Synthesizing 3D Multi-Contrast Brain Tumor MRIs Using Tumor Mask Conditioning.

Data scarcity and data imbalance are two major challenges in training deep learning models on medical images, such as brain tumor MRI data. The recent advancements in generative artificial intelligence have opened new possibilities for synthetically generating MRI data, including brain tumor MRI scans. This approach can be a potential solution to mitigate the data scarcity problem and enhance training data availability. This work focused on adapting the 2D latent diffusion models to generate 3D multi-contrast brain tumor MRI data with a tumor mask as the condition. The framework comprises two components: a 3D autoencoder model for perceptual compression and a conditional 3D Diffusion Probabilistic Model (DPM) for generating high-quality and diverse multi-contrast brain tumor MRI samples, guided by a conditional tumor mask. Unlike existing works that focused on generating either 2D multi-contrast or 3D single-contrast MRI samples, our models generate multi-contrast 3D MRI samples. We also integrated a conditional module within the UNet backbone of the DPM to capture the semantic class-dependent data distribution driven by the provided tumor mask to generate MRI brain tumor samples based on a specific brain tumor mask. We trained our models using two brain tumor datasets: The Cancer Genome Atlas (TCGA) public dataset and an internal dataset from the University of Texas Southwestern Medical Center (UTSW). The models were able to generate high-quality 3D multi-contrast brain tumor MRI samples with the tumor location aligned by the input condition mask. The quality of the generated images was evaluated using the Fréchet Inception Distance (FID) score. This work has the potential to mitigate the scarcity of brain tumor data and improve the performance of deep learning models involving brain tumor MRI data.

Two-Stage Training Framework Using Multicontrast MRI Radiomics for IDH Mutation Status Prediction in Glioma.

Purpose To develop a radiomics framework for preoperative MRI-based prediction of isocitrate dehydrogenase (IDH) mutation status, a crucial glioma prognostic indicator. Materials and Methods Radiomics features (shape, first-order statistics, and texture) were extracted from the whole tumor or the combination of nonenhancing, necrosis, and edema regions. Segmentation masks were obtained via the federated tumor segmentation tool or the original data source. Boruta, a wrapper-based feature selection algorithm, identified relevant features. Addressing the imbalance between mutated and wild-type cases, multiple prediction models were trained on balanced data subsets using random forest or XGBoost and assembled to build the final classifier. The framework was evaluated using retrospective MRI scans from three public datasets (The Cancer Imaging Archive [TCIA, 227 patients], the University of California San Francisco Preoperative Diffuse Glioma MRI dataset [UCSF, 495 patients], and the Erasmus Glioma Database [EGD, 456 patients]) and internal datasets collected from the University of Texas Southwestern Medical Center (UTSW, 356 patients), New York University (NYU, 136 patients), and University of Wisconsin-Madison (UWM, 174 patients). TCIA and UTSW served as separate training sets, while the remaining data constituted the test set (1617 or 1488 testing cases, respectively). Results The best performing models trained on the TCIA dataset achieved area under the receiver operating characteristic curve (AUC) values of 0.89 for UTSW, 0.86 for NYU, 0.93 for UWM, 0.94 for UCSF, and 0.88 for EGD test sets. The best performing models trained on the UTSW dataset achieved slightly higher AUCs: 0.92 for TCIA, 0.88 for NYU, 0.96 for UWM, 0.93 for UCSF, and 0.90 for EGD. Conclusion This MRI radiomics-based framework shows promise for accurate preoperative prediction of IDH mutation status in patients with glioma. Keywords: Glioma, Isocitrate Dehydrogenase Mutation, IDH Mutation, Radiomics, MRI Supplemental material is available for this article. Published under a CC BY 4.0 license. See also commentary by Moassefi and Erickson in this issue.

Expert-centered Evaluation of Deep Learning Algorithms for Brain Tumor Segmentation.

