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

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

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

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

Generative Adversarial Network-Enhanced Ultra-Low-Dose [18F]-PI-2620 τ PET/MRI in Aging and Neurodegenerative Populations

BACKGROUND AND PURPOSE

With the utility of hybrid τ PET/MR imaging in the screening, diagnosis, and follow-up of individuals with neurodegenerative diseases, we investigated whether deep learning techniques can be used in enhancing ultra-low-dose [18F]-PI-2620 τ PET/MR images to produce diagnostic-quality images.

MATERIALS AND METHODS

Forty-four healthy aging participants and patients with neurodegenerative diseases were recruited for this study, and [18F]-PI-2620 τ PET/MR data were simultaneously acquired. A generative adversarial network was trained to enhance ultra-low-dose τ images, which were reconstructed from a random sampling of 1/20 (approximately 5% of original count level) of the original full-dose data. MR images were also used as additional input channels. Region-based analyses as well as a reader study were conducted to assess the image quality of the enhanced images compared with their full-dose counterparts.

RESULTS

The enhanced ultra-low-dose τ images showed apparent noise reduction compared with the ultra-low-dose images. The regional standard uptake value ratios showed that while, in general, there is an underestimation for both image types, especially in regions with higher uptake, when focusing on the healthy-but-amyloid-positive population (with relatively lower τ uptake), this bias was reduced in the enhanced ultra-low-dose images. The radiotracer uptake patterns in the enhanced images were read accurately compared with their full-dose counterparts.

CONCLUSIONS

The clinical readings of deep learning-enhanced ultra-low-dose τ PET images were consistent with those performed with full-dose imaging, suggesting the possibility of reducing the dose and enabling more frequent examinations for dementia monitoring.

Deep Learning-Generated Synthetic MR Imaging STIR Spine Images Are Superior in Image Quality and Diagnostically Equivalent to Conventional STIR: A Multicenter, Multireader Trial

BACKGROUND AND PURPOSE

Deep learning image reconstruction allows faster MR imaging acquisitions while matching or exceeding the standard of care and can create synthetic images from existing data sets. This multicenter, multireader spine study evaluated the performance of synthetically created STIR compared with acquired STIR.

MATERIALS AND METHODS

From a multicenter, multiscanner data base of 328 clinical cases, a nonreader neuroradiologist randomly selected 110 spine MR imaging studies in 93 patients (sagittal T1, T2, and STIR) and classified them into 5 categories of disease and healthy. A DICOM-based deep learning application generated a synthetically created STIR series from the sagittal T1 and T2 images. Five radiologists (3 neuroradiologists, 1 musculoskeletal radiologist, and 1 general radiologist) rated the STIR quality and classified disease pathology (study 1, n = 80). They then assessed the presence or absence of findings typically evaluated with STIR in patients with trauma (study 2, n = 30). The readers evaluated studies with either acquired STIR or synthetically created STIR in a blinded and randomized fashion with a 1-month washout period. The interchangeability of acquired STIR and synthetically created STIR was assessed using a noninferiority threshold of 10%.

RESULTS

For classification, there was a decrease in interreader agreement expected by randomly introducing synthetically created STIR of 3.23%. For trauma, there was an overall increase in interreader agreement by +1.9%. The lower bound of confidence for both exceeded the noninferiority threshold, indicating interchangeability of synthetically created STIR with acquired STIR. Both the Wilcoxon signed-rank and t tests showed higher image-quality scores for synthetically created STIR over acquired STIR (P < .0001).

CONCLUSIONS

Synthetically created STIR spine MR images were diagnostically interchangeable with acquired STIR, while providing significantly higher image quality, suggesting routine clinical practice potential.

Critical Appraisal of Artificial Intelligence-Enabled Imaging Tools Using the Levels of Evidence System

Clinical adoption of an artificial intelligence-enabled imaging tool requires critical appraisal of its life cycle from development to implementation by using a systematic, standardized, and objective approach that can verify both its technical and clinical efficacy. Toward this concerted effort, the ASFNR/ASNR Artificial Intelligence Workshop Technology Working Group is proposing a hierarchal evaluation system based on the quality, type, and amount of scientific evidence that the artificial intelligence-enabled tool can demonstrate for each component of its life cycle. The current proposal is modeled after the levels of evidence in medicine, with the uppermost level of the hierarchy showing the strongest evidence for potential impact on patient care and health care outcomes. The intended goal of establishing an evidence-based evaluation system is to encourage transparency, foster an understanding of the creation of artificial intelligence tools and the artificial intelligence decision-making process, and to report the relevant data on the efficacy of artificial intelligence tools that are developed. The proposed system is an essential step in working toward a more formalized, clinically validated, and regulated framework for the safe and effective deployment of artificial intelligence imaging applications that will be used in clinical practice.

Unsupervised Deep Learning for Stroke Lesion Segmentation on Follow-up CT Based on Generative Adversarial Networks

BACKGROUND AND PURPOSE

Supervised deep learning is the state-of-the-art method for stroke lesion segmentation on NCCT. Supervised methods require manual lesion annotations for model development, while unsupervised deep learning methods such as generative adversarial networks do not. The aim of this study was to develop and evaluate a generative adversarial network to segment infarct and hemorrhagic stroke lesions on follow-up NCCT scans.

MATERIALS AND METHODS

Training data consisted of 820 patients with baseline and follow-up NCCT from 3 Dutch acute ischemic stroke trials. A generative adversarial network was optimized to transform a follow-up scan with a lesion to a generated baseline scan without a lesion by generating a difference map that was subtracted from the follow-up scan. The generated difference map was used to automatically extract lesion segmentations. Segmentation of primary hemorrhagic lesions, hemorrhagic transformation of ischemic stroke, and 24-hour and 1-week follow-up infarct lesions were evaluated relative to expert annotations with the Dice similarity coefficient, Bland-Altman analysis, and intraclass correlation coefficient.

RESULTS

The median Dice similarity coefficient was 0.31 (interquartile range, 0.08-0.59) and 0.59 (interquartile range, 0.29-0.74) for the 24-hour and 1-week infarct lesions, respectively. A much lower Dice similarity coefficient was measured for hemorrhagic transformation (median, 0.02; interquartile range, 0-0.14) and primary hemorrhage lesions (median, 0.08; interquartile range, 0.01-0.35). Predicted lesion volume and the intraclass correlation coefficient were good for the 24-hour (bias, 3 mL; limits of agreement, -64-59 mL; intraclass correlation coefficient, 0.83; 95% CI, 0.78-0.88) and excellent for the 1-week (bias, -4 m; limits of agreement,-66-58 mL; intraclass correlation coefficient, 0.90; 95% CI, 0.83-0.93) follow-up infarct lesions.

CONCLUSIONS

An unsupervised generative adversarial network can be used to obtain automated infarct lesion segmentations with a moderate Dice similarity coefficient and good volumetric correspondence.

Investigating Simultaneity for Deep Learning-Enhanced Actual Ultra-Low-Dose Amyloid PET/MR Imaging

BACKGROUND AND PURPOSE

Diagnostic-quality amyloid PET images can be created with deep learning using actual ultra-low-dose PET images and simultaneous structural MR imaging. Here, we investigated whether simultaneity is required; if not, MR imaging-assisted ultra-low-dose PET imaging could be performed with separate PET/CT and MR imaging acquisitions.

