Accelerated dual-venc 4D flow MRI with variable high-venc spatial resolution for neurovascular applications.

PURPOSE: Dual-velocity encoded (dual-venc or DV) 4D flow MRI achieves wide velocity dynamic range and velocity-to-noise ratio (VNR), enabling accurate neurovascular flow characterization. To reduce scan time, we present interleaved dual-venc 4D Flow with independently prescribed, prospectively undersampled spatial resolution of the high-venc (HV) acquisition: Variable Spatial Resolution Dual Venc (VSRDV). METHODS: A prototype VSRDV sequence was developed based on a Cartesian acquisition with eight-point phase encoding, combining PEAK-GRAPPA acceleration with zero-filling in phase and partition directions for HV. The VSRDV approach was optimized by varying z, the zero-filling fraction of HV relative to low-venc, between 0%-80% in vitro (realistic neurovascular model with pulsatile flow) and in vivo (n = 10 volunteers). Antialiasing precision, mean and peak velocity quantification accuracy, and test-retest reproducibility were assessed relative to reference images with equal-resolution HV and low venc (z = 0%). RESULTS: In vitro results for all z demonstrated an antialiasing true positive rate at least 95% for RPEAK-GRAPPA$$ {R}_{\mathrm{PEAK}-\mathrm{GRAPPA}} $$  = 2 and 5, with no linear relationship to z (p = 0.62 and 0.13, respectively). Bland-Altman analysis for z = 20%, 40%, 60%, or 80% versus z = 0% in vitro and in vivo demonstrated no bias >1% of venc in mean or peak velocity values at any RZF$$ {R}_{\mathrm{ZF}} $$ . In vitro mean and peak velocity, and in vivo peak velocity, had limits of agreement within 15%. CONCLUSION: VSRDV allows up to 34.8% scan time reduction compared to PEAK-GRAPPA accelerated DV 4D Flow MRI, enabling large spatial coverage and dynamic range while maintaining VNR and velocity measurement accuracy.

Intracranial Blood Flow Quantification by Accelerated Dual-venc 4D Flow MRI: Comparison With Transcranial Doppler Ultrasound.

BACKGROUND: Dual-venc 4D flow MRI, recently introduced for the assessment of intracranial hemodynamics, may provide a promising complementary approach to well-established tools such as transcranial Doppler ultrasound (TCD) and overcome some of their disadvantages. However, data comparing intracranial flow measures from dual-venc 4D flow MRI and TCD are lacking. PURPOSE: To compare cerebral blood flow velocity measures derived from dual-venc 4D flow MRI and TCD. STUDY TYPE: Prospective cohort. SUBJECTS: A total of 25 healthy participants (56 ± 4 years old, 44% female). FIELD STRENGTH/SEQUENCE: A 3 T/dual-venc 4D flow MRI using a time-resolved three-dimensional phase-contrast sequence with three-dimensional velocity encoding. ASSESSMENT: Peak velocity measurements in bilateral middle cerebral arteries (MCA) were quantified from dual-venc 4D flow MRI and TCD. The MRI data were quantified by two independent observers (S.M and Y.M.) and TCD was performed by a trained technician (A.L.M.). We assessed the agreement between 4D flow MRI and TCD measures, and the interobserver agreement of 4D flow MRI measurements. STATISTICAL TESTS: Peak velocity from MRI and TCD was compared using Bland-Altman analysis and coefficient of variance. Intraclass correlation coefficient (ICC) was used to assess MRI interobserver agreement. A P value < 0.05 was considered statistically significant. RESULTS: There was excellent interobserver agreement in dual-venc 4D flow MRI-based measurements of peak velocity in bilateral MCA (ICC = 0.97 and 0.96 for the left and right MCA, respectively). Dual-venc 4D flow MRI significantly underestimated peak velocity in the left and right MCA compared to TCD (bias = 0.13 [0.59, -0.33] m/sec and 0.15 [0.47, -0.17] m/sec, respectively). The coefficient of variance between dual-venc 4D flow MRI and TCD measurements was 26% for the left MCA and 22% for the right MCA. DATA CONCLUSION: There was excellent interobserver agreement for the assessment of MCA peak velocity using dual-venc 4D flow MRI, and ≤20% under-estimation compared with TCD. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

Noninvasive quantification of cerebrovascular pressure changes using 4D Flow MRI.