Purpose To present results from a literature survey on practices in deep learning segmentation algorithm evaluation and perform a study on expert quality perception of brain tumor segmentation. Materials and Methods A total of 180 articles reporting on brain tumor segmentation algorithms were surveyed for the reported quality evaluation. Additionally, ratings of segmentation quality on a four-point scale were collected from medical professionals for 60 brain tumor segmentation cases. Results Of the surveyed articles, Dice score, sensitivity, and Hausdorff distance were the most popular metrics to report segmentation performance. Notably, only 2.8% of the articles included clinical experts' evaluation of segmentation quality. The experimental results revealed a low interrater agreement (Krippendorff α, 0.34) in experts' segmentation quality perception. Furthermore, the correlations between the ratings and commonly used quantitative quality metrics were low (Kendall tau between Dice score and mean rating, 0.23; Kendall tau between Hausdorff distance and mean rating, 0.51), with large variability among the experts. Conclusion The results demonstrate that quality ratings are prone to variability due to the ambiguity of tumor boundaries and individual perceptual differences, and existing metrics do not capture the clinical perception of segmentation quality. Keywords: Brain Tumor Segmentation, Deep Learning Algorithms, Glioblastoma, Cancer, Machine Learning Clinical trial registration nos. NCT00756106 and NCT00662506 Supplemental material is available for this article. © RSNA, 2023.

MRI-Based Deep Learning Method for Classification of IDH Mutation Status.

Isocitrate dehydrogenase (IDH) mutation status has emerged as an important prognostic marker in gliomas. This study sought to develop deep learning networks for non-invasive IDH classification using T2w MR images while comparing their performance to a multi-contrast network. Methods: Multi-contrast brain tumor MRI and genomic data were obtained from The Cancer Imaging Archive (TCIA) and The Erasmus Glioma Database (EGD). Two separate 2D networks were developed using nnU-Net, a T2w-image-only network (T2-net) and a multi-contrast network (MC-net). Each network was separately trained using TCIA (227 subjects) or TCIA + EGD data (683 subjects combined). The networks were trained to classify IDH mutation status and implement single-label tumor segmentation simultaneously. The trained networks were tested on over 1100 held-out datasets including 360 cases from UT Southwestern Medical Center, 136 cases from New York University, 175 cases from the University of Wisconsin-Madison, 456 cases from EGD (for the TCIA-trained network), and 495 cases from the University of California, San Francisco public database. A receiver operating characteristic curve (ROC) was drawn to calculate the AUC value to determine classifier performance. Results: T2-net trained on TCIA and TCIA + EGD datasets achieved an overall accuracy of 85.4% and 87.6% with AUCs of 0.86 and 0.89, respectively. MC-net trained on TCIA and TCIA + EGD datasets achieved an overall accuracy of 91.0% and 92.8% with AUCs of 0.94 and 0.96, respectively. We developed reliable, high-performing deep learning algorithms for IDH classification using both a T2-image-only and a multi-contrast approach. The networks were tested on more than 1100 subjects from diverse databases, making this the largest study on image-based IDH classification to date.

Longitudinal assessment of mitochondrial dysfunction in acute traumatic brain injury using hyperpolarized [1-13 C]pyruvate.

PURPOSE: [13 C]Bicarbonate formation from hyperpolarized [1-13 C]pyruvate via pyruvate dehydrogenase, a key regulatory enzyme, represents the cerebral oxidation of pyruvate and the integrity of mitochondrial function. The present study is to characterize the chronology of cerebral mitochondrial metabolism during secondary injury associated with acute traumatic brain injury (TBI) by longitudinally monitoring [13 C]bicarbonate production from hyperpolarized [1-13 C]pyruvate in rodents. METHODS: Male Wistar rats were randomly assigned to undergo a controlled-cortical impact (CCI, n = 31) or sham surgery (n = 22). Seventeen of the CCI and 9 of the sham rats longitudinally underwent a 1 H/13 C-integrated MR protocol that includes a bolus injection of hyperpolarized [1-13 C]pyruvate at 0 (2 h), 1, 2, 5, and 10 days post-surgery. Separate CCI and sham rats were used for histological validation and enzyme assays. RESULTS: In addition to elevated lactate, we observed significantly reduced bicarbonate production in the injured site. Unlike the immediate appearance of hyperintensity on T2 -weighted MRI, the contrast of bicarbonate signals between the injured region and the contralateral brain peaked at 24 h post-injury, then fully recovered to the normal level at day 10. A subset of TBI rats demonstrated markedly increased bicarbonate in normal-appearing contralateral brain regions post-injury. CONCLUSION: This study demonstrates that aberrant mitochondrial metabolism occurring in acute TBI can be monitored by detecting [13 C]bicarbonate production from hyperpolarized [1-13 C]pyruvate, suggesting that [13 C]bicarbonate is a sensitive in-vivo biomarker of the secondary injury processes.

