73/92% PD, with 79% accuracy and 0.62 kappa. There was correct classification of 16/17 MSA, 5/8 PSP, 6/8 PD autopsy-confirmed patients, and 6/8 MRIs that preceded motor symptom onset. Fully automated decision trees utilizing volumetric MRI data distinguished NMD patients from healthy subjects and MSA from other NMDs with promising accuracy, including autopsy-confirmed and pre-symptomatic subsets. Our open-source methodology is well-suited for widespread clinical translation. Assessment in even more heterogeneous retrospective and prospective cohorts is indicated.

Ultra-high-field 7.0 Tesla (T) MRI offers substantial gains in signal-to-noise ratio (SNR) over 3T and 1.5T, but for over two decades has remained a research tool, while 3T scanners have achieved widespread clinical use. This much slower translation of 7T relates to daunting technical challenges encountered in ultra-high-field human MR imaging. The recent introduction of United States Food and Drug Administration (FDA)-approved clinical 7T scanners promises to be a watershed for many 7T neuroimaging applications, including epilepsy imaging. The high SNR of 7T allows clinical imaging of fine neuroanatomic detail at unprecedented spatial resolution, helping with detection and differentiation of subtle, potentially treatable lesions undetectable or suboptimally assessed at 3T. The accompanying research paper reports our group's analysis of 7T MRI efficacy in epilepsy treatment planning. Here, we introduce the technical background and clinical approach we currently use, in order to assist clinical epileptologists and neuroimagers contemplating, creating, or referring patients to a clinical 7T epilepsy imaging service. We describe a tiered epilepsy imaging strategy and protocols designed to optimize 7T value and work around signal intensity variation and signal loss artifacts, which remain significant challenges to full exploitation of 7T clinical value. We describe FDA-approved techniques for mitigating these artifacts and briefly outline techniques currently under development, but not yet FDA approved. Finally, we discuss the major issues in 7T patient safety and toleration, outlining their physical causes and effects on workflow, and provide references to more comprehensive technical reviews for readers seeking greater technical detail.

In this work we present a framework of designing iterative techniques for image deblurring in inverse problem. The new framework is based on two observations about existing methods. We used Landweber method as the basis to develop and present the new framework but note that the framework is applicable to other iterative techniques. First, we observed that the iterative steps of Landweber method consist of a constant term, which is a low-pass filtered version of the already blurry observation. We proposed a modification to use the observed image directly. Second, we observed that Landweber method uses an estimate of the true image as the starting point. This estimate, however, does not get updated over iterations. We proposed a modification that updates this estimate as the iterative process progresses. We integrated the two modifications into one framework of iteratively deblurring images. Finally, we tested the new method and compared its performance with several existing techniques, including Landweber method, Van Cittert method, GMRES (generalized minimal residual method), and LSQR (least square), to demonstrate its superior performance in image deblurring.

Deep neural networks (DNNs) have been widely used in the medical imaging field. The large and high quality dataset is crucial for the performance of the deep learning models, but the medical data and ground-truth is often insufficient and very expensive in terms of time and human effort on the data collection. However, we can improve the performance of the deep learning model by augmenting the data we already have. In this work, we introduce a novel differential geometry-based quasi conformal (QC) mapping augmentation technique to augment the brain tumor images. The QC method lets the user specify or randomly generate a complex-valued function on the image domain via Beltrami coefficient. By solving the Beltrami equation with given Beltrami coefficient, the QC map, which can further guide the deformation of the image, is able to generate all possible linear and non-linear image warpings and it is flexible to allow the user to fully control the global and local deformations. Our experimental results demonstrate the efficiency and efficacy of the proposed method.