PURPOSE: Hemodynamic alterations are indicative of cerebrovascular disease. However, the narrow and tortuous cerebrovasculature complicates image-based assessment, especially when quantifying relative pressure. Here, we present a systematic evaluation of image-based cerebrovascular relative pressure mapping, investigating the accuracy of the routinely used reduced Bernoulli (RB), the extended unsteady Bernoulli (UB), and the full-field virtual work-energy relative pressure ( ν WERP) method. METHODS: Patient-specific in silico models were used to generate synthetic cerebrovascular 4D Flow MRI, with RB, UB, and ν WERP performance quantified as a function of spatiotemporal sampling and image noise. Cerebrovascular relative pressures were also derived in 4D Flow MRI from healthy volunteers ( n=8 ), acquired at two spatial resolutions (dx = 1.1 and 0.8 mm). RESULTS: The in silico analysis indicate that accurate relative pressure estimations are inherently coupled to spatial sampling: at dx = 1.0 mm high errors are reported for all methods; at dx = 0.5 mm ν WERP recovers relative pressures at a mean error of 0.02 ± 0.25 mm Hg, while errors remain higher for RB and UB (mean error of -2.18 ± 1.91 and -2.18 ± 1.87 mm Hg, respectively). The dependence on spatial sampling is also indicated in vivo, albeit with higher correlative dependence between resolutions using ν WERP (k = 0.64, R2 = 0.81 for dx = 1.1 vs. 0.8 mm) than with RB or UB (k = 0.04, R2 = 0.03, and k = 0.07, R2 = 0.07, respectively). CONCLUSION: Image-based full-field methods such as ν WERP enable cerebrovascular relative pressure mapping; however, accuracy is directly dependent on utilized spatial resolution.

Bilateral Multiband 4D Flow MRI of the Carotid Arteries at 7T.

PURPOSE: Simultaneous multislab (SMSb) 4D flow MRI was developed and implemented at 7T for accelerated acquisition of the 3D blood velocity vector field in both carotid bifurcations. METHODS: SMSb was applied to 4D flow to acquire blood velocities in both carotid bifurcations in sagittal orientation using a local transmit/receive coil at 7T. B1+ transmit efficiency was optimized by B1+ shimming. SMSb 4D flow was obtained in 8 healthy subjects in single-band (SB) and multiband (MB) fashion. Additionally, MB data were retrospectively undersampled to simulate GRAPPA R = 2 (MB2_GRAPPA2), and both SB datasets were added to form an artificial MB dataset (SumSB). The band separation performance was quantified by signal leakage. Peak velocity and total flow values were calculated and compared to SB via intraclass correlation analysis (ICC). RESULTS: Clean slab separation was achieved yielding a mean signal leakage of 13% above the mean SB noise level. Mean total flow for MB2, SumSB, and MB_GRAPPA2 deviated less than 9% from the SB values. Peak velocities averaged over all vessels and subjects were 0.48 ± 0.11 m/s for SB, 0.47 ± 0.12 m/s for SumSB, 0.50 ± 0.13 m/s for MB2, and 0.53 ± 0.13 m/s for MB2_GRAPPA2. ICC revealed excellent absolute agreement and consistency of total flow for all methods compared to SB2. Peak velocity showed good to excellent agreement and consistency for SumSB and MB2 and MB2_GRAPPA2 method showed poor to excellent agreement and good to excellent consistency. CONCLUSION: Simultaneous multislab 4D Flow MRI allows accurate quantification of total flow and peak velocity while reducing scan times.