MATERIALS AND METHODS

We recruited 48 participants: Thirty-two (20 women; mean, 67.7 [SD, 7.9] years) were used for pretraining; 328 (SD, 32) MBq of [¹⁸F] florbetaben was injected. Sixteen participants (6 women; mean, 71.4 [SD. 8.7] years of age) were scanned in 2 sessions, with 6.5 (SD, 3.8) and 300 (SD, 14) MBq of [¹⁸F] florbetaben injected, respectively. Structural MR imaging was acquired simultaneously with PET (90-110 minutes postinjection) on integrated PET/MR imaging in 2 sessions. Multiple U-Net-based deep networks were trained to create diagnostic PET images. For each method, training was done with the ultra-low-dose PET as input combined with MR imaging from either the ultra-low-dose session (simultaneous) or from the standard-dose PET session (nonsimultaneous). Image quality of the enhanced and ultra-low-dose PET images was evaluated using quantitative signal-processing methods, standardized uptake value ratio correlation, and clinical reads.

RESULTS

Qualitatively, the enhanced images resembled the standard-dose image for both simultaneous and nonsimultaneous conditions. Three quantitative metrics showed significant improvement for all networks and no differences due to simultaneity. Standardized uptake value ratio correlation was high across different image types and network training methods, and 31/32 enhanced image pairs were read similarly.

CONCLUSIONS

This work suggests that accurate amyloid PET images can be generated using enhanced ultra-low-dose PET and either nonsimultaneous or simultaneous MR imaging, broadening the utility of ultra-low-dose amyloid PET imaging.

Deep Learning Enables 60% Accelerated Volumetric Brain MRI While Preserving Quantitative Performance: A Prospective, Multicenter, Multireader Trial

BACKGROUND AND PURPOSE

In this prospective, multicenter, multireader study, we evaluated the impact on both image quality and quantitative image-analysis consistency of 60% accelerated volumetric MR imaging sequences processed with a commercially available, vendor-agnostic, DICOM-based, deep learning tool (SubtleMR) compared with that of standard of care.

MATERIALS AND METHODS

Forty subjects underwent brain MR imaging examinations on 6 scanners from 5 institutions. Standard of care and accelerated datasets were acquired for each subject, and the accelerated scans were enhanced with deep learning processing. Standard of care, accelerated scans, and accelerated-deep learning were subjected to NeuroQuant quantitative analysis and classified by a neuroradiologist into clinical disease categories. Concordance of standard of care and accelerated-deep learning biomarker measurements were assessed. Randomized, side-by-side, multiplanar datasets (360 series) were presented blinded to 2 neuroradiologists and rated for apparent SNR, image sharpness, artifacts, anatomic/lesion conspicuity, image contrast, and gray-white differentiation to evaluate image quality.

RESULTS

Accelerated-deep learning was statistically superior to standard of care for perceived quality across imaging features despite a 60% sequence scan-time reduction. Both accelerated-deep learning and standard of care were superior to accelerated scans for all features. There was no difference in quantitative volumetric biomarkers or clinical classification for standard of care and accelerated-deep learning datasets.

CONCLUSIONS

Deep learning reconstruction allows 60% sequence scan-time reduction while maintaining high volumetric quantification accuracy, consistent clinical classification, and what radiologists perceive as superior image quality compared with standard of care. This trial supports the reliability, efficiency, and utility of deep learning-based enhancement for quantitative imaging. Shorter scan times may heighten the use of volumetric quantitative MR imaging in routine clinical settings.

Tissue at Risk and Ischemic Core Estimation Using Deep Learning in Acute Stroke

BACKGROUND AND PURPOSE

In acute stroke patients with large vessel occlusions, it would be helpful to be able to predict the difference in the size and location of the final infarct based on the outcome of reperfusion therapy. Our aim was to demonstrate the value of deep learning-based tissue at risk and ischemic core estimation. We trained deep learning models using a baseline MR image in 3 multicenter trials.

MATERIALS AND METHODS

Patients with acute ischemic stroke from 3 multicenter trials were identified and grouped into minimal (≤20%), partial (20%-80%), and major (≥80%) reperfusion status based on 4- to 24-hour follow-up MR imaging if available or into unknown status if not. Attention-gated convolutional neural networks were trained with admission imaging as input and the final infarct as ground truth. We explored 3 approaches: 1) separate: train 2 independent models with patients with minimal and major reperfusion; 2) pretraining: develop a single model using patients with partial and unknown reperfusion, then fine-tune it to create 2 separate models for minimal and major reperfusion; and 3) thresholding: use the current clinical method relying on apparent diffusion coefficient and time-to-maximum of the residue function maps. Models were evaluated using area under the curve, the Dice score coefficient, and lesion volume difference.

RESULTS

Two hundred thirty-seven patients were included (minimal, major, partial, and unknown reperfusion: n = 52, 80, 57, and 48, respectively). The pretraining approach achieved the highest median Dice score coefficient (tissue at risk = 0.60, interquartile range, 0.43-0.70; core = 0.57, interquartile range, 0.30-0.69). This was higher than the separate approach (tissue at risk = 0.55; interquartile range, 0.41-0.69; P = .01; core = 0.49; interquartile range, 0.35-0.66; P = .04) or thresholding (tissue at risk = 0.56; interquartile range, 0.42-0.65; P = .008; core = 0.46; interquartile range, 0.16-0.54; P < .001).

CONCLUSIONS

Deep learning models with fine-tuning lead to better performance for predicting tissue at risk and ischemic core, outperforming conventional thresholding methods.

Prediction of Clinical Outcome in Patients with Large-Vessel Acute Ischemic Stroke: Performance of Machine Learning versus SPAN-100

BACKGROUND AND PURPOSE

Traditional statistical models and pretreatment scoring systems have been used to predict the outcome for acute ischemic stroke patients (AIS). Our aim was to select the most relevant features in terms of outcome prediction on the basis of machine learning algorithms for patients with acute ischemic stroke and to compare the performance between multiple models and the Stroke Prognostication Using Age and National Institutes of Health Stroke Scale (SPAN-100) index model.

MATERIALS AND METHODS

A retrospective multicenter cohort of 1431 patients with acute ischemic stroke was subdivided into recanalized and nonrecanalized patients. Extreme Gradient Boosting machine learning models were built to predict the mRS score at 90 days using clinical, imaging, combined, and best-performing features. Feature selection was performed using the relative weight and frequency of occurrence in the models. The model with the best performance was compared with the SPAN-100 index model using area under the receiver operating curve analysis.

RESULTS

In 3 groups of patients, the baseline NIHSS was the most significant predictor of outcome among all the parameters, with relative weights of 0.36∼0.69; ischemic core volume on CTP ranked as the most important imaging biomarker with relative weights of 0.29∼0.47. The model with the best-performing features had a better performance than the other machine learning models. The area under the curve of the model with the best-performing features was higher than SPAN-100 model and reached statistical significance for the total (P < .05) and the nonrecanalized patients (P < .001).

CONCLUSIONS

Machine learning-based feature selection can identify parameters with higher performance in outcome prediction. Machine learning models with the best-performing features, especially advanced CTP data, had superior performance of the recovery outcome prediction for patients with stroke at admission in comparison with SPAN-100.

Artificial Intelligence in Neuroradiology: Current Status and Future Directions

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

Application of Deep Learning to Predict Standardized Uptake Value Ratio and Amyloid Status on 18F-Florbetapir PET Using ADNI Data

BACKGROUND AND PURPOSE

Cortical amyloid quantification on PET by using the standardized uptake value ratio is valuable for research studies and clinical trials in Alzheimer disease. However, it is resource intensive, requiring co-registered MR imaging data and specialized segmentation software. We investigated the use of deep learning to automatically quantify standardized uptake value ratio and used this for classification.

MATERIALS AND METHODS

Using the Alzheimer's Disease Neuroimaging Initiative dataset, we identified 2582 18F-florbetapir PET scans, which were separated into positive and negative cases by using a standardized uptake value ratio threshold of 1.1. We trained convolutional neural networks (ResNet-50 and ResNet-152) to predict standardized uptake value ratio and classify amyloid status. We assessed performance based on network depth, number of PET input slices, and use of ImageNet pretraining. We also assessed human performance with 3 readers in a subset of 100 randomly selected cases.