Glioblastoma multiforme (GBM) is the largest and most genetically and phenotypically heterogeneous category of primary brain tumors. Numerous novel chemical, targeted molecular and immune-active therapies in trial produce promising responses in a small disparate subset of patients but which patient will respond to which therapy remains unpredictable. Reliable imaging biomarkers for prediction and early detection of treatment response and survival are critical needs in neuro-oncology. In this study, brain tumor MRI 'deep features' extracted via transfer learning techniques were combined with features derived from an explicitly designed radiomics model to search for MRI markers predictive of overall survival (OS) in GBM patients. Two pre-trained convolutional neural network (CNN) models were utilized as the deep learning models and the elastic net-Cox model was performed to distinguish GBM patients into two survival groups. Two patient cohorts were included in this study. One was 50 GBM patients from our hospital and the other was 128 GBM patients from the Cancer Genome Atlas (TCGA) and the Cancer Image Archive (TCIA). The combined feature framework was predictive of OS in both data set with log-rank test p-value < 0.05 and may merit further study for reproducible prediction of treatment response.

BACKGROUND AND PURPOSE: Patient survival in high-grade glioma remains poor, despite the recent developments in cancer treatment. As new chemo-, targeted molecular, and immune therapies emerge and show promising results in clinical trials, image-based methods for early prediction of treatment response are needed. Deep learning models that incorporate radiomics features promise to extract information from brain MR imaging that correlates with response and prognosis. We report initial production of a combined deep learning and radiomics model to predict overall survival in a clinically heterogeneous cohort of patients with high-grade gliomas. MATERIALS AND METHODS: Fifty patients with high-grade gliomas from our hospital and 128 patients with high-grade glioma from The Cancer Genome Atlas were included. For each patient, we calculated 348 hand-crafted radiomics features and 8192 deep features generated by a pretrained convolutional neural network. We then applied feature selection and Elastic Net-Cox modeling to differentiate patients into long- and short-term survivors. RESULTS: In the 50 patients with high-grade gliomas from our institution, the combined feature analysis framework classified the patients into long- and short-term survivor groups with a log-rank test P value < .001. In the 128 patients from The Cancer Genome Atlas, the framework classified patients into long- and short-term survivors with a log-rank test P value of .014. For the mixed cohort of 50 patients from our institution and 58 patients from The Cancer Genome Atlas, it yielded a log-rank test P value of .035. CONCLUSIONS: A deep learning model combining deep and radiomics features can dichotomize patients with high-grade gliomas into long- and short-term survivors.

BACKGROUND AND PURPOSE: Estimates of cumulative CT/CTA radiation dose based on volumetric CT dose index have raised concern that neurological intensive care unit patient exposures may reach thresholds for deterministic skin injury. Because the accuracy of volumetric CT dose index for this purpose in unknown, we set out to directly measure head CT and CTA peak skin dose, assess the relationship of volumetric CT dose index to measured peak skin dose, and determine whether multiple CT/CTA exposures in typical patients in the neurological intensive care unit produce cumulative doses approaching or exceeding single-dose deterministic thresholds for skin injury. MATERIALS AND METHODS: In a prospective study from 2011–2013, nanoDot optical stimulated luminescence dosimeters were used to measure head CT/CTA peak skin dose in 52 patients (28 female, 24 male; mean age, 63 years) divided equally between 2 CT scanners. Volumetric CT dose index and dose-length product were recorded for each examination. Peak skin dose was also measured on an acrylic skull phantom in each scanner. A 2-tailed, unpaired t test was used to compare mean patient skin doses between the 2 scanners. The measured peak skin doses were then used to calculate cumulative peak skin dose in 4 typical patients in intensive care units who received multiple CT/CTA scans. RESULTS: Head CT/CTA peak skin dose agreed between scanners in patients and phantoms: (scanner 1 CT/CTA: patients, 39.2 ± 3.7 mGy and 98.9 ± 5.3 mGy, respectively, versus phantom, 40.0 mGy and 105.4 mGy, respectively; scanner 2 CT/CTA: patients, 42.9 ± 9.4 mGy and 98.8 ± 7.4 mGy, respectively, versus phantom, 37.6 mGy and 95.2 mGy, respectively). Volumetric CT dose index overestimated peak skin dose by a factor of 1.4–1.9 depending on examination and CT scanner. Cumulative doses in 4 patients in the intensive care unit estimated from measured CT/CTA peak skin dose ranged from 1.9–4.5 Gy. CONCLUSIONS: Directly measured radiation skin doses from head CT/CTA patient examinations are substantially lower than volumetric CT dose index. Measured peak skin dose confirms that multiple head CT/CTA examinations in representative patients in the neurological intensive care unit may produce cumulative doses exceeding the single-dose deterministic threshold for skin injury.

