Recent reports have suggested that age-related arterial stiffening and excessive cerebral arterial pulsatility cause blood–brain barrier breakdown, brain atrophy and cognitive decline. This has spurred interest in developing non-invasive methods to measure pulsatility in distal vessels, closer to the cerebral microcirculation. Here, we report a method based on four-dimensional (4D) flow MRI to estimate a global composite flow waveform of distal cerebral arteries. The method is based on finding and sampling arterial waveforms from thousands of cross sections in numerous small vessels of the brain, originating from cerebral cortical arteries. We demonstrate agreement with internal and external reference methods and show the ability to capture significant increases in distal cerebral arterial pulsatility as a function of age. The proposed approach can be used to advance our understanding regarding excessive arterial pulsatility as a potential trigger of cognitive decline and dementia.

Background Accelerated 4D flow MRI allows for high‐resolution velocity measurements with whole‐brain coverage. Such scans are increasingly used to calculate flow rates of individual arteries in the vascular tree, but detailed information about the accuracy and precision in relation to different postprocessing options is lacking. Purpose To evaluate and optimize three proposed segmentation methods and determine the accuracy of in vivo 4D flow MRI blood flow rate assessments in major cerebral arteries, with high‐resolution 2D PCMRI as a reference. Study Type Prospective. Subjects Thirty‐five subjects (20 women, 79 ± 5 years, range 70–91 years). Field Strength/Sequence 4D flow MRI with PC‐VIPR and 2D PCMRI acquired with a 3 T scanner. Assessment We compared blood flow rates measured with 4D flow MRI, to the reference, in nine main cerebral arteries. Lumen segmentation in the 4D flow MRI was performed with k‐means clustering using four different input datasets, and with two types of thresholding methods. The threshold was defined as a percentage of the maximum intensity value in the complex difference image. Local and global thresholding approaches were used, with evaluated thresholds from 6–26%. Statistical Tests Paired t‐test, F‐test, linear correlation (P < 0.05 was considered significant) along with intraclass correlation (ICC). Results With the thresholding methods, the lowest average flow difference was obtained for 20% local (0.02 ± 15.0 ml/min, ICC = 0.97, n = 310) or 10% global (0.08 ± 17.3 ml/min, ICC = 0.97, n = 310) thresholding with a significant lower standard deviation for local (F‐test, P = 0.01). For all clustering methods, we found a large systematic underestimation of flow compared with 2D PCMRI (16.1–22.3 ml/min). Data Conclusion A locally adapted threshold value gives a more stable result compared with a globally fixed threshold. 4D flow with the proposed segmentation method has the potential to become a useful reliable clinical tool for assessment of blood flow in the major cerebral arteries. Level of Evidence: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:511–518.

