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.

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.

As atherosclerosis is one of the main causes of death in industrial nations, noninvasive imaging modalities for studying its underlying mechanisms are in great demand. The quantification of hemodynamic parameters such as pulse wave velocity (PWV) assessed by flow sensitive magnetic resonance imaging (MRI) is a promising tool to observe plaque progression in preclinical models. Mostly, a global PWV value is assessed, however, previous studies already pointed to heterogeneous elasticity profiles in the presence of atherosclerotic plaques. Here, we present the measurement of local PWV values in the murine aortic arch assessed by 4D-flow MRI for spatially resolved elasticity measurements.

Introduction: Cardiac MRI offers detailed tissue characterization and high sensitivity to macromolecules using T 1ρ based imaging. However, fast and accurate T 1ρ mapping in the myocardium is still a major challenge in small animal models. The sequence design based on prospective triggering and breath gating causes a corruption of T 1ρ relaxation leading to quantification errors in the in vivo experiment. In this study, we present a T 1ρ quantification correction using the T 1ρ * relaxation pathway. Methods: A new signal equation was derived by analytical means describing spin-lock prepared fast gradient echo readouts: S(t SL ) = S 0 / [ exp(t SL /T 1ρ ) - cos(α) NR exp(-(N R T R +T rec )/T 1 ) ] The equation was validated in phantom experiments and used in vivo for corrected T 1ρ mapping in mice. Here, the correlation of T 1ρ and the breath dependent spin recovery time T rec was examined. The results were compared to the conventional monoexponential T 1ρ fitting. Results: In the phantom study, the novel signal equation provided improved quantification accuracy compared to monoexponential fitting. The mean quantification error could be reduced from -7.4% to -0.97%. In vivo, a correlation of T 1ρ with the respiratory cycle was observed for monoexponential fitting (PCC=0.804). Using the new correction method, this correlation was reduced (PCC=0.495) and the standard deviation was decreased from ±4.2% to ±1.3%. Conclusions: Our corrected formalism enables fast and accurate T 1ρ quantification for small animals and can improve the comparability of study results. The new technique thus offers a reasonable tool for assessing myocardial diseases. Besides, the derived signal equation could be used for sequence optimization or for subsequent correction of study results.

T1ρ dispersion quantification can potentially be used as a cardiac magnetic resonance index for sensitive detection of myocardial fibrosis without the need of contrast agents. However, dispersion quantification is still a major challenge, because T1ρ mapping for different spin lock amplitudes is a very time consuming process. This study aims to develop a fast and accurate T1ρ mapping sequence, which paves the way to cardiac T1ρ dispersion quantification within the limited measurement time of an in vivo study in small animals. A radial spin lock sequence was developed using a Bloch simulation-optimized sampling pattern and a view-sharing method for image reconstruction. For validation, phantom measurements with a conventional sampling pattern and a gold standard sequence were compared to examine T1ρ quantification accuracy. The in vivo validation of T1ρ mapping was performed in N = 10 mice and in a reproduction study in a single animal, in which ten maps were acquired in direct succession. Finally, the feasibility of myocardial dispersion quantification was tested in one animal. The Bloch simulation-based sampling shows considerably higher image quality as well as improved T1ρ quantification accuracy (+ 56%) and precision (+ 49%) compared to conventional sampling. Compared to the gold standard sequence, a mean deviation of − 0.46 ± 1.84% was observed. The in vivo measurements proved high reproducibility of myocardial T1ρ mapping. The mean T1ρ in the left ventricle was 39.5 ± 1.2 ms for different animals and the maximum deviation was 2.1% in the successive measurements. The myocardial T1ρ dispersion slope, which was measured for the first time in one animal, could be determined to be 4.76 ± 0.23 ms/kHz. This new and fast T1ρ quantification technique enables high-resolution myocardial T1ρ mapping and even dispersion quantification within the limited time of an in vivo study and could, therefore, be a reliable tool for improved tissue characterization.

Atherosclerosis is an inflammatory disease of large and medium-sized arteries, characterized by the growth of atherosclerotic lesions (plaques). These plaques often develop at inner curvatures of arteries, branchpoints, and bifurcations, where the endothelial wall shear stress is low and oscillatory. In conjunction with other processes such as lipid deposition, biomechanical factors lead to local vascular inflammation and plaque growth. There is also evidence that low and oscillatory shear stress contribute to arterial remodeling, entailing a loss in arterial elasticity and, therefore, an increased pulse-wave velocity. Although altered shear stress profiles, elasticity and inflammation are closely intertwined and critical for plaque growth, preclinical and clinical investigations for atherosclerosis mostly focus on the investigation of one of these parameters only due to the experimental limitations. However, cardiovascular magnetic resonance imaging (MRI) has been demonstrated to be a potent tool which can be used to provide insights into a large range of biological parameters in one experimental session. It enables the evaluation of the dynamic process of atherosclerotic lesion formation without the need for harmful radiation. Flow-sensitive MRI provides the assessment of hemodynamic parameters such as wall shear stress and pulse wave velocity which may replace invasive and radiation-based techniques for imaging of the vascular function and the characterization of early plaque development. In combination with inflammation imaging, the analyses and correlations of these parameters could not only significantly advance basic preclinical investigations of atherosclerotic lesion formation and progression, but also the diagnostic clinical evaluation for early identification of high-risk plaques, which are prone to rupture. In this review, we summarize the key applications of magnetic resonance imaging for the evaluation of plaque characteristics through flow sensitive and morphological measurements. The simultaneous measurements of functional and structural parameters will further preclinical research on atherosclerosis and has the potential to fundamentally improve the detection of inflammation and vulnerable plaques in patients.