Blood circulation in the human brain is supplied through a network of cerebral arteries. If a clinician suspects a patient has a stroke or other cerebrovascular condition they order imaging tests. Neuroradiologists visually search the resulting scans for abnormalities. Their visual search tasks correspond to the abstract network analysis tasks of browsing and path following. To assist neuroradiologists in identifying cerebral artery abnormalities we designed CerebroVis, a novel abstract---yet spatially contextualized---cerebral artery network visualization. In this design study, we contribute a novel framing and definition of the cerebral artery system in terms of network theory and characterize neuroradiologist domain goals as abstract visualization and network analysis tasks. Through an iterative, user-centered design process we developed an abstract network layout technique which incorporates cerebral artery spatial context. The abstract visualization enables increased domain task performance over 3D geometry representations, while including spatial context helps preserve the user's mental map of the underlying geometry. We provide open source implementations of our network layout technique and prototype cerebral artery visualization tool. We demonstrate the robustness of our technique by successfully laying out 61 open source brain scans. We evaluate the effectiveness of our layout through a mixed methods study with three neuroradiologists. In a formative controlled experiment our study participants used CerebroVis and a conventional 3D visualization to examine real cerebral artery imaging data and to identify a simulated intracranial artery stenosis. Participants were more accurate at identifying stenoses using CerebroVis (absolute risk difference 13%). A free copy of this paper, the evaluation stimuli and data, and source code are available at https://osf.io/e5sxt/., Comment: IEEE InfoVis 2019 ACM 2012 CCS-Human-centered computing,Visualization,Visualization application domains,Information visualization ACM 2012 CCS-Human-centered computing,Visualization,Visualization techniques,Graph drawings

In many applications, image deblurring is a pre-requisite to improve the sharpness of an image before it can be further processed. Iterative methods are widely used for deblurring images but care must be taken to ensure that the iterative process is robust, meaning that the process does not diverge and reaches the solution reasonably fast, two goals that sometimes compete against each other. In practice, it remains challenging to choose parameters for the iterative process to be robust. We propose a new approach consisting of relaxed initialization and pixel-wise updates of the step size for iterative methods to achieve robustness. The first novel design of the approach is to modify the initialization of existing iterative methods to stop a noise term from being propagated throughout the iterative process. The second novel design is the introduction of a vectorized step size that is adaptively determined through the iteration to achieve higher stability and accuracy in the whole iterative process. The vectorized step size aims to update each pixel of an image individually, instead of updating all the pixels by the same factor. In this work, we implemented the above designs based on the Landweber method to test and demonstrate the new approach. Test results showed that the new approach can deblur images from noisy observations and achieve a low mean squared error with a more robust performance.