RESULTS

We have found that 48% of cases were amyloid positive. The best performance was seen for ResNet-50 by using regression before classification, 3 input PET slices, and pretraining, with a standardized uptake value ratio root-mean-square error of 0.054, corresponding to 95.1% correct amyloid status prediction. Using more than 3 slices did not improve performance, but ImageNet initialization did. The best trained network was more accurate than humans (96% versus a mean of 88%, respectively).

CONCLUSIONS

Deep learning algorithms can estimate standardized uptake value ratio and use this to classify 18F-florbetapir PET scans. Such methods have promise to automate this laborious calculation, enabling quantitative measurements rapidly and in settings without extensive image processing manpower and expertise.

Deep Learning in Neuroradiology

Deep learning is a form of machine learning using a convolutional neural network architecture that shows tremendous promise for imaging applications. It is increasingly being adapted from its original demonstration in computer vision applications to medical imaging. Because of the high volume and wealth of multimodal imaging information acquired in typical studies, neuroradiology is poised to be an early adopter of deep learning. Compelling deep learning research applications have been demonstrated, and their use is likely to grow rapidly. This review article describes the reasons, outlines the basic methods used to train and test deep learning models, and presents a brief overview of current and potential clinical applications with an emphasis on how they are likely to change future neuroradiology practice. Facility with these methods among neuroimaging researchers and clinicians will be important to channel and harness the vast potential of this new method.

Evaluation of Thick-Slab Overlapping MIP Images of Contrast-Enhanced 3D T1-Weighted CUBE for Detection of Intracranial Metastases: A Pilot Study for Comparison of Lesion Detection, Interpretation Time, and Sensitivity with Nonoverlapping CUBE MIP, CUBE, and Inversion-Recovery-Prepared Fast-Spoiled Gradient Recalled Brain Volume

BACKGROUND AND PURPOSE

Early and accurate identification of cerebral metastases is important for prognostication and treatment planning although this process is often time consuming and labor intensive, especially with the hundreds of images associated with 3D volumetric imaging. This study aimed to evaluate the benefits of thick-slab overlapping MIPs constructed from contrast-enhanced T1-weighted CUBE (overlapping CUBE MIP) for the detection of brain metastases in comparison with traditional CUBE and inversion-recovery prepared fast-spoiled gradient recalled brain volume (IR-FSPGR-BRAVO) and nonoverlapping CUBE MIP.

MATERIALS AND METHODS

A retrospective review of 48 patients with cerebral metastases was performed at our institution from June 2016 to October 2017. Brain MRIs, which were acquired on multiple 3T scanners, included gadolinium-enhanced T1-weighted IR-FSPGR-BRAVO and CUBE, with subsequent generation of nonoverlapping CUBE MIP and overlapping CUBE MIP. Two blinded radiologists identified the total number and location of metastases on each image type. The Cohen κ was used to determine interrater agreement. Sensitivity, interpretation time, and lesion contrast-to-noise ratio were assessed.

RESULTS

Interrater agreement for identification of metastases was fair-to-moderate for all image types (κ = 0.222-0.598). The total number of metastases identified was not significantly different across the image types. Interpretation time for CUBE MIPs was significantly shorter than for CUBE and IR-FSPGR-BRAVO, saving at least 50 seconds per case on average (P < .001). The mean lesion contrast-to-noise ratio for both CUBE MIPs was higher than for IR-FSPGR-BRAVO. The mean contrast-to-noise ratio for small lesions (<4 mm) was lower for nonoverlapping CUBE MIP (1.55) than for overlapping CUBE MIP (2.35). For both readers, the sensitivity for lesion detection was high for all image types but highest for overlapping CUBE MIP and CUBE (0.93-0.97).

CONCLUSIONS

This study suggests that the use of overlapping CUBE MIP or nonoverlapping CUBE MIP for the detection of brain metastases can reduce interpretation time without sacrificing sensitivity, though the contrast-to-noise ratio of lesions is highest for overlapping CUBE MIP.

Resting-State Functional MRI: Everything That Nonexperts Have Always Wanted to Know

Resting-state fMRI was first described by Biswal et al in 1995 and has since then been widely used in both healthy subjects and patients with various neurologic, neurosurgical, and psychiatric disorders. As opposed to paradigm- or task-based functional MR imaging, resting-state fMRI does not require subjects to perform any specific task. The low-frequency oscillations of the resting-state fMRI signal have been shown to relate to the spontaneous neural activity. There are many ways to analyze resting-state fMRI data. In this review article, we will briefly describe a few of these and highlight the advantages and limitations of each. This description is to facilitate the adoption and use of resting-state fMRI in the clinical setting, helping neuroradiologists become familiar with these techniques and applying them for the care of patients with neurologic and psychiatric diseases.

Arterial Spin-Labeling Improves Detection of Intracranial Dural Arteriovenous Fistulas with MRI

BACKGROUND AND PURPOSE

Intracranial dural arteriovenous fistulas carry a risk of substantial neurologic complications but can be difficult to detect on structural MR imaging and TOF-MRA. The purpose of this study was to assess the accuracy and added value of 3D pseudocontinuous arterial spin-labeling MR imaging for the detection of these lesions.

MATERIALS AND METHODS

This retrospective study included 39 patients with a dural arteriovenous fistula and 117 controls who had undergone both DSA and MR imaging with pseudocontinuous arterial spin-labeling. Two neuroradiologists blinded to the DSA results independently assessed MR imaging with and without pseudocontinuous arterial spin-labeling. They recorded specific signs, including venous arterial spin-labeling signal, and the likelihood of a dural arteriovenous fistula using a 5-point Likert scale. Logistic regression and receiver operating characteristic analyses were performed to determine the accuracy of specific signs and the added value of pseudocontinuous arterial spin-labeling. Interobserver agreement was determined by using κ statistics.

RESULTS

Identification of the venous arterial spin-labeling signal had a high sensitivity (94%) and specificity (88%) for the presence a dural arteriovenous fistula. Receiver operating characteristic analysis showed significant improvement in diagnostic performance with the addition of pseudocontinuous arterial spin-labeling in comparison with structural MR imaging (Δarea under the receiver operating characteristic curve = 0.179) and a trend toward significant improvement in comparison with structural MR imaging with time-of-flight MRA (Δarea under the receiver operating characteristic curve = 0.043). Interobserver agreement for the presence of a dural arteriovenous fistula improved substantially and was almost perfect with the addition of pseudocontinuous arterial spin-labeling (κ = 0.92).

CONCLUSIONS

Venous arterial spin-labeling signal has high sensitivity and specificity for the presence of a dural arteriovenous fistula, and the addition of pseudocontinuous arterial spin-labeling increases confidence in the diagnosis of this entity on MR imaging.

MR Vascular Fingerprinting in Stroke and Brain Tumors Models

In this study, we evaluated an MRI fingerprinting approach (MRvF) designed to provide high-resolution parametric maps of the microvascular architecture (i.e., blood volume fraction, vessel diameter) and function (blood oxygenation) simultaneously. The method was tested in rats (n = 115), divided in 3 models: brain tumors (9 L, C6, F98), permanent stroke, and a control group of healthy animals. We showed that fingerprinting can robustly distinguish between healthy and pathological brain tissues with different behaviors in tumor and stroke models. In particular, fingerprinting revealed that C6 and F98 glioma models have similar signatures while 9 L present a distinct evolution. We also showed that it is possible to improve the results of MRvF and obtain supplemental information by changing the numerical representation of the vascular network. Finally, good agreement was found between MRvF and conventional MR approaches in healthy tissues and in the C6, F98, and permanent stroke models. For the 9 L glioma model, fingerprinting showed blood oxygenation measurements that contradict results obtained with a quantitative BOLD approach. In conclusion, MR vascular fingerprinting seems to be an efficient technique to study microvascular properties in vivo. Multiple technical improvements are feasible and might improve diagnosis and management of brain diseases.