Deep-learning-enabled brain hemodynamic mapping using resting-state fMRI.

Cerebrovascular disease is a leading cause of death globally. Prevention and early intervention are known to be the most effective forms of its management. Non-invasive imaging methods hold great promises for early stratification, but at present lack the sensitivity for personalized prognosis. Resting-state functional magnetic resonance imaging (rs-fMRI), a powerful tool previously used for mapping neural activity, is available in most hospitals. Here we show that rs-fMRI can be used to map cerebral hemodynamic function and delineate impairment. By exploiting time variations in breathing pattern during rs-fMRI, deep learning enables reproducible mapping of cerebrovascular reactivity (CVR) and bolus arrival time (BAT) of the human brain using resting-state CO2 fluctuations as a natural "contrast media". The deep-learning network is trained with CVR and BAT maps obtained with a reference method of CO2-inhalation MRI, which includes data from young and older healthy subjects and patients with Moyamoya disease and brain tumors. We demonstrate the performance of deep-learning cerebrovascular mapping in the detection of vascular abnormalities, evaluation of revascularization effects, and vascular alterations in normal aging. In addition, cerebrovascular maps obtained with the proposed method exhibit excellent reproducibility in both healthy volunteers and stroke patients. Deep-learning resting-state vascular imaging has the potential to become a useful tool in clinical cerebrovascular imaging.

Structural MRI Ratios Fail to Distinguish Progressive Supranuclear Palsy From Parkinson Disease in Individual Patients.

Background and Objectives: Parkinson disease (PD) and progressive supranuclear palsy (PSP) are often difficult to differentiate in the clinic. The MR parkinsonism index (MRPI) has been recommended to assist in making this distinction. We aimed to assess the usefulness of this tool in our real-world practice of movement disorders. Methods: We prospectively obtained MRI scans on consecutive patients with movement disorders with a clinical indication for imaging and obtained measures of MRI regions of interest (ROIs) from our neuroradiologists. The authors reviewed all MRI scans and corrected any errors in the original ROI drawings for this analysis. We retrospectively assigned diagnoses using established consensus criteria from progress notes stored in our electronic medical record. We analyzed the data using multinomial logistic regression models and receiver operating curve analysis to determine the predictive accuracy of the MRI ratios. Results: MRI measures and consensus diagnoses were available on 130 patients with PD, 54 with PSP, and 77 diagnosed as other. The out-of-sample prediction error rate of our 5 regression models ranged from 45% to 59%. The average sensitivity and specificity of the 5 models in the testing sample were 53% and 80%, respectively. The positive predictive value of an MRPI ≥13.55 (the published cutoff) in our patients was 79%. Discussion: These results indicate that MRI measures of brain structures were not effective at predicting diagnosis in individual patients. We conclude that the search for a biomarker that can differentiate PSP from PD must continue.

Errata: Brain tumor IDH, 1p/19q, and MGMT molecular classification using MRI-based deep learning: an initial study on the effect of motion and motion correction.

[This corrects the article DOI: 10.1117/1.JMI.9.1.016001.].

Corrigendum to: A novel fully automated MRI-based deep-learning method for classification of 1p/19q co-deletion status in brain gliomas.

[This corrects the article DOI: 10.1093/noajnl/vdaa066.].

Selective vulnerability of brainstem and cervical spinal cord regions in people with non-progressive multiple sclerosis of Black or African American and European ancestry.