Background Approximately one-fourth of all cancer metastases are found in the brain. MRI is the primary technique for detection of brain metastasis, planning of radiotherapy, and the monitoring of treatment response. Progress in tumor treatment now requires detection of new or growing metastases at the small subcentimeter size, when these therapies are most effective. Purpose To develop a deep-learning-based approach for finding brain metastasis on MRI. Study type Retrospective. Sequence Axial postcontrast 3D T1 -weighted imaging. Field strength 1.5T and 3T. Population A total of 361 scans of 121 patients were used to train and test the Faster region-based convolutional neural network (Faster R-CNN): 1565 lesions in 270 scans of 73 patients for training; 488 lesions in 91 scans of 48 patients for testing. From the 48 outputs of Faster R-CNN, 212 lesions in 46 scans of 18 patients were used for training the RUSBoost algorithm (MatLab) and 276 lesions in 45 scans of 30 patients for testing. Assessment Two radiologists diagnosed and supervised annotation of metastases on brain MRI as ground truth. This data were used to produce a 2-step pipeline consisting of a Faster R-CNN for detecting abnormal hyperintensity that may represent brain metastasis and a RUSBoost classifier to reduce the number of false-positive foci detected. Statistical tests The performance of the algorithm was evaluated by using sensitivity, false-positive rate, and receiver's operating characteristic (ROC) curves. The detection performance was assessed both per-metastases and per-slice. Results Testing on held-out brain MRI data demonstrated 96% sensitivity and 20 false-positive metastases per scan. The results showed an 87.1% sensitivity and 0.24 false-positive metastases per slice. The area under the ROC curve was 0.79. Conclusion Our results showed that deep-learning-based computer-aided detection (CAD) had the potential of detecting brain metastases with high sensitivity and reasonable specificity. Level of evidence 3 TECHNICAL EFFICACY STAGE: 2 J. Magn. Reson. Imaging 2020;52:1227-1236.

OBJECTIVECushing’s disease (CD) involves brain impairments caused by excessive cortisol. Whether these impairments are reversible in remitted CD after surgery has long been controversial due to a lack of high-quality longitudinal studies. In this study the authors aimed to assess the reversibility of whole-brain changes in remitted CD after transsphenoidal surgery (TSS), and its correlations with clinical and hormonal parameters, in the largest longitudinal study cohort to date for CD patient brain analysis.METHODSFifty patients with pathologically diagnosed CD and 36 matched healthy controls (HCs) were enrolled in a tertiary comprehensive hospital and national pituitary disease registry center in China. 3-T MRI studies were analyzed using an artificial intelligence–assisted web-based autosegmentation tool to quantify 3D brain volumes. Clinical parameters as well as levels of serum cortisol, adrenocorticotrophic hormone (ACTH), and 24-hour urinary free cortisol were collected for the correlation analysis. All CD patients underwent TSS and 46 patients achieved remission. All clinical, hormonal, and MRI parameters were reevaluated at the 3-month follow-up after surgery.RESULTSWidespread brain volume loss was observed in active CD patients compared with HCs, including total gray matter (p = 0.003, with false discovery rate [FDR] correction) and the frontal, parietal, occipital, and temporal lobes; insula; cingulate lobe; and enlargement of lateral and third ventricles (p < 0.05, corrected with FDR). All affected brain regions improved significantly after TSS (p < 0.05, corrected with FDR). In patients with remitted CD, total gray matter and most brain regions (except the frontal and temporal lobes) showed full recovery of volume, with volumes that did not differ from those of HCs (p > 0.05, corrected with FDR). ACTH and serum cortisol changes were negatively correlated with brain volume changes during recovery (p < 0.05).CONCLUSIONSThis study demonstrates the rapid reversal of total gray matter loss in remitted CD. The combination of full recovery areas and partial recovery areas after TSS is consistent with the incomplete recovery of memory and cognitive function observed in CD patients in clinical practice. Correlation analyses suggest that ACTH and serum cortisol levels are reliable serum biomarkers of brain recovery for clinical use after surgery.