Non-Relative Value Unit-Generating Activities Represent One-Fifth of Academic Neuroradiologist Productivity

BACKGROUND AND PURPOSE

A neuroradiologist's activity includes many tasks beyond interpreting relative value unit-generating imaging studies. Our aim was to test a simple method to record and quantify the non-relative value unit-generating clinical activity represented by consults and clinical conferences, including tumor boards.

MATERIALS AND METHODS

Four full-time neuroradiologists, working an average of 50% clinical and 50% academic activity, systematically recorded all the non-relative value unit-generating consults and conferences in which they were involved during 3 months by using a simple, Web-based, computer-based application accessible from smartphones, tablets, or computers. The number and type of imaging studies they interpreted during the same period and the associated relative value units were extracted from our billing system.

RESULTS

During 3 months, the 4 neuroradiologists working an average of 50% clinical activity interpreted 4241 relative value unit-generating imaging studies, representing 8152 work relative value units. During the same period, they recorded 792 non-relative value unit-generating study reviews as part of consults and conferences (not including reading room consults), representing 19% of the interpreted relative value unit-generating imaging studies.

CONCLUSIONS

We propose a simple Web-based smartphone app to record and quantify non-relative value unit-generating activities including consults, clinical conferences, and tumor boards. The quantification of non-relative value unit-generating activities is paramount in this time of a paradigm shift from volume to value. It also represents an important tool for determining staffing levels, which cannot be performed on the basis of relative value unit only, considering the importance of time spent by radiologists on non-relative value unit-generating activities. It may also influence payment models from medical centers to radiology departments or practices.

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

BACKGROUND AND PURPOSE

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

MATERIALS AND METHODS

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

RESULTS

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

CONCLUSIONS

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

MR vascular fingerprinting: A new approach to compute cerebral blood volume, mean vessel radius, and oxygenation maps in the human brain

In the present study, we describe a fingerprinting approach to analyze the time evolution of the MR signal and retrieve quantitative information about the microvascular network. We used a Gradient Echo Sampling of the Free Induction Decay and Spin Echo (GESFIDE) sequence and defined a fingerprint as the ratio of signals acquired pre- and post-injection of an iron-based contrast agent. We then simulated the same experiment with an advanced numerical tool that takes a virtual voxel containing blood vessels as input, then computes microscopic magnetic fields and water diffusion effects, and eventually derives the expected MR signal evolution. The parameter inputs of the simulations (cerebral blood volume [CBV], mean vessel radius [R], and blood oxygen saturation [SO2]) were varied to obtain a dictionary of all possible signal evolutions. The best fit between the observed fingerprint and the dictionary was then determined by using least square minimization. This approach was evaluated in 5 normal subjects and the results were compared to those obtained by using more conventional MR methods, steady-state contrast imaging for CBV and R and a global measure of oxygenation obtained from the superior sagittal sinus for SO2. The fingerprinting method enabled the creation of high-resolution parametric maps of the microvascular network showing expected contrast and fine details. Numerical values in gray matter (CBV=3.1±0.7%, R=12.6±2.4μm, SO2=59.5±4.7%) are consistent with literature reports and correlated with conventional MR approaches. SO2 values in white matter (53.0±4.0%) were slightly lower than expected. Numerous improvements can easily be made and the method should be useful to study brain pathologies.

Imaging recommendations for acute stroke and transient ischemic attack patients: A joint statement by the American Society of Neuroradiology, the American College of Radiology, and the Society of NeuroInterventional Surgery

SUMMARY

Stroke is a leading cause of death and disability worldwide. Imaging plays a critical role in evaluating patients suspected of acute stroke and transient ischemic attack, especially before initiating treatment. Over the past few decades, major advances have occurred in stroke imaging and treatment, including Food and Drug Administration approval of recanalization therapies for the treatment of acute ischemic stroke. A wide variety of imaging techniques has become available to assess vascular lesions and brain tissue status in acute stroke patients. However, the practical challenge for physicians is to understand the multiple facets of these imaging techniques, including which imaging techniques to implement and how to optimally use them, given available resources at their local institution. Important considerations include constraints of time, cost, access to imaging modalities, preferences of treating physicians, availability of expertise, and availability of endovascular therapy. The choice of which imaging techniques to employ is impacted by both the time urgency for evaluation of patients and the complexity of the literature on acute stroke imaging. Ideally, imaging algorithms should incorporate techniques that provide optimal benefit for improved patient outcomes without delaying treatment.

Imaging brain oxygenation with MRI using blood oxygenation approaches: methods, validation, and clinical applications

SUMMARY

In many pathophysiologic situations, including brain neoplasms, neurodegenerative disease, and chronic and acute ischemia, an imbalance exists between oxygen tissue consumption and delivery. Furthermore, oxygenation changes following a stress challenge, such as with carbogen gas or acetazolamide, can yield information about cerebrovascular reactivity. The unique sensitivity of the BOLD effect to the presence of deoxyhemoglobin has led to its widespread use in the field of cognitive neurosciences. However, the high spatial and temporal resolution afforded by BOLD imaging does not need to be limited to the study of healthy brains. While the complex relationship between the MR imaging signal and tissue oxygenation hinders a direct approach, many different methods have been developed during the past decade to obtain specific oxygenation measurements. These include qBOLD, phase- and susceptibility-based imaging, and intravascular T2-based approaches. The aim of this review is to give an overview of the theoretic basis of these methods as well as their application to measure oxygenation in both healthy subjects and those with disease.

Clinical assessment of standard and generalized autocalibrating partially parallel acquisition diffusion imaging: effects of reduction factor and spatial resolution

BACKGROUND AND PURPOSE

PI improves routine EPI-based DWI by enabling higher spatial resolution and reducing geometric distortion, though it remains unclear which of these is most important. We evaluated the relative contribution of these factors and assessed their ability to increase lesion conspicuity and diagnostic confidence by using a GRAPPA technique.

MATERIALS AND METHODS

Four separate DWI scans were obtained at 1.5T in 48 patients with independent variation of in-plane spatial resolution (1.88 mm(2) versus 1.25 mm(2)) and/or reduction factor (R = 1 versus R = 3). A neuroradiologist with access to clinical history and additional imaging sequences provided a reference standard diagnosis for each case. Three blinded neuroradiologists assessed scans for abnormalities and also evaluated multiple imaging-quality metrics by using a 5-point ordinal scale. Logistic regression was used to determine the impact of each factor on subjective image quality and confidence.

RESULTS

Reference standard diagnoses in the patient cohort were acute ischemic stroke (n = 30), ischemic stroke with hemorrhagic conversion (n = 4), intraparenchymal hemorrhage (n = 9), or no acute lesion (n = 5). While readers preferred both a higher reduction factor and a higher spatial resolution, the largest effect was due to an increased reduction factor (odds ratio, 47 ± 16). Small lesions were more confidently discriminated from artifacts on R = 3 images. The diagnosis changed in 5 of 48 scans, always toward the reference standard reading and exclusively for posterior fossa lesions.

CONCLUSIONS

PI improves DWI primarily by reducing geometric distortion rather than by increasing spatial resolution. This outcome leads to a more accurate and confident diagnosis of small lesions.

Clinical evaluation of reduced field-of-view diffusion-weighted imaging of the cervical and thoracic spine and spinal cord

BACKGROUND AND PURPOSE

DWI has the potential to improve the detection and evaluation of spine and spinal cord pathologies. This study assessed whether a recently described method (rFOV DWI) adds diagnostic value in clinical patients.