BACKGROUND: We evaluated imaging features suggestive of neurodegeneration within the brainstem and upper cervical spinal cord (UCSC) in non-progressive multiple sclerosis (MS). METHODS: Standardized 3-Tesla three-dimensional brain magnetic resonance imaging (MRI) studies were prospectively acquired. Rates of change in volume, surface texture, curvature were quantified at the pons and medulla-UCSC. Whole and regional brain volumes and T2-weighted lesion volumes were also quantified. Independent regression models were constructed to evaluate differences between those of Black or African ancestry (B/AA) and European ancestry (EA) with non-progressive MS. RESULTS: 209 people with MS (pwMS) having at least two MRI studies, 29% possessing 3-6 timepoints, resulted in 487 scans for analysis. Median follow-up time between MRI timepoints was 1.33 (25th-75th percentile: 0.51-1.98) years. Of 183 non-progressive pwMS, 88 and 95 self-reported being B/AA and EA, respectively. Non-progressive pwMS demonstrated greater rates of decline in pontine volume (p < 0.0001) in B/AA and in medulla-UCSC volume (p < 0.0001) for EA pwMS. Longitudinal surface texture and curvature changes suggesting reduced tissue integrity were observed at the ventral medulla-UCSC (p < 0.001), dorsal pons (p < 0.0001) and dorsal medulla (p < 0.0001) but not the ventral pons (p = 0.92) between groups. CONCLUSIONS: Selectively vulnerable regions within the brainstem-UCSC may allow for more personalized approaches to disease surveillance and management.

CVR-MRICloud: An online processing tool for CO2-inhalation and resting-state cerebrovascular reactivity (CVR) MRI data.

Cerebrovascular Reactivity (CVR) provides an assessment of the brain's vascular reserve and has been postulated to be a sensitive marker in cerebrovascular diseases. MRI-based CVR measurement typically employs alterations in arterial carbon dioxide (CO2) level while continuously acquiring Blood-Oxygenation-Level-Dependent (BOLD) images. CO2-inhalation and resting-state methods are two commonly used approaches for CVR MRI. However, processing of CVR MRI data often requires special expertise and may become an obstacle in broad utilization of this promising technique. The aim of this work was to develop CVR-MRICloud, a cloud-based CVR processing pipeline, to enable automated processing of CVR MRI data. The CVR-MRICloud consists of several major steps including extraction of end-tidal CO2 (EtCO2) curve from raw CO2 recording, alignment of EtCO2 curve with BOLD time course, computation of CVR value on a whole-brain, regional, and voxel-wise basis. The pipeline also includes standard BOLD image processing steps such as motion correction, registration between functional and anatomic images, and transformation of the CVR images to canonical space. This paper describes these algorithms and demonstrates the performance of the CVR-MRICloud in lifespan healthy subjects and patients with clinical conditions such as stroke, brain tumor, and Moyamoya disease. CVR-MRICloud has potential to be used as a data processing tool for a variety of basic science and clinical applications.

Association of distal hyperintense vessel sign and recurrent stroke in patients with symptomatic intracranial stenosis.

OBJECTIVE: The distal hyperintense vessel sign (DHV) on fluid-attenuated inversion recovery magnetic resonance image (MRI) is an imaging biomarker of slow leptomeningeal collateral flow in the presence of large artery stenosis or occlusion reflecting impaired cerebral hemodynamics. In this study, we aim to investigate the significance of the DHV sign in patients with symptomatic ≥ 70% intracranial atherosclerotic stenosis. METHODS: We retrospectively reviewed patients with ischemic stroke or transient ischemic attack admitted to a single center from January 2010 to December 2017. Patients were included if they had symptomatic ≥ 70% atherosclerotic stenosis of the intracranial internal carotid artery or middle cerebral artery. The presence of the DHV sign was evaluated by blinded neuroradiologist and vascular neurologists. Recurrent ischemic stroke in the vascular territory of symptomatic intracranial artery was defined as new neurological deficits with associated neuroimaging findings during the follow up period. RESULTS: A total of 109 patients were included in the study, of which 55 had DHV sign. Average duration of follow up was 297 ± 326 days. Four patients were lost during follow up. Patients with the DHV sign had a higher rate of recurrent ischemic stroke (38%), compared to patients without the DHV sign (17%; p=0.018). In multivariate regression analysis, the presence of DHV sign was an independent predictor of recurrent ischemic stroke. A DHV score of ≥ 2 had a 63% sensitivity and 69% specificity for recurrent ischemic stroke. INTERPRETATION: In patients with severe symptomatic intracranial atherosclerotic stenosis, those with a DHV sign on MRI are at higher risk of recurrent ischemic stroke.

Does the use of fiber posts increase the fracture resistance of simulated immature teeth? A systematic review.