PURPOSE Phosphatidylinositol 3-kinase (PI3K) signaling is highly active in glioblastomas. We assessed pharmacokinetics, pharmacodynamics, and efficacy of the pan-PI3K inhibitor buparlisib in patients with recurrent glioblastoma with PI3K pathway activation. METHODS This study was a multicenter, open-label, multi-arm, phase II trial in patients with PI3K pathway–activated glioblastoma at first or second recurrence. In cohort 1, patients scheduled for re-operation after progression received buparlisib for 7 to 13 days before surgery to evaluate brain penetration and modulation of the PI3K pathway in resected tumor tissue. In cohort 2, patients not eligible for re-operation received buparlisib until progression or unacceptable toxicity. Once daily oral buparlisib 100 mg was administered on a continuous 28-day schedule. Primary end points were PI3K pathway inhibition in tumor tissue and buparlisib pharmacokinetics in cohort 1 and 6-month progression-free survival (PFS6) in cohort 2. RESULTS Sixty-five patients were treated (cohort 1, n = 15; cohort 2, n = 50). In cohort 1, reduction of phosphorylated AKTS473 immunohistochemistry score was achieved in six (42.8%) of 14 patients, but effects on phosphoribosomal protein S6S235/236 and proliferation were not significant. Tumor-to-plasma drug level was 1.0. In cohort 2, four (8%) of 50 patients reached 6-month PFS6, and the median PFS was 1.7 months (95% CI, 1.4 to 1.8 months). The most common grade 3 or greater adverse events related to treatment were lipase elevation (n = 7 [10.8%]), fatigue (n = 4 [6.2%]), hyperglycemia (n = 3 [4.6%]), and elevated ALT (n = 3 [4.6%]). CONCLUSION Buparlisib had minimal single-agent efficacy in patients with PI3K-activated recurrent glioblastoma. Although buparlisib achieved significant brain penetration, the lack of clinical efficacy was explained by incomplete blockade of the PI3K pathway in tumor tissue. Integrative results suggest that additional study of PI3K inhibitors that achieve more-complete pathway inhibition may still be warranted.

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.

2521 Background: Despite concerns of immunotherapy-induced inflammatory response during PD1 inhibition, questions remain as to the true incidence of inflammatory response and potential clinical implications. The goals of the project were to use GBM patients pooled from academic centers to estimate radiographic PFS and OS, as well as determine the incidence of immunotherapy-induced inflammatory response. Methods: 152 patients with newly diagnosed (N = 57) or recurrent (N = 95) GBM treated with either nivolumab or pembrolizumab from Dana-Farber (N = 88), UCLA (N = 35) or UCSF (N = 29) were included in this study. Radiographic progression was defined by a 25% increase in bidirectional measurements according to RANO. Results: Median PFS and OS for newly diagnosed patients was 162 and 520 days, respectively, while median PFS and OS for recurrent patients was 72 and 225 days, respectively. No difference in OS was observed in recurrent patients treated with nivolumab vs. pembrolizumab (P = 0.58), but recurrent patients treated with nivolumab trended toward a longer PFS (P = 0.097). Of the recurrent patients with OS and PFS data available and radiographic progression, 95% of them progressed within 6 mos of starting treatment. Median post-progression survival (PPS) in recurrent patients with PFS < 6 mos was 151 days, while PPS for patients with PFS > 6 mos was 178 days (P = 0.51). In the 77 recurrent patients who progressed within 6 mos, 36.4% had an OS that was > 9 mos, while 63.6% had an OS < 9 mos, suggesting the majority of patients with “early progression” also died early and a minority of patients had what could be considered “immune-related inflammation”. Of the 81 recurrent patients with documented progression, only 2.5% showed stabilization within 3 mos of first progression, 30.9% died before the 3-month confirmation scan could be obtained, 24.7% showed continued tumor growth by 3 months, and 48.1% had no follow-up confirmatory imaging exams. Of the 70 patients who progressed within 6 mos and had documented death, 2.9% had disease stabilization, 31.4% died before the 3-month confirmation, and 75.7% had either documented tumor growth or had no follow-up confirmatory imaging exams. Conclusions: This study suggests immunotherapy-induced inflammation followed by a favorable PPS is uncommon in GBM. While patients treated with other types of immunotherapy may exhibit different imaging characteristics, these data provide an important basis to refine the iRANO criteria.

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.