Semi-automated analysis of 4D flow MRI to assess the hemodynamic impact of intracranial atherosclerotic disease.

PURPOSE: This study evaluated the feasibility of using 4D flow MRI and a semi-automated analysis tool to assess the hemodynamic impact of intracranial atherosclerotic disease (ICAD). The ICAD impact was investigated by evaluating pressure drop (PD) at the atherosclerotic stenosis and changes in cerebral blood flow distribution in patients compared to healthy controls. METHODS: Dual-venc 4D flow MRI was acquired in 25 healthy volunteers and 16 ICAD patients (ICA, N = 3; MCA, N = 13) with mild (<50%), moderate (50-69%), or severe (>70%) intracranial stenosis. A semi-automated analysis tool was developed to quantify velocity and flow from 4D flow MRI and to evaluate cerebral blood flow redistribution. PD at stenosis was estimated using the Bernoulli equation. The PD calculation was examined by an in vitro phantom study against flow simulations. RESULTS: Flow analysis in controls indicated symmetry in blood flow rate (FR) and peak velocity (PV) between the brain hemispheres. For patients, PV in the affected hemisphere was significantly (65%) higher than the normal side (P = 0.002). However, FR to both hemispheres of the brain was the same. The PD depicted significant correlation with PV asymmetry in patients (ρ = 0.67 and P = 0.02), and it was significantly higher for severe compared to moderate stenosis (3.73 vs. 2.30 mm Hg, P = 0.02). CONCLUSION: 4D flow MRI quantification enables assessment of the hemodynamic impact of ICAD. The significant difference of the PD between patients with severe and moderate stenosis and its correlation with PV asymmetry suggest that PD may be a pertinent hemodynamic biomarker to evaluate ICAD.

Standardized Evaluation of Cerebral Arteriovenous Malformations Using Flow Distribution Network Graphs and Dual-venc 4D Flow MRI.

BACKGROUND: Cerebral arteriovenous malformations (AVMs) are pathological connections between arteries and veins. Dual-venc 4D flow MRI, an extended 4D flow MRI method with improved velocity dynamic range, provides time-resolved 3D cerebral hemodynamics. PURPOSE: To optimize dual-venc 4D flow imaging parameters for AVM; to assess the relationship between spatial resolution, acceleration, and flow quantification accuracy; and to introduce and apply the flow distribution network graph (FDNG) paradigm for storing and analyzing complex neurovascular 4D flow data. STUDY TYPE: Retrospective cohort study. SUBJECTS/PHANTOM: Scans were performed in a specialized flow phantom: 26 healthy subjects (age 41 ± 17 years) and five AVM patients (age 27-68 years). FIELD STRENGTH/SEQUENCE: Dual-venc 4D flow with varying spatial resolution and acceleration factors were performed at 3T field strength. ASSESSMENT: Quantification accuracy was assessed in vitro by direct comparison to measured flow. FDNGs were used to quantify and compare flow, peak velocity (PV), and pulsatility index (PI) between healthy controls with various Circle of Willis (CoW) anatomy and AVM patients. STATISTICAL TESTS: In vitro measurements were compared to ground truth with Student's t-test. In vivo groups were compared with Wilcoxon rank-sum test and Kruskal-Wallis test. RESULTS: Flow was overestimated in all in vitro experiments, by an average 7.1 ± 1.4% for all measurement conditions. Error in flow measurement was significantly correlated with number of voxels across the channel (P = 3.11 × 10-28 ) but not with acceleration factor (P = 0.74). For the venous-arterial PV and PI ratios, a significant difference was found between AVM nidal and extranidal circulation (P = 0.008 and 0.05, respectively), and between AVM nidal and healthy control circulation (P = 0.005 and 0.003, respectively). DATA CONCLUSION: Dual-venc 4D flow MRI and standardized FDNG analysis might be feasible in clinical applications. Venous-arterial ratios of PV and PI are proposed as network-based biomarkers characterizing AVM nidal hemodynamics. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;50:1718-1730.