This systematic review aimed to assess if the use of fiber posts reinforces weakened immature teeth. A systematic review was conducted of laboratory studies that evaluated the fracture resistance of simulated immature teeth restored with fiber posts compared to teeth restored exclusively with resin. An electronic search was performed using the following databases: PubMed/MEDLINE, Web of Science, Scopus and LILACS, BBO, and grey literature. Two independent researchers screened the titles and abstracts of the retrieved studies for relevance to the research question. Subsequently, the full texts of potentially relevant studies were screened based on the exclusion criteria. Ten out of 1792 unique records were included in this systematic review. Risk of bias was assessed using an adapted tool based on the Cochrane risk of bias tool. The laboratory studies included in this systematic review were performed on both human and bovine teeth. Eight studies concluded that fiber posts reinforce the structure of weakened roots, and two studies reported that fiber posts did not strengthen the radicular structure compared to teeth exclusively restored with resin composite. The highly heterogeneous data made it challenging to synthesize the results into a summary estimate, and thus no meta-analysis was undertaken. A summary effect could not be estimated without a meta-analysis. Although the laboratory literature suggests that fiber posts reinforce the structure of immature teeth, the results should be interpreted with caution, as most of the studies had an unclear or high risk of bias.

Imaging characteristics of 4th ventricle subependymoma.

PURPOSE: Subependymomas located within the 4th ventricle are rare, and the literature describing imaging characteristics is sparse. Here, we describe the clinical and radiological characteristics of 29 patients with 4th ventricle subependymoma. METHODS: This is a retrospective multi-center study performed after Institutional Review Board (IRB) approval. Patients diagnosed with suspected 4th ventricle subependymoma were identified. A review of clinical, radiology, and pathology reports along with magnetic resonance imaging (MRI) images was performed. RESULTS: Twenty-nine patients, including 6 females, were identified. Eighteen patients underwent surgery with histopathological confirmation of subependymoma. The median age at diagnosis was 52 years. Median tumor volume for the operative cohort was 9.87 cm3, while for the non-operative cohort, it was 0.96 cm3. Thirteen patients in the operative group exhibited symptoms at diagnosis. For the total cohort, the majority of subependymomas (n = 22) were isointense on T1, hyperintense (n = 22) on T2, and enhanced (n = 24). All tumors were located just below the body of the 4th ventricle, terminating near the level of the obex. Fourteen cases demonstrated extension of tumor into foramen of Magendie or Luschka. CONCLUSION: To the best of our knowledge, this is the largest collection of 4th ventricular subependymomas with imaging findings reported to date. All patients in this cohort had tumors originating between the bottom of the body of the 4th ventricle and the obex. This uniform and specific site of origin aids with imaging diagnosis and may infer possible theories of origin.

Direction and magnitude of displacement differ between slowly expanding and non-expanding multiple sclerosis lesions as compared to small vessel disease.

BACKGROUND AND PURPOSE: Differentiating between multiple sclerosis (MS) and small vessel disease (SVD) lesions represents a key challenge in the day-to-day management of patients. We aimed to distinguish between MS and SVD by identifying the dynamics of lesion movement patterns between enlarging and contracting foci from two MRI time points. METHODS: Standardized 3-Tesla 3-dimensional brain magnetic resonance imaging (MRI) studies were performed at two time points on enrolled MS and SVD patients. Selected supratentorial lesions were segmented and longitudinal changes in the direction of lesion displacement and magnitude along with the evolution of contracting and expanding T1-weighted and T2-weighted MS lesions were quantified based on lesion centroid positioning. Bayesian linear mixed effects regression models were constructed to evaluate associations between changes in lesion transitions and disease state. RESULTS: A total of 420 lesions were analyzed from 35 MS (female (F):22 (62.9%); median age (range):38 years (y) (22-61), median disease duration:7.38y (0.38-20.99)) and 12 SVD patients (F:11 (100%); 54y (40-66)). MS T2-weighted lesions that increased in volume between MRI time points demonstrated movement toward the cortex (p = 0.01), whereas those that decreased in volume moved toward the center (p < 0.0001). Lesion volume changes related to SVD demonstrated no effect on movement direction over time. Both expanding (p = 0.03) and contracting (p = 0.01) MS lesions demonstrated greater distances between centroids when compared to SVD. CONCLUSION: Lesion dynamics may reveal distinct characteristics associated with the biology of disease while providing further insights into the behavior of inflammatory CNS disorders.