MATERIALS AND METHODS

Consecutive patients undergoing clinically indicated cervical and/or thoracic spine imaging received standard anatomic sequences supplemented with sagittal rFOV DWI by using a b-value of 500 s/mm(2). Two neuroradiologists blinded to clinical history evaluated the standard anatomic sequences only for pathology and provided their level of confidence in their diagnosis. These readers then rescored the examinations after reviewing the rFOV DWI study and indicated whether this sequence altered findings or confidence levels.

RESULTS

Two hundred twenty-three patients were included in this study. One hundred eighty patient scans (80.7%) demonstrated at least 1 pathologic finding. Interobserver agreement for identifying pathology (κ = 0.77) and in assessing the added value of the rFOV DWI sequence (κ = 0.77) was high. In pathologic cases, the rFOV DWI sequence added clinical utility in 33% of cases (P < .00001, Fisher exact test). The rFOV DWI sequence was found to be helpful in the evaluation of acute infarction, demyelination, infection, neoplasm, and intradural and epidural collections (P < .001, χ(2) test) and provided a significant increase in clinical confidence in the evaluation of 11 of the 15 pathologic subtypes assessed (P < .05, 1-sided paired Wilcoxon test).

CONCLUSIONS

rFOV diffusion-weighted imaging of the cervical and thoracic spine is feasible in a clinical population and increases clinical confidence in the diagnosis of numerous common spinal pathologies.

Extracranial venous drainage patterns in patients with multiple sclerosis and healthy controls

BACKGROUND AND PURPOSE

CCSVI hypothesizes an association between impaired extracranial venous drainage and MS. Published sonographic criteria for CCSVI are controversial, and no MR imaging data exist to support the CCSVI hypothesis. Our purpose was to evaluate possible differences in the extracranial venous drainage of MS and healthy controls using both TOF and contrast-enhanced TRICKS MRV.

MATERIALS AND METHODS

Healthy subjects (n = 20) and patients with MS (n = 19) underwent axial 2D-TOF neck MRV (to assess flattening) and TRICKS MRV (to assess collaterals) at 3T. Two neuroradiologists blinded to cohort status scored IJV flattening and the severity of non-IJV collaterals by using a 4-point qualitative scale (normal = 0, mild = 1, moderate = 2, severe = 3). κ was used to assess reader agreement. Comparisons between groups were performed by using the Wilcoxon rank sum test. The Spearman rank correlation was used to assess the relationship between IJV flattening and collateral scores and, in patients with MS, EDSS scores.

RESULTS

The 2 groups were matched for age and sex (MS, 45 ± 8 years, 79% female; healthy controls, 47 ± 10 years, 65% female). Reader agreement for IJV flattening and collateral severity was good (κ = 0.74) and moderate (κ = 0.58), respectively. While IJV flattening was seen in both patients with MS and healthy controls, scores for the patients with MS were significantly higher (P = .002). Despite a trend, there was no significant difference in collateral scores between groups (P = .063). There was a significant positive correlation between flattening and collateral scores (ρ = 0.32, P = .005) and EDSS and flattening scores (ρ = 0.45, P = .004) but not between EDSS and collateral scores (ρ = 0.01, P = .97).

CONCLUSIONS

These results indicate that patients with MS have greater IJV flattening and a trend toward more non-IJV collaterals than healthy subjects. The role that this finding plays in the pathogenesis or progression of MS, if any, requires further study.

Identification of venous signal on arterial spin labeling improves diagnosis of dural arteriovenous fistulas and small arteriovenous malformations

BACKGROUND AND PURPOSE

DAVFs and small AVMs are difficult to detect on conventional MR imaging/MRA or CTA examinations and often require DSA for definitive diagnosis. The purpose of this study was to assess the value of venous signal intensity on ASL imaging for making this diagnosis.

MATERIALS AND METHODS

Two neuroradiologists and 1 neurologist reviewed MR imaging studies in 26 patients, 15 of whom had DSA-proved DAVFs or small (<2 cm) AVMs. Pseudocontinuous ASL was performed at 1.5T with background-suppressed 3D-FSE readout. Using a 5-point scale, these readers assessed the likelihood of positive findings on a DSA study before and after reviewing the ASL findings. Agreement on imaging findings, including venous ASL signal intensity, was performed by using κ statistics. Logistic regression and ROC analysis were performed to determine which imaging findings improved diagnosis.

RESULTS

Venous ASL signal intensity was seen frequently in cases with positive findings on DSA. The sensitivity and specificity of venous ASL signal intensity for predicting positive findings on a DSA study were 78% and 85%, respectively. On ROC analysis, there was a significant increase in the AUC after review of the ASL images (AUC = 0.798 pre-ASL, AUC = 0.891 post-ASL; P = .02). Multivariate regression identified venous ASL signal intensity as the strongest predictor of positive findings on a DSA study, with an odds ratio of 17.3 (95% CI, 3.3-90.4).

CONCLUSIONS

Identifying venous ASL signal intensity improved detection of DAVFs and small AVMs. Attention to this finding may improve triage to DSA in patients with suspected small vascular malformations.

Cerebral CT perfusion using an interventional C-arm imaging system: cerebral blood flow measurements

BACKGROUND AND PURPOSE

CTP imaging in the interventional suite could reduce delays to the start of image-guided interventions and help determine the treatment progress and end point. However, C-arms rotate slower than clinical CT scanners, making CTP challenging. We developed a cerebral CTP protocol for C-arm CBCT and evaluated it in an animal study.

MATERIALS AND METHODS

Five anesthetized swine were imaged by using C-arm CBCT and conventional CT. The C-arm rotates in 4.3 seconds plus a 1.25-second turnaround, compared with 0.5 seconds for clinical CT. Each C-arm scan had 6 continuous bidirectional sweeps. Multiple scans each with a different delay to the start of an aortic arch iodinated contrast injection and a novel image reconstruction algorithm were used to increase temporal resolution. Three different scan sets (consisting of 6, 3, or 2 scans) and 3 injection protocols (3-mL/s 100%, 3-mL/s 67%, and 6-mL/s 50% contrast concentration) were studied. CBF maps for each scan set and injection were generated. The concordance and Pearson correlation coefficients (ρ and r) were calculated to determine the injection providing the best match between the following: the left and right hemispheres, and CT and C-arm CBCT.

RESULTS

The highest ρ and r values (both 0.92) for the left and right hemispheres were obtained by using the 6-mL 50% iodinated contrast concentration injection. The same injection gave the best match for CT and C-arm CBCT for the 6-scan set (ρ = 0.77, r = 0.89). Some of the 3-scan and 2-scan protocols provided matches similar to those in CT.

CONCLUSIONS

This study demonstrated that C-arm CBCT can produce CBF maps that correlate well with those from CTP.

Comparison of MR and contrast venography of the cervical venous system in multiple sclerosis

BACKGROUND AND PURPOSE

MRV has been proposed as a possible screening method to identify chronic cerebrospinal venous insufficiency, which may play a role in MS. We report our initial experience comparing MRV and CV in MS patients to evaluate venous stenosis and collateral venous drainage.

MATERIALS AND METHODS

Time-of-flight and time-resolved imaging of contrast kinetics MRV and CV were performed in 39 MS patients. The presence and severity of both IJ vein caliber changes and non-IJ collaterals were graded by using a 4-point scale by 2 radiologists in an independent and blinded manner.

RESULTS

Both studies frequently showed venous abnormalities, most commonly IJ flattening at the C1 level and in the lower neck. There was moderate-to-good agreement between the modalities (κ = 0.55; 95% CI, 0.45%-0.65%). For collaterals, agreement was only fair (κ = 0.30; 95% CI, 0.09%-0.50%). The prevalence of IJ segments graded mild or worse on CV was 54%. If CV was considered a standard, the sensitivity and specificity of MRV was 0.79 (0.71-0.86) and 0.76 (0.67-0.83), respectively. Degree of stenosis was related to the severity of collaterals for CV but not for MRV.