Accelerated dual-venc 4D flow MRI for neurovascular applications.

PURPOSE: To improve velocity-to-noise ratio (VNR) and dynamic velocity range of 4D flow magnetic resonance imaging (MRI) by using dual-velocity encoding (dual-venc) with k-t generalized autocalibrating partially parallel acquisition (GRAPPA) acceleration. MATERIALS AND METHODS: A dual-venc 4D flow MRI sequence with k-t GRAPPA acceleration was developed using a shared reference scan followed by three-directional low- and high-venc scans (repetition time / echo time / flip angle = 6.1 msec / 3.4 msec / 15°, temporal/spatial resolution = 43.0 msec/1.2 × 1.2 × 1.2 mm3 ). The high-venc data were used to correct for aliasing in the low-venc data, resulting in a single dataset with the favorable VNR of the low-venc but without velocity aliasing. The sequence was validated with a 3T MRI scanner in phantom experiments and applied in 16 volunteers to investigate its feasibility for assessing intracranial hemodynamics (net flow and peak velocity) at the major intracranial vessels. In addition, image quality and image noise were assessed in the in vivo acquisitions. RESULTS: All 4D flow MRI scans were acquired successfully with an acquisition time of 20 ± 4 minutes. The shared reference scan reduced the total acquisition time by 12.5% compared to two separate scans. Phantom experiments showed 51.4% reduced noise for dual-venc compared to high-venc and an excellent agreement of velocities (ρ = 0.8, P < 0.001). The volunteer data showed decreased noise in dual-venc data (54.6% lower) compared to high-venc, and improved image quality, as graded by two observers: fewer artifacts (P < 0.0001), improved vessel conspicuity (P < 0.0001), and reduced noise (P < 0.0001). CONCLUSION: Dual-venc 4D flow MRI exhibits the superior VNR of the low-venc acquisition and reliably incorporates low- and high-velocity fields simultaneously. In vitro and in vivo data demonstrate improved flow visualization, image quality, and image noise. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 1 J. MAGN. RESON. IMAGING 2017;46:102-114.

Longitudinal Medical Student Collaborative Care in Primary Care Patients With Type 2 Diabetes.

Introduction: The Education-Centered Medical Home (ECMH) is a longitudinal clerkship that emphasizes continuity and quality improvement in primary care. We aimed to evaluate our ECMH's ability to improve type 2 diabetes mellitus (T2DM) care through a systematic chart audit and care planning process. The effect of this intervention was measured by adherence to process and outcome measures. Methods: From November 2015 to March 2017, medical students were educated on and performed monthly chart audits of guideline-based quality metrics: hemoglobin A1c systolic blood pressure; lipid and microalbuminuria evaluation; annual ophthalmic and foot examinations; flu, hepatitis, and pneumonia vaccination; and statin therapy. Patients were included if they had a diagnosis of T2DM and were seen by the ECMH clinic before and after the audits started. Students shared audit logs, using them to plan patient appointments. We assessed changes in proportion of patients meeting each guideline with Fisher's exact test. Results: The project included 11 patients with T2DM. ECMH adherence to the annual eye exam increased significantly 1 year postintervention, compared to preintervention (73% vs 55%; P=.03) and 6 months (73% vs 46%, P=.01). Conclusion: The metric with significant improvement during the chart audit, annual eye exam, is a process measure requiring advance planning. This small study suggests that a formal, regular audit process can improve student adherence to evidence-based care guidelines, particularly for tasks that require advance planning or action by the care team outside the day of a patient visit.

Automated intracranial vessel segmentation of 4D flow MRI data in patients with atherosclerotic stenosis using a convolutional neural network.