Pitfalls and Recommended Strategies and Metrics for Suppressing Motion Artifacts in Functional MRI.

In resting-state functional magnetic resonance imaging (rs-fMRI), artefactual signals arising from subject motion can dwarf and obfuscate the neuronal activity signal. Typical motion correction approaches involve the generation of nuisance regressors, which are timeseries of non-brain signals regressed out of the fMRI timeseries to yield putatively artifact-free data. Recent work suggests that concatenating all regressors into a single regression model is more effective than the sequential application of individual regressors, which may reintroduce previously removed artifacts. This work compares 18 motion correction pipelines consisting of head motion, independent components analysis, and non-neuronal physiological signal regressors in sequential or concatenated combinations. The pipelines are evaluated on a dataset of cognitively normal individuals with repeat imaging and on datasets of studies of Autism Spectrum Disorder, Major Depressive Disorder, and Parkinson's Disease. Extensive metrics of motion artifact removal are measured, including resting state network recovery, Quality Control-Functional Connectivity (QC-FC) correlation, distance-dependent artifact, network modularity, and test-retest reliability of multiple rs-fMRI analyses. The results reveal limitations in previously proposed metrics, including the QC-FC correlation and modularity quality, and identify more robust artifact removal metrics. The results also reveal limitations in the concatenated regression approach, which is outperformed by the sequential regression approach in the test-retest reliability metrics. Finally, pipelines are recommended that perform well based on quantitative and qualitative comparisons across multiple datasets and robust metrics. These new insights and recommendations help address the need for effective motion artifact correction to reduce noise and confounds in rs-fMRI.

Adverse Radiation Therapy Effects in the Treatment of Head and Neck Tumors.

Whether used as a single modality or as part of a combined approach, radiation therapy (RT) plays an essential role in the treatment of several head and neck malignancies. Despite the improvement in radiation delivery techniques, normal structures in the vicinity of the target area remain susceptible to a wide range of adverse effects. Given their high incidence, some of these effects are referred to as expected postradiation changes (eg, mucositis, sialadenitis, and edema), while others are considered true complications, meaning they should not be expected and can even represent life-threatening conditions (eg, radionecrosis, fistulas, and radiation-induced neoplasms). Also, according to their timing of onset, these deleterious effects can be divided into four groups: acute (during RT), subacute (within weeks to months), delayed onset (within months to years), and very delayed onset (after several years).The authors provide a comprehensive review of the most important radiation-induced changes related to distinct head and neck sites, focusing on their typical cross-sectional imaging features and correlating them with the time elapsed after treatment. Radiologists should not only be familiar with these imaging findings but also actively seek essential clinical data at the time of interpretation (including knowledge of the RT dose and time, target site, and manifesting symptoms) to better recognize imaging findings, avoid pitfalls and help guide appropriate management. © RSNA, 2022.

Non-contrast hemodynamic imaging of Moyamoya disease with MR fingerprinting ASL: A feasibility study.

PURPOSE: MR Fingerprinting (MRF) Arterial Spin Labeling (ASL) is a non-contrast technique to estimate multiple brain hemodynamic and structural parameters in a single scan. The purpose of this study is to examine the feasibility and initial utility of MRF-ASL in Moyamoya disease. METHODS: MRF-ASL, conventional single-delay ASL, Time-of-flight (TOF) MR angiography, and contrast-based dynamic-susceptibility-contrast (DSC) MRI were prospectively collected from a group of Moyamoya patients in North America (N = 21, 4 men and 17 women). Sixteen healthy subjects (7 men and 9 women) also underwent an MRF-ASL scan. Cerebral blood flow (CBF), bolus arrival time (BAT), and tissue T1 were compared between Moyamoya patients and healthy controls. Perfusion parameters from MRF-ASL were compared to those from other MRI sequences. Multi-linear regression was used for comparisons of parameter values between Moyamoya and control groups. Linear mixed-effects models was used when comparing MRF-ASL to PCASL and DSC parameters. Spearman's Rank Correlation Coefficient was calculated when comparing MRF-ASL to and MRA grades. A P value of 0.05 or less was considered significant. RESULTS: BAT in stenotic internal carotid artery (ICA) territories was prolonged (P < 0.001) in Moyamoya patients, when compared with healthy controls. CBF in stenotic ICA territories of Moyamoya patients was not different from CBF in healthy controls; but in the PCA territories, CBF in Moyamoya patients was higher (P < 0.01) than controls. Quantitative T1 values in the stenotic ICA territories was longer (P < 0.05) than that in controls. Hemodynamic parameters estimated from MRF-ASL were significantly correlated with single-delay ASL and DSC. Longer BAT was associated with more severe intracranial artery stenosis in ICA. CONCLUSIONS: MRF-ASL is a promising technique to assess perfusion and structural abnormalities in Moyamoya patients.