Journal of Magnetic Resonance ImagingEarly View Editorial Editorial for “Non-enhanced Chemical Exchange Saturation Transfer Cardiac Magnetic Resonance Imaging in Patients With Amyloid Light Chain Amyloidosis” Patrick M. Winter PhD, Corresponding Author Patrick M. Winter PhD patrick.winter@utdallas.edu orcid.org/0000-0001-5975-5465 Department of Bioengineering, The University of Texas at Dallas, Richardson, Texas, USA Address reprint requests to: P.M.W., 800 W. Campbell Rd., BSB11, Richardson, TX 75080, USA. E-mail: patrick.winter@utdallas.eduSearch for more papers by this author Patrick M. Winter PhD, Corresponding Author Patrick M. Winter PhD patrick.winter@utdallas.edu orcid.org/0000-0001-5975-5465 Department of Bioengineering, The University of Texas at Dallas, Richardson, Texas, USA Address reprint requests to: P.M.W., 800 W. Campbell Rd., BSB11, Richardson, TX 75080, USA. E-mail: patrick.winter@utdallas.eduSearch for more papers by this author First published: 23 July 2021 https://doi.org/10.1002/jmri.27857 Level of Evidence:: 5 Technical Efficacy Stage:: 2 Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Early ViewOnline Version of Record before inclusion in an issue RelatedInformation

Growth, ageing and atherosclerotic plaque development alter the biomechanical forces acting on the vessel wall. However, monitoring the detailed local changes in wall shear stress (WSS) at distinct sites of the murine aortic arch over time has been challenging. Here, we studied the temporal and spatial changes in flow, WSS, oscillatory shear index (OSI) and elastic properties of healthy wildtype (WT, n = 5) and atherosclerotic apolipoprotein E-deficient (Apoe−/−, n = 6) mice during ageing and atherosclerosis using high-resolution 4D flow magnetic resonance imaging (MRI). Spatially resolved 2D projection maps of WSS and OSI of the complete aortic arch were generated, allowing the pixel-wise statistical analysis of inter- and intragroup hemodynamic changes over time and local correlations between WSS, pulse wave velocity (PWV), plaque and vessel wall characteristics. The study revealed converse differences of local hemodynamic profiles in healthy WT and atherosclerotic Apoe−/− mice, and we identified the circumferential WSS as potential marker of plaque size and composition in advanced atherosclerosis and the radial strain as a potential marker for vascular elasticity. Two-dimensional (2D) projection maps of WSS and OSI, including statistical analysis provide a powerful tool to monitor local aortic hemodynamics during ageing and atherosclerosis. The correlation of spatially resolved hemodynamics and plaque characteristics could significantly improve our understanding of the impact of hemodynamics on atherosclerosis, which may be key to understand plaque progression towards vulnerability.