Introduction: Intracranial 4D flow MRI enables quantitative assessment of hemodynamics in patients with intracranial atherosclerotic disease (ICAD). However, quantitative assessments are still challenging due to the time-consuming vessel segmentation, especially in the presence of stenoses, which can often result in user variability. To improve the reproducibility and robustness as well as to accelerate data analysis, we developed an accurate, fully automated segmentation for stenosed intracranial vessels using deep learning. Methods: 154 dual-VENC 4D flow MRI scans (68 ICAD patients with stenosis, 86 healthy controls) were retrospectively selected. Manual segmentations were used as ground truth for training. For automated segmentation, deep learning was performed using a 3D U-Net. 20 randomly selected cases (10 controls, 10 patients) were separated and solely used for testing. Cross-sectional areas and flow parameters were determined in the Circle of Willis (CoW) and the sinuses. Furthermore, the flow conservation error was calculated. For statistical comparisons, Dice scores (DS), Hausdorff distance (HD), average symmetrical surface distance (ASSD), Bland-Altman analyses, and interclass correlations were computed using the manual segmentations from two independent observers as reference. Finally, three stenosis cases were analyzed in more detail by comparing the 4D flow-based segmentations with segmentations from black blood vessel wall imaging (VWI). Results: Training of the network took approximately 10 h and the average automated segmentation time was 2.2 ± 1.0 s. No significant differences in segmentation performance relative to two independent observers were observed. For the controls, mean DS was 0.85 ± 0.03 for the CoW and 0.86 ± 0.06 for the sinuses. Mean HD was 7.2 ± 1.5 mm (CoW) and 6.6 ± 3.7 mm (sinuses). Mean ASSD was 0.15 ± 0.04 mm (CoW) and 0.22 ± 0.17 mm (sinuses). For the patients, the mean DS was 0.85 ± 0.04 (CoW) and 0.82 ± 0.07 (sinuses), the HD was 8.4 ± 3.1 mm (CoW) and 5.7 ± 1.9 mm (sinuses) and the mean ASSD was 0.22 ± 0.10 mm (CoW) and 0.22 ± 0.11 mm (sinuses). Small bias and limits of agreement were observed in both cohorts for the flow parameters. The assessment of the cross-sectional lumen areas in stenosed vessels revealed very good agreement (ICC: 0.93) with the VWI segmentation but a consistent overestimation (bias ± LOA: 28.1 ± 13.9%). Discussion: Deep learning was successfully applied for fully automated segmentation of stenosed intracranial vasculatures using 4D flow MRI data. The statistical analysis of segmentation and flow metrics demonstrated very good agreement between the CNN and manual segmentation and good performance in stenosed vessels. To further improve the performance and generalization, more ICAD segmentations as well as other intracranial vascular pathologies will be considered in the future.

lGeneralized super-resolution 4D Flow MRI-using ensemble learning to extend across the cardiovascular system.

4D Flow Magnetic Resonance Imaging (4D Flow MRI) is a non-invasive measurement technique capable of quantifying blood flow across the cardiovascular system. While practical use is limited by spatial resolution and image noise, incorporation of trained super-resolution (SR) networks has potential to enhance image quality post-scan. However, these efforts have predominantly been restricted to narrowly defined cardiovascular domains, with limited exploration of how SR performance extends across the cardiovascular system; a task aggravated by contrasting hemodynamic conditions apparent across the cardiovasculature. The aim of our study was to explore the generalizability of SR 4D Flow MRI using a combination of heterogeneous training sets and dedicated ensemble learning. With synthetic training data generated across three disparate domains (cardiac, aortic, cerebrovascular), varying convolutional base and ensemble learners were evaluated as a function of domain and architecture, quantifying performance on both in-silico and acquired in-vivo data from the same three domains. Results show that both bagging and stacking ensembling enhance SR performance across domains, accurately predicting high-resolution velocities from low-resolution input data in-silico. Likewise, optimized networks successfully recover native resolution velocities from downsampled in-vivo data, as well as show qualitative potential in generating denoised SR-images from clinicallevel input data. In conclusion, our work presents a viable approach for generalized SR 4D Flow MRI, with ensemble learning extending utility across various clinical areas of interest.