Brain tumor IDH, 1p/19q, and MGMT molecular classification using MRI-based deep learning: an initial study on the effect of motion and motion correction.

Purpose: Deep learning has shown promise for predicting the molecular profiles of gliomas using MR images. Prior to clinical implementation, ensuring robustness to real-world problems, such as patient motion, is crucial. The purpose of this study is to perform a preliminary evaluation on the effects of simulated motion artifact on glioma marker classifier performance and determine if motion correction can restore classification accuracies. Approach: T2w images and molecular information were retrieved from the TCIA and TCGA databases. Simulated motion was added in the k-space domain along the phase encoding direction. Classifier performance for IDH mutation, 1p/19q co-deletion, and MGMT methylation was assessed over the range of 0% to 100% corrupted k-space lines. Rudimentary motion correction networks were trained on the motion-corrupted images. The performance of the three glioma marker classifiers was then evaluated on the motion-corrected images. Results: Glioma marker classifier performance decreased markedly with increasing motion corruption. Applying motion correction effectively restored classification accuracy for even the most motion-corrupted images. For isocitrate dehydrogenase (IDH) classification, 99% accuracy was achieved, exceeding the original performance of the network and representing a new benchmark in non-invasive MRI-based IDH classification. Conclusions: Robust motion correction can facilitate highly accurate deep learning MRI-based molecular marker classification, rivaling invasive tissue-based characterization methods. Motion correction may be able to increase classification accuracy even in the absence of a visible artifact, representing a new strategy for boosting classifier performance.

Dynamic 13 C MR spectroscopy as an alternative to imaging for assessing cerebral metabolism using hyperpolarized pyruvate in humans.

PURPOSE: This study is to investigate time-resolved 13 C MR spectroscopy (MRS) as an alternative to imaging for assessing pyruvate metabolism using hyperpolarized (HP) [1-13 C]pyruvate in the human brain. METHODS: Time-resolved 13 C spectra were acquired from four axial brain slices of healthy human participants (n = 4) after a bolus injection of HP [1-13 C]pyruvate. 13 C MRS with low flip-angle excitations and a multichannel 13 C/1 H dual-frequency radiofrequency (RF) coil were exploited for reliable and unperturbed assessment of HP pyruvate metabolism. Slice-wise areas under the curve (AUCs) of 13 C-metabolites were measured and kinetic analysis was performed to estimate the production rates of lactate and HCO3- . Linear regression analysis between brain volumes and HP signals was performed. Region-focused pyruvate metabolism was estimated using coil-wise 13 C reconstruction. Reproducibility of HP pyruvate exams was presented by performing two consecutive injections with a 45-minutes interval. RESULTS: [1-13 C]Lactate relative to the total 13 C signal (tC) was 0.21-0.24 in all slices. [13 C] HCO3- /tC was 0.065-0.091. Apparent conversion rate constants from pyruvate to lactate and HCO3- were calculated as 0.014-0.018 s-1 and 0.0043-0.0056 s-1 , respectively. Pyruvate/tC and lactate/tC were in moderate linear relationships with fractional gray matter volume within each slice. White matter presented poor linear regression fit with HP signals, and moderate correlations of the fractional cerebrospinal fluid volume with pyruvate/tC and lactate/tC were measured. Measured HP signals were comparable between two consecutive exams with HP [1-13 C]pyruvate. CONCLUSIONS: Dynamic MRS in combination with multichannel RF coils is an affordable and reliable alternative to imaging methods in investigating cerebral metabolism using HP [1-13 C]pyruvate.