CONCLUSIONS

IJ caliber changes were seen in characteristic locations on both MRV and CV in MS patients. Agreement between modalities was higher for stenosis than for collaterals. If CV is considered a standard, MRV performance is good but may require additional improvement before MRV can be used for screening.

Reduced field-of-view diffusion imaging of the human spinal cord: comparison with conventional single-shot echo-planar imaging

BACKGROUND AND PURPOSE

DWI of the spinal cord is challenging because of its small size and artifacts associated with the most commonly used clinical imaging method, SS-EPI. We evaluated the performance of rFOV spinal cord DWI and compared it with the routine fFOV SS-EPI in a clinical population.

MATERIALS AND METHODS

Thirty-six clinical patients underwent 1.5T MR imaging examination that included rFOV SS-EPI DWI of the cervical spinal cord as well as 2 comparison diffusion sequences: fFOV SS-EPI DWI normalized for either image readout time (low-resolution fFOV) or spatial resolution (high-resolution fFOV). ADC maps were created and compared between the methods by using single-factor analysis of variance. Two neuroradiologists blinded to sequence type rated the 3 DWI methods, based on susceptibility artifacts, perceived spatial resolution, signal intensity-to-noise ratio, anatomic detail, and clinical utility.

RESULTS

ADC values for the rFOV and both fFOV sequences were not statistically different (rFOV: 1.01 ± 0.18 × 10(-3) mm(2)/s; low-resolution fFOV: 1.12 ± 0.22 × 10(-3) mm(2)/s; high-resolution fFOV: 1.10 ± 0.21 × 10(-3) mm(2)/s; F = 2.747, P > .05). The neuroradiologist reviewers rated the rFOV diffusion images superior in terms of all assessed measures (P < 0.0001). Particular improvements were noted in patients with metal hardware, degenerative disease, or both.

CONCLUSIONS

rFOV DWI of the spinal cord overcomes many of the problems associated with conventional fFOV SS-EPI and is feasible in a clinical population. From a clinical standpoint, images were deemed superior to those created by using standard fFOV methods.

Perfusion MRI (Tmax and MTT) correlation with xenon CT cerebral blood flow in stroke patients

BACKGROUND

While stable xenon CT (Xe-CT) cerebral blood flow (CBF) is an accepted standard for quantitative assessment of cerebral hemodynamics, the accuracy of magnetic resonance perfusion-weighted imaging (PWI-MRI) is unclear. The Improved PWI Methodology in Acute Clinical Stroke Study compares PWI findings with Xe-CT CBF values in patients experiencing symptomatic severe cerebral hypoperfusion.

METHODS

We compared mean transit time (MTT) and Tmax PWI-MRI with the corresponding Xe-CT CBF values in 25 coregistered regions of interest (ROIs) of multiple sizes and locations in nine subacute stroke patients. Comparisons were performed with Pearson correlation coefficients (R). We performed receiver operating characteristic (ROC) curve analyses to define the threshold of Tmax and absolute MTT that could best predict a Xe-CT CBF <20 mL/100 g/minute.

RESULTS

The subjects' mean (SD) age was 50 (15) years, the median (interquartile range [IQR]) NIH Stroke Scale score was 2 (2-6), and the median (IQR) time between MRI and Xe-CT was 12 (-7-19) hours. The total number of ROIs was 225, and the median (IQR) ROI size was 550 (360-960) pixels. Tmax correlation with Xe-CT CBF (R = 0.63, p < 0.001) was stronger than absolute MTT (R = 0.55, p < 0.001), p = 0.049. ROC curve analysis found that Tmax >4 seconds had 68% sensitivity, 80% specificity, and 77% accuracy and MTT >10 seconds had 68% sensitivity, 77% specificity, and 75% accuracy for predicting ROIs with Xe-CT CBF <20 mL/100 g/minute.

CONCLUSION

Our results suggest that in subacute ischemic stroke patients, Tmax correlates better than absolute mean transit time (MTT) with xenon CT cerebral blood flow (Xe-CT CBF) and that both Tmax >4 seconds and MTT >10 seconds are strongly associated with Xe-CT CBF <20 mL/100 g/minute. CBF = cerebral blood flow; DBP = diastolic blood pressure; DEFUSE = Diffusion and Perfusion Imaging Evaluation for Understanding Stroke Evolution; DWI = diffusion-weighted imaging; EPITHET = Echoplanar Imaging Thrombolytic Evaluation Trial; FOV = field of view; ICA = internal carotid artery; IQR = interquartile range; MCA = middle cerebral artery; MTT = mean transit time; NIHSS = NIH Stroke Scale; PWI = perfusion-weighted imaging; PWI-MRI = magnetic resonance perfusion-weighted imaging; ROC = receiver operating characteristic; ROI = region of interest; SBP = systolic blood pressure; SVD = singular value decomposition; Xe-CT = xenon CT.

Comparison of multidetector CT angiography and MR imaging of cervical artery dissection

BACKGROUND AND PURPOSE

Conventional angiography has been historically considered the gold standard for the diagnosis of cervical artery dissection, but MR imaging/MR angiography (MRA) and CT/CT angiography (CTA) are commonly used noninvasive alternatives. The goal of this study was to compare the ability of multidetector CT/CTA and MR imaging/MRA to detect common imaging findings of dissection.

MATERIALS AND METHODS

Patients in the data base of our Stroke Center between 2003 and 2007 with dissections who had CT/CTA and MR imaging/MRA on initial work-up were reviewed retrospectively. Two neuroradiologists evaluated the images for associated findings of dissection, including acute ischemic stroke, luminal narrowing, vessel irregularity, wall thickening/hematoma, pseudoaneurysm, and intimal flap. The readers also subjectively rated each vessel on the basis of whether the imaging findings were more clearly displayed with CT/CTA or MR imaging/MRA or were equally apparent.

RESULTS

Eighteen patients with 25 dissected vessels (15 internal carotid arteries [ICA] and 10 vertebral arteries [VA]) met the inclusion criteria. CT/CTA identified more intimal flaps, pseudoaneurysms, and high-grade stenoses than MR imaging/MRA. CT/CTA was preferred for diagnosis in 13 vessels (5 ICA, 8 VA), whereas MR imaging/MRA was preferred in 1 vessel (ICA). The 2 techniques were deemed equal in the remaining 11 vessels (9 ICA, 2 VA). A significant preference for CT/CTA was noted for VA dissections (P < .05), but not for ICA dissections.

CONCLUSION

Multidetector CT/CTA visualized more features of cervical artery dissection than MR imaging/MRA. CT/CTA was subjectively favored for vertebral dissection, whereas there was no technique preference for ICA dissection. In many cases, MR imaging/MRA provided complementary or confirmatory information, particularly given its better depiction of ischemic complications.

Noninvasive imaging of quantitative cerebral blood flow changes during 100% oxygen inhalation using arterial spin-labeling MR imaging

BACKGROUND AND PURPOSE

Tracer studies have demonstrated that 100% oxygen inhalation causes a small cerebral blood flow (CBF) decrease. This study was performed to determine whether arterial spin-labeling (ASL), a noninvasive MR imaging technique, could image these changes with clinically reasonable imaging durations.

MATERIALS AND METHODS

Continuous ASL imaging was performed in 7 healthy subjects before, during, and after 100% oxygen inhalation. ASL difference signal intensity (DeltaM, control - label), CBF, and CBF percentage change were measured. A test-retest paradigm was used to calculate the variability of the initial and final room air CBF measurements.

RESULTS

During oxygen inhalation, DeltaM decreased significantly in all regions (eg, global DeltaM decreased by 23 +/- 11%, P < .01, all values mean +/- SD). Accounting for the reduced T1 of hyperoxygenated blood, we found a smaller CBF decrease, which did not reach significance in any of the regions. Global CBF dropped from 50 +/- 10 mL per 100 g/minute to 47 +/- 10 mL per 100 g/minute following 100% oxygen inhalation, a decrease of 5 +/- 14% (P > .17). The root-mean-square variability of the initial and final room air CBF measurements was 7-8 mL per 100 g/minute.