Background: Four-dimensional flow magnetic resonance imaging (4D flow MRI) enables efficient investigation of cerebral blood flow pulsatility in the cerebral arteries. This is important for exploring hemodynamic mechanisms behind vascular diseases associated with arterial pulsations. Purpose: To investigate the feasibility of pulsatility assessments with 4D flow MRI, its agreement with reference twodimensional phase-contrast MRI (2D PC-MRI) measurements, and to demonstrate how 4D flow MRI can be used to assess cerebral arterial compliance and cerebrovascular resistance in major cerebral arteries. Study Type: Prospective. Subjects: Thirty-five subjects (20 women, 79 AE 5 years, range 70-91 years). Field Strength/Sequence: 4D flow MRI (PC-VIPR) and 2D PC-MRI acquired with a 3T scanner. Assessment: Time-resolved flow was assessed in nine cerebral arteries. From the pulsatile flow waveform in each artery, amplitude (ΔQ), volume load (ΔV), and pulsatility index (PI) were calculated. To reduce high-frequency noise in the 4D flow MRI data, the flow waveforms were low-pass filtered. From the total cerebral blood flow, total PI (PI tot ), total volume load (ΔV tot ), cerebral arterial compliance (C), and cerebrovascular resistance (R) were calculated. Statistical Tests: Two-tailed paired t-test, intraclass correlation (ICC). Results: There was no difference in ΔQ between 4D flow MRI and the reference (0.00 AE 0.022 ml/s, mean AE SEM, P = 0.97, ICC = 0.95, n = 310) with a cutoff frequency of 1.9 Hz and 15 cut plane long arterial segments. For ΔV, the difference was -0.006 AE 0.003 ml (mean AE SEM, P = 0.07, ICC = 0.93, n = 310) without filtering. Total R was 11.4 AE 2.41 mmHg/(ml/s) (mean AE SD) and C was 0.021 AE 0.009 ml/mmHg (mean AE SD). ΔV tot was 1.21 AE 0.29 ml (mean AE SD) with an ICC of 0.82 compared with the reference. PI tot was 1.08 AE 0.21 (mean AE SD). Data Conclusion: We successfully assessed 4D flow MRI cerebral arterial pulsatility, cerebral arterial compliance, and cerebrovascular resistance. Averaging of multiple cut planes and low-pass filtering was necessary to assess accurate peak-topeak features in the flow rate waveforms. Level of Evidence: 2 Technical Efficacy Stage: 2 J. MAGN. RESON. IMAGING 2020;51:1516-1525.View this article online at wileyonlinelibrary.com.

An internal carotid artery (ICA) stenosis can potentially decrease the perfusion pressure to the brain. In this study, computational fluid dynamics (CFD) was used to study if there was a hemispheric pressure laterality between the contra- and ipsilateral middle cerebral artery (MCA) in patients with a symptomatic ICA stenosis. We further investigated if this MCA pressure laterality (ΔPMCA) was related to the hemispheric flow laterality (ΔQ) in the anterior circulation, i.e., ICA, proximal MCA and the proximal anterior cerebral artery (ACA). Twenty-eight patients (73±6 years, range 59–80 years, 21 men) with symptomatic ICA stenosis were included. Flow rates were measured using 4D flow MRI data (PC-VIPR) and vessel geometries were obtained from computed tomography angiography. The ΔPMCA was calculated from CFD, where patient-specific flow rates were applied at all input- and output boundaries. The ΔPMCA between the contra- and ipsilateral side was 6.4±8.3 mmHg (p<0.001) (median 3.9 mmHg, range -1.3 to 31.9 mmHg). There was a linear correlation between the ΔPMCA and ΔQICA (r = 0.85, p<0.001) and ΔQACA (r = 0.71, p<0.001), respectively. The correlation to ΔQMCA was weaker (r = 0.47, p = 0.011). In conclusion, the MCA pressure laterality obtained with CFD, is a promising physiological biomarker that can grade the hemodynamic disturbance in patients with a symptomatic ICA stenosis.

Visualizing medical images from patients as physical 3D models (phantom models) have many roles in the medical field, from education to preclinical preparation and clinical research. However, current phantom models are generally generic, expensive, and time-consuming to fabricate. Thus, there is a need for a cost- and time-efficient pipeline from medical imaging to patient-specific phantom models. In this work, we present a method for creating complex 3D sacrificial molds using an off-the-shelf water-soluble resin and a low-cost desktop 3D printer. This enables us to recreate parts of the cerebral arterial tree as a full-scale phantom model ($$10\times 6\times 4$$ 10 × 6 × 4  cm) in transparent silicone rubber (polydimethylsiloxane, PDMS) from computed tomography angiography images (CTA). We analyzed the model with magnetic resonance imaging (MRI) and compared it with the patient data. The results show good agreement and smooth surfaces for the arteries. We also evaluate our method by looking at its capability to reproduce 1 mm channels and sharp corners. We found that round shapes are well reproduced, whereas sharp features show some divergence. Our method can fabricate a patient-specific phantom model with less than 2 h of total labor time and at a low fabrication cost.