Abstract Introduction Over the past decade, CMRI has become the method of choice for characterizing fibrotic scars. Native T1ρ mapping offers an alternative to conventional T1 and T2 quantification techniques due to its high sensitivity to low-frequency processes. In addition, there is the possibility of T1ρ dispersion imaging, which could be used as a sensitive biomarker for assessing myocardial fibrosis [1]. However, due to a very long measurement time, T1ρ dispersion quantification in myocardium can hardly be done in the limited time of a small animal study. In this work we present a concept for rapid T1ρ dispersion quantification based on the new approach of synthetic dispersion reconstruction (SynDR). Theory A T1ρ map is calculated by measuring Nt T1ρ weighted images using different spin lock (SL) times. T1ρ dispersion quantification requires Nf T1ρ maps with different SL amplitudes. Hence the measurement time is very time consuming, because it requires the acquisition of Nt*Nf images (full mapping). With our new approach (SynDR), only a single T1ρ reference map and a series of dispersion weighted images need to be acquired. The T1ρ dispersion can be reconstructed by synthetically generated maps, whereby each map is calculated from the reference map and the dispersion weighted images, only requiring Nt+Nf images. Methods All measurements were performed on a 7T small animal scanner. The method was based on an optional cartesian/radial gradient echo sequence using large flip angles (45°) and an optimized readout sorting. The quantification accuracy of SynDR was compared with full mapping measurements in a phantom experiment and validated in vivo on mice. The synthetic T1ρ maps were used to perform a dispersion analysis in myocardium. Results The comparison between SynDR and the full mapping reference in phantoms showed a very high quantification accuracy with a mean/maximum deviation of 1.1% and 1.7%. Fig. 1 shows synthetic T1ρ maps (a) in healthy mice and the obtained dispersion map (b) using SynDR. In the dispersion analysis (c) a T1ρ slope of 5.6±1.5ms/kHz was obtained for myocardium. Here an acceleration factor of 4 could be realized in comparison to full mapping. In further measurements, an acceleration of 7.4 could be reached using a radial readout with KWIC filter view sharing. Discussion In this work, a novel T1ρ dispersion imaging method was presented that far exceeds the speed of conventional full mapping methods. The acceleration is based on avoiding unnecessary measurements of T1ρ weighted images through more efficient mathematical modeling. Further acceleration could be achieved using an optimized radial data acquisition. The method shows good image quality and high quantification accuracy both in phantom and in vivo. Based on the promising results, further studies in mice are planned to investigate the dispersion character of healthy and diseased tissues. Reference [1] Yin Q et al. Magn Reson Imaging. 2017 Oct; 42:69–73. SynDR method and T1ρ dispersion analysis Funding Acknowledgement Type of funding source: Public grant(s) – National budget only. Main funding source(s): BRD, Bundesministerium für Bildung und Forschung

4D flow cardiovascular magnetic resonance (CMR) and the assessment of wall shear stress (WSS) are non-invasive tools to study cardiovascular risks in vivo. Major limitations of conventional triggered methods are the long measurement times needed for high-resolution data sets and the necessity of stable electrocardiographic (ECG) triggering. In this work an ECG-free retrospectively synchronized method is presented that enables accelerated high-resolution measurements of 4D flow and WSS in the aortic arch of mice. 4D flow and WSS were measured in the aortic arch of 12-week-old wildtype C57BL/6 J mice (n = 7) with a radial 4D-phase-contrast (PC)-CMR sequence, which was validated in a flow phantom. Cardiac and respiratory motion signals were extracted from the radial CMR signal and were used for the reconstruction of 4D-flow data. Rigid motion correction and a first order B0 correction was used to improve the robustness of magnitude and velocity data. The aortic lumen was segmented semi-automatically. Temporally averaged and time-resolved WSS and oscillatory shear index (OSI) were calculated from the spatial velocity gradients at the lumen surface at 14 locations along the aortic arch. Reproducibility was tested in 3 animals and the influence of subsampling was investigated. Volume flow, cross-sectional areas, WSS and the OSI were determined in a measurement time of only 32 min. Longitudinal and circumferential WSS and radial stress were assessed at 14 analysis planes along the aortic arch. The average longitudinal, circumferential and radial stress values were 1.52 ± 0.29 N/m2, 0.28 ± 0.24 N/m2 and − 0.21 ± 0.19 N/m2, respectively. Good reproducibility of WSS values was observed. This work presents a robust measurement of 4D flow and WSS in mice without the need of ECG trigger signals. The retrospective approach provides fast flow quantification within 35 min and a flexible reconstruction framework.

When classifying molecules for virtual screening, the molecular structure first needs to be converted into meaningful features, before a classifier can be trained. The most common methods use a static algorithm that has been created based on domain knowledge to perform this generation of features. We propose an approach where this conversion is learned by a convolutional neural network finding features that are useful for the task at hand based on the available data. Preliminary results indicate that our current approach can already come up with features that perform similarly well as common methods. Since this approach does not yet use any chemical properties, results could be improved in future versions.

Local aortic pulse wave velocity (PWV) is a measure for vascular stiffness and has a predictive value for cardiovascular events. Ultra high field CMR scanners allow the quantification of local PWV in mice, however these systems are yet unable to monitor the distribution of local elasticities.