CONCLUSIONS

The DeltaM signal intensity decreased significantly with oxygen inhalation; however, after accounting for changes in blood T1 with oxygen, CBF decreases were small. Such measurements support the use of hyperoxia as an MR imaging contrast agent and may be helpful to interpret hyperoxia-based stroke trials.

Theoretical basis of hemodynamic MR imaging techniques to measure cerebral blood volume, cerebral blood flow, and permeability

Cerebrovascular hemodynamic assessment adds new information to standard anatomic MR imaging and improves patient care. This article reviews the theoretic underpinnings of several potentially quantitative MR imaging-based methods that shed light on the hemodynamic status of the brain, including cerebral blood flow (CBF), cerebral blood volume (CBV), and contrast agent permeability. Techniques addressed include dynamic susceptibility contrast (which most simply and accurately estimates CBV), arterial spin labeling (a powerful method to measure CBF), and contrast-enhanced methods to derive permeability parameters such as the transport constant Ktrans.

Comparative overview of brain perfusion imaging techniques

Numerous imaging techniques have been developed and applied to evaluate brain hemodynamics. Among these are: Positron Emission Tomography (PET), Single Photon Emission Computed Tomography (SPECT), Xenon-enhanced Computed Tomography (XeCT), Dynamic Perfusion-computed Tomography (PCT), Magnetic Resonance Imaging Dynamic Susceptibility Contrast (DSC), Arterial Spin-Labeling (ASL), and Doppler Ultrasound. These techniques give similar information about brain hemodynamics in the form of parameters such as cerebral blood flow (CBF) or volume (CBV). All of them are used to characterize the same types of pathological conditions. However, each technique has its own advantages and drawbacks. This article addresses the main imaging techniques dedicated to brain hemodynamics. It represents a comparative overview, established by consensus among specialists of the various techniques. For clinicians, this paper should offers a clearer picture of the pros and cons of currently available brain perfusion imaging techniques, and assist them in choosing the proper method in every specific clinical setting.

Is all perfusion-weighted magnetic resonance imaging for stroke equal? The temporal evolution of multiple hemodynamic parameters after focal ischemia in rats correlated with evidence of infarction

Although perfusion-weighted imaging techniques are increasingly used to study stroke, no particular hemodynamic variable has emerged as a standard marker for accumulated ischemic damage. To better characterize the hemodynamic signature of infarction. the authors have assessed the severity and temporal evolution of ischemic hemodynamics in a middle cerebral artery occlusion model in the rat. Cerebral blood flow (CBF) and total and microvascular cerebral blood volume (CBV) changes were measured with arterial spin labeling and steady-state susceptibility contrast magnetic resonance imaging (MRI), respectively, and analyzed in regions corresponding to infarcted and spared ipsilateral tissue, based on 2,3,5-triphenyltetrazolium chloride histology sections after 24 hours ischemia. Spin echo susceptibility contrast was used to measure microvascular-weighted CBV, which had a maximum sensitivity for vessels with radii between 4 and 30 microm. Serial measurements between 1 and 3 hours after occlusion showed no change in CBF (22 +/- 20% of contralateral, mean +/- SD) or in total CBV (78 +/- 13% of contralateral) in regions destined to infarct. However, microvascular CBV progressively declined from 72 +/- 5% to 64 +/- 11% (P < 0.01) during this same period. Microvascular CBV changes with time were entirely due to decreases in subcortical infarcted zones (from 73 +/- 9% to 57 +/- 14%. P < 0.001) without changes in the cortical infarcted territory. The hemodynamic variables showed differences in magnitude and temporal response, and these changes varied based on histologic outcome and brain architecture. Such factors should be considered when designing imaging studies for human stroke.

Cerebrovascular dynamics of autoregulation and hypoperfusion. An MRI study of CBF and changes in total and microvascular cerebral blood volume during hemorrhagic hypotension

BACKGROUND AND PURPOSE

To determine how cerebral blood flow (CBF), total and microvascular cerebral blood volume (CBV), and blood oxygenation level-dependent (BOLD) contrast change during autoregulation and hypotension using hemodynamic MRI.

METHODS

Using arterial spin labeling and steady-state susceptibility contrast, we measured CBF and changes in both total and microvascular CBV during hemorrhagic hypotension in the rat (n=9).

RESULTS

We observed CBF autoregulation for mean arterial blood pressure (MABP) between 50 and 140 mm Hg, at which average CBF was 1.27+/-0.44 mL. g(-1). min(-1) (mean+/-SD). During autoregulation, total and microvascular CBV changes were small and not significantly different from CBF changes. Consistent with this, no significant BOLD changes were observed. For MABP between 10 and 40 mm Hg, total CBV in the striatum increased slightly (+7+/-12%, P<0.05) whereas microvascular CBV decreased (-15+/-17%, P<0.01); on the cortical surface, total CBV increases were larger (+21+/-18%, P<0.01) and microvascular CBV was unchanged (3+/-22%, P>0.05). With severe hypotension, both total and microvascular CBV decreased significantly. Over the entire range of graded global hypoperfusion, there were increases in the CBV/CBF ratio.

CONCLUSIONS

Parenchymal CBV changes are smaller than those of previous reports but are consistent with the small arteriolar fraction of total blood volume. Such measurements allow a framework for understanding effective compensatory vasodilation during autoregulation and volume-flow relationships during hypoperfusion.

Evidence of a cerebrovascular postarteriole windkessel with delayed compliance

A pronounced temporal mismatch was observed between the responses of relative cerebral blood volume (rCBV) measured by magnetic resonance imaging and relative cerebral blood flow measured by laser-Doppler flowmetry in rat somatosensory cortex after electrical forepaw stimulation. The increase of relative cerebral blood flow after stimulus onset and decrease after stimulus cessation were accurately described with a single exponential time constant of 2.4 +/- 0.8 seconds. In contrast, rCBV exhibited two distinct and nearly sequential processes after both onset and cessation of stimulation. A rapid change of rCBV (1.5 +/- 0.8 seconds) occurring immediately after onset and cessation was not statistically different from the time constant for relative cerebral blood flow. However, a slow phase of increase (onset) and decrease (cessation) with an exponential time constant of 14 +/- 13 seconds began approximately 8 seconds after the rapid phase of CBV change. A modified windkessel model was developed to describe the temporal evolution of rCBV as a rapid elastic response of capillaries and veins followed by slow venous relaxation of stress. Venous delayed compliance was suggested as the mechanism for the poststimulus undershoot in blood oxygen-sensitive magnetic resonance imaging signal that has been observed in this animal model and in human data.

Multislice perfusion and perfusion territory imaging in humans with separate label and image coils

An arterial spin labeling technique using separate RF labeling and imaging coils was used to obtain multislice perfusion images of the human brain at 3 T. Continuous RF irradiation at a peak power of 0.3 W was applied to the carotid arteries to adiabatically invert spins. Labeling was achieved without producing magnetization transfer effects since the B1 field of the labeling coil did not extend into the imaging region or couple significant power into the imaging coil. Eliminating magnetization transfer allowed the acquisition of multislice perfusion images of arbitrary orientation. Combining surface coil labeling with a reduced RF duty cycle permitted significantly lower SAR than single coil approaches. The technique was also found to allow selective labeling of blood in either carotid, providing an assessment of the artery's perfusion territory. In normal subjects, these territories were well-defined and localized to the ipsilateral hemisphere.