PURPOSE: To validate Gaussian normalized cerebral blood volume (GN-nCBV) by association with overall survival (OS) in newly diagnosed glioblastoma patients and compare this association with current standard white matter normalized cerebral blood volume (WN-nCBV). METHODS: We retrieved spin-echo echo-planar dynamic susceptibility contrast MRI acquired after maximal resection and prior to radiation therapy between 2006 and 2011 in 51 adult patients (28 male, 23 female; age 23-87 years) with newly diagnosed glioblastoma. Software code was developed in house to perform Gaussian normalization of CBV to the standard deviation of the whole brain CBV. Three expert readers manually selected regions of interest in tumor and normal-appearing white matter on CBV maps. Receiver operating characteristics (ROC) curves associating nCBV with 15-month OS were calculated for both GN-nCBV and WN-nCBV. Reproducibility and interoperator variability were compared using within-subject coefficient of variation (wCV) and intraclass correlation coefficients (ICCs). RESULTS: GN-nCBV ICC (≥0.82) and wCV (≤21%) were superior to WN-nCBV ICC (0.54-0.55) and wCV (≥46%). The area under the ROC curve analysis demonstrated both GN-nCBV and WN-nCBV to be good predictors of OS, but GN-nCBV was consistently superior, although the difference was not statistically significant. CONCLUSION: GN-nCBV has a slightly better association with clinical gold standard OS than conventional WM-nCBV in our glioblastoma patient cohort. This equivalent or superior validity, combined with the advantages of higher reproducibility, lower interoperator variability, and easier automation, makes GN-nCBV superior to WM-nCBV for clinical and research use in glioma patients. We recommend widespread adoption and incorporation of GN-nCBV into commercial dynamic susceptibility contrast processing software.

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.

Spin-echo echo planar (EP) perfusion weighted imaging (SE-PWI) has been demonstrated to be more selective than gradient-echo EP PWI for blood volume in microvessels the size of glioma neocapillaries, but it has not been comprehensively studied in human clinical use. We assessed whether SE-PWI before and after initiating chemoradiation can stratify patients with respect to progression free survival (PFS) and overall survival (OS). Sixty-eight patients with newly diagnosed glioblastoma (mean age 58.3, 36 males) were included in analysis. SE EP cerebral blood volumes (SE-CBVs) in enhancing and nonenhancing tumor, normalized to contralateral normal appearing white matter (SE-nCBV), were assessed at baseline and after initial chemoradiation. SE-nCBV parameters predictive of PFS and OS were identified in univariate and multivariate Cox proportional hazards models. Multivariate analysis demonstrated that baseline tumor mean SE-nCBV was predictive of PFS (p = 0.038) and OS (p = 0.004). Within the patient sample, baseline tumor mean SE-nCBV2.0 predicted longer patient PFS (median 47.0 weeks, p0.001) and OS (median 98.6 weeks, p = 0.003) compared with baseline mean SE-nCBV2.0 (median PFS 25.3, median OS 56.0 weeks). Exploratory multi-group stratification demonstrated that very high (4.0) tumor SE-nCBV was associated with worse patient OS than intermediate high (2.0,4.0) SE-nCBV (p = 0.025). Baseline mean SE-nCBV can stratify patients for PFS and OS prior to initiation of chemoradiation, which may help select patients who require closer surveillance. Our exploratory analysis indicates a magnitude-dependent relationship between baseline SE-nCBV and OS.

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.

Conventional histopathology with hematoxylin & eosin (H&E) has been the gold standard for histopathological diagnosis of a wide range of diseases. However, it is not performed in vivo and requires thin tissue sections obtained after tissue biopsy, which carries risk, particularly in the central nervous system. Here we describe the development of an alternative, multicolored way to visualize tissue in real-time through the use of coherent Raman imaging (CRI), without the use of dyes. CRI relies on intrinsic chemical contrast based on vibrational properties of molecules and intrinsic optical sectioning by nonlinear excitation. We demonstrate that multicolor images originating from CH(2) and CH(3) vibrations of lipids and protein, as well as two-photon absorption of hemoglobin, can be obtained with subcellular resolution from fresh tissue. These stain-free histopathological images show resolutions similar to those obtained by conventional techniques, but do not require tissue fixation, sectioning or staining of the tissue analyzed.