Increased aortic stiffness is known to be associated with atherosclerosis and has a predictive value for cardiovascular events. This study aims to investigate the local distribution of early arterial stiffening due to initial atherosclerotic lesions. Therefore, global and local pulse wave velocity (PWV) were measured in ApoE-/- and wild type (WT) mice using ultrahigh field MRI. For quantification of global aortic stiffness, a new multi-point transit-time (TT) method was implemented and validated to determine the global PWV in the murine aorta. Local aortic stiffness was measured by assessing the local PWV in the upper abdominal aorta, using the flow/area (QA) method. Significant differences between age matched ApoE-/- and WT mice were determined for global and local PWV measurements (global PWV: ApoE-/-: 2.7±0.2m/s vs WT: 2.1±0.2m/s, P<0.03; local PWV: ApoE-/-: 2.9±0.2m/s vs WT: 2.2±0.2m/s, P<0.03). Within the WT mouse group, the global PWV correlated well with the local PWV in the upper abdominal aorta (R2 = 0.75, P<0.01), implying a widely uniform arterial elasticity. In ApoE-/- animals, however, no significant correlation between individual local and global PWV was present (R2 = 0.07, P = 0.53), implying a heterogeneous distribution of vascular stiffening in early atherosclerosis. The assessment of global PWV using the new multi-point TT measurement technique was validated against a pressure wire measurement in a vessel phantom and showed excellent agreement. The experimental results demonstrate that vascular stiffening caused by early atherosclerosis is unequally distributed over the length of large vessels. This finding implies that assessing heterogeneity of arterial stiffness by multiple local measurements of PWV might be more sensitive than global PWV to identify early atherosclerotic lesions.

Purpose: The importance of the orientation of the selective inversion slice in relation to the anatomy in flow-sensitive alternating inversion recovery arterial spin labeling (FAIR ASL) kidney perfusion measurements is demonstrated by comparing the standard FAIR scheme to a scheme with an improved slice selective control experiment. Methods: A FAIR ASL method is used. The selective inversion preparation slice is set perpendicular to the measurement slice to decrease the unintended labeling of arterial spins in the control experiment. A T t-based quantification method compensates for the effects of the imperfect inversion on the edge of the selective inversion slice. The quantified perfusion values are compared to the standard experiment with parallel orientation of imaging and selective inversion slice. Results: Perfusion maps acquired with the perpendicular inversion slice orientation show higher sensitivity compared to the parallel orientation. The 77-based quantification method removes artifacts arising from imperfect inversion slice profiles. The stability is improved. Conclusion: Adjusting the labeling technique to the anatomy is of high importance. Improved sensitivity and reproducibility could be demonstrated. The proposed method provides a solution to the problem of FAIR ASL measurements of renal perfusion in coronal view.

PURPOSE:An algorithm is presented to enable cardiac and respiratory self-gating in combination with Inversion Recovery Look-Locker read-outs. METHODS:A radial inversion recovery snapshot FLASH sequence was adapted for retrospective cardiac T1 measurements in mice. Cardiac and respiratory data were extracted from the k-space center of radial projections and an adapted method for retrospective cardiac synchronization is introduced. Electrocardiogram (ECG) data was acquired concurrently for validation of the proposed self-gating technique. T1 maps generated by the proposed technique were compared with maps reconstructed with the ECG reference. RESULTS:Respiratory gating and cardiac trigger points could be obtained for the whole time course of the relaxation dynamic and correlate very well to the ECG signal. T1 maps reconstructed with the self-gating technique are in very good agreement with maps reconstructed with the external reference. CONCLUSION:The proposed method extends "wireless" cardiac MRI to non-steady-state inversion recovery measurements. T1 maps were generated with a quality comparable to ECG based reconstructions. As the method does not rely on an ECG trigger signal it provides easier animal handling. Magn Reson Med 76:1887-1894, 2016. © 2016 International Society for Magnetic Resonance in Medicine.

PurposeMRI‐guided high intensity focused ultrasound (MR‐HIFU) allows noninvasive heating of deep tissues. Specifically targeting visceral fat deposits with MR‐HIFU could offer an effective therapy for reversing the development of obesity, diabetes, and metabolic syndrome.MethodsOverweight rats received either MR‐HIFU of visceral fat, sham treatment, no treatment, or ex vivo temperature calibration. Conventional MR thermometry methods are not effective in fat tissue. Therefore, the T2 of fat was used to estimate heating in adipose tissue.ResultsHIFU treated rats lost 7.5% of their body weight 10 days after HIFU, compared with 1.9% weight loss in sham animals (P = 0.008) and 1.3% weight increase in untreated animals (P = 0.004). Additionally, the abdominal fat volume in treated animals decreased by 8.2 mL 7 days after treatment (P = 0.002). The T2 of fat at 1.5 Tesla increased by 3.3 ms per °C. The fat T2 was 103.3 ms before HIFU, but increased to 128.7 ms (P = 0.0005) after HIFU at 70 watts for 16 s and to 131.9 ms (P = 0.0005) after HIFU at 100 watts for 16 s.ConclusionThese experiments demonstrate that MR‐HIFU of visceral fat could provide a safe, effective, and noninvasive weight loss therapy for combating obesity and the subsequent medical complications. Magn Reson Med 76:282–289, 2016. © 2015 Wiley Periodicals, Inc.

A method for the quantification of perfusion in murine myocardium is demonstrated. The method allows for the reconstruction of perfusion maps on arbitrary time points in the heart cycle while addressing problems that arise due to the irregular heart beat of mice.