Delivery of imaging agents into brain

Delivery of diagnostic agents to the central nervous system (CNS) poses several challenges as a result of the special features of CNS blood vessels and tissue fluids. Diffusion barriers exist between blood and neural tissue, in the endothelium of parenchymal vessels (blood-brain barrier, BBB), and in the epithelia of the choroid plexuses and arachnoid membrane (blood-CSF barriers), which severely restrict penetration of several diagnostic imaging agents. The anatomy of large vessels can be imaged using bolus injection of X-ray contrast agents to identify sites of malformation or occlusion, and blood flow measured using MRI and CT, while new techniques permit analysis of capillary perfusion and blood volume. Absolute quantities can be derived, although relative measures in different CNS regions may be as useful in diagnosis. Local blood flow, blood volume, and their ratio (mean transit time) can be measured with high speed tomographic imaging using MRI and CT. Intravascular contrast agents for MRI are based on high magnetic susceptibility agents such as gadolinium, dysprosium and iron. Steady-state imaging using agents that cross the BBB including (123)I- and (99m)Tc-labelled lipophilic agents with SPECT, gives a 'snapshot' of perfusion at the time of injection. Cerebral perfusion can also be measured with PET, using H(2)(15)O, (11)C- or (15)O-butanol, and (18)F-fluoromethane, and cerebral blood volume measured with C(15)O. Recent advances in MRI permit the non-invasive 'labelling' of endogenous water protons in flowing blood, with subsequent detection as a measure of blood flow. Imaging the BBB most commonly involves detecting disruptions of the barrier, allowing contrast agents to leak out of the vascular system. Gd-DTPA is useful in imaging leaky vessels as in some cerebral tumors, while the shortening of T(1) by MR contrast agents can be used to detect more subtle changes in BBB permeability to water as in cerebral ischemia. Techniques for imaging the dynamic activity of the brain parenchyma mainly involve PET, using a variety of radiopharmaceuticals to image glucose transport and metabolism, neurotransmitter binding and uptake, protein synthesis and DNA dynamics. PET methods permit detailed analysis of regional function by comparing resting and task-related images, important in improving understanding of both normal and pathological brain function.

Continuous assessment of perfusion by tagging including volume and water extraction (CAPTIVE): a steady-state contrast agent technique for measuring blood flow, relative blood volume fraction, and the water extraction fraction

A new technique, CAPTIVE, that is a synthesis of arterial spin labeling (ASL) blood flow and steady-state susceptibility contrast relative blood volume imaging is described. Using a single injection of a novel, long half-life intravascular magnetopharmaceutical with a high tissue:blood susceptibility difference (deltachi) to deltaR1 ratio, changes in tissue transverse relaxivity (deltaR2 or deltaR2*) that arise from changes in blood volume were measured, while preserving the ability to measure blood flow using traditional T1-based ASL techniques. This modification permits the continuous measurement of both blood flow and blood volume. Also, because the contrast agent can be used to remove the signal from intravascular spins, it is possible to measure the first-pass water extraction fraction. Contrast-to-noise is easily traded off with repetition rate, allowing the use of non-EPI scanners and more flexible imaging paradigms. The basic theory of these measurements, several experimental scenarios, and validating results are presented. Specifically, the PaCO2-reactivity of microvascular and total relative cerebral blood volume (rCBV), cerebral blood flow (CBF), and the water extraction-flow product (EF) in rats with the new contrast agent MPEG-PL-DyDTPA is measured, and the values are concordant with those of previous literature. As an example of one possible application, continuous flow and volume measurements during transient focal ischemia are presented. It is believed that CAPTIVE imaging will yield a more complete picture of the hemodynamic state of an organ, and has further application for understanding the origins of the BOLD effect.

Mismatch between cerebral blood volume and flow index during transient focal ischemia studied with MRI and GD-BOPTA

We investigated the regional and temporal changes in cerebral blood volume (CBV), cerebral blood flow (CBF), and vascular transit time in seven mongrel cats during 30 min transient focal ischemia, caused by occlusion of the middle cerebral artery. Dynamic susceptibility contrast magnetic resonance imaging was done at 4.7 T, using fast gradient echo T2* weighted imaging and intravenous injection of gadolinium-BOPTA/Dimeglumine. During occlusion, the areas showing a blood volume change were predominantly within the middle cerebral artery territory and could be divided into areas showing either CBV increases or decreases. The area with decreased blood volume also had decreased blood flow as measured by our flow-based index (p < 0.05) and was located in the central territory of the middle cerebral artery. Peripheral to this region was an area showing increased blood volume but without significant CBF changes (p > 0.05). During reperfusion, the CBF increased in the entire zone showing changes in blood volume during occlusion, and remained significantly elevated until 45 min post-occlusion, while CBV remained elevated in the hyperemic rim for at least 2 h. The presence of a peri-ischemic zone showing flow/volume mismatch identified a region wherein baseline CBF is maintained by means of compensatory vasodilatation, but where the ratio of CBF to CBV is decreased. Dynamic susceptibility contrast magnetic resonance imaging with gadolinium-BOPTA/Dimeglumine may be a valuable technique for the investigation of regional and temporal perturbations of hemodynamics during ischemia and reperfusion.

Measurement of changes in cerebral blood volume in spontaneously hypertensive rats following L-arginine infusion using dynamic susceptibility contrast MRI

To understand whether the NO-dependent vasodilator L-arginine was effective upon a chronically hypertensive cerebral capillary endothelium, dynamic susceptibility contrast MRI was used to measure the relative cerebral blood volume (rCBV) changes in nonischemic spontaneously hypertensive rats (SHRs). rCBV was measured in 11 rats at 4.7 T using fast gradient echo imaging with intravenous injection of Gd-DTPA. Images were acquired before, immediately after, and up to 90 min after the infusion of 300 mg/kg L-arginine (n = 7) or of an equivalent volume of saline (n = 4). L-arginine increased rCBV in cortex beginning 10 min after infusion and reached significance after 30 min (P < 0.01), reached a peak of 1.24 +/- 0.06 (mean +/- SEM) times pre-injection level after 50 min, and was sustained throughout the 90 min observation period. In contrast, the rCBV in the deeper gray matter (striatum) showed no statistically significant change over the 90 min observation period. While this is consistent with previous studies showing that L-arginine infusion can directly modulate vascular tone and cerebral hemodynamics, it demonstrates that the effect is present only in cortex, and that it can occur also in the setting of a disturbed capillary endothelium.

Neuronal nitric oxide synthase mutant mice show smaller infarcts and attenuated apparent diffusion coefficient changes in the peri-infarct zone during focal cerebral ischemia

Diffusion-weighted MRI at 2 T was used to monitor and assess tissue damage after permanent middle cerebral artery occlusion (MCAO) in wild-type (WT) and mice deficient in nitric oxide synthase gene expression (nNOS-). The ischemic lesion was evaluated 3 h after occlusion and subdivided into the lesion core and peri-infarct zone based on the magnitude of the apparent diffusion coefficient (ADC) change. Infarct volume, measured by using histochemical staining 24 h after MCA occlusion, correlated best with MRI infarct volume as assessed by an ADC threshold of 25% decrease from baseline at 3 h. For ADC thresholds of greater than 25% decrease, lesion size was not significantly different in nNOS- and WT mice. However, brain tissue showing ADC decreases of 10-25% was significantly smaller in the ipsilateral hemisphere of mutants (27 +/- 2% and 21 +/- 2% in WT and nNOS-, respectively; P < 0.05). These findings occurred independently of infarct volume and are consistent with a smaller peri-infarct zone in nNOS- mice. We postulate that the smaller peri-infarct zone is a reflection of less severe metabolic disturbance after ischemia in nNOS- mice, possibly related to diminished production of nitric oxide (NO) or a related product. We conclude that magnetic resonance techniques previously used to assess ischemic damage in larger animals can be extended to the mouse, raising the possibility that the molecular mechanisms leading to ischemic damage can be examined by using genetically engineered mice.