4Dflow-VP-Net: A deep convolutional neural network for noninvasive estimation of relative pressures in stenotic flows from 4D flow MRI.

PURPOSE: To estimate relative transvalvular pressure gradient (TVPG) noninvasively from 4D flow MRI. METHODS: A novel deep learning-based approach is proposed to estimate pressure gradient across stenosis from four-dimensional flow MRI (4D flow MRI) velocities. A deep neural network 4D flow Velocity-to-Presure Network (4Dflow-VP-Net) was trained to learn the spatiotemporal relationship between velocities and pressure in stenotic vessels. Training data were simulated by computational fluid dynamics (CFD) for different pulsatile flow conditions under an aortic flow waveform. The network was tested to predict pressure from CFD-simulated velocity data, in vitro 4D flow MRI data, and in vivo 4D flow MRI data of patients with both moderate and severe aortic stenosis. TVPG derived from 4Dflow-VP-Net was compared to catheter-based pressure measurements for available flow rates, in vitro and Doppler echocardiography-based pressure measurement, in vivo. RESULTS: Relative pressures calculated by 4Dflow-VP-Net and in vitro pressure catheterization revealed strong correlation (r2 = 0.91). Correlations analysis of TVPG from reference CFD and 4Dflow-VP-Net for 450 simulated flow conditions showed strong correlation (r2 = 0.99). TVPG from in vitro MRI had a correlation coefficient of r2 = 0.98 with reference CFD. 4Dflow-VP-Net, applied to 4D flow MRI in 16 patients, showed comparable TVPG measurement with Doppler echocardiography (r2 = 0.85). Bland-Altman analysis of TVPG measurements showed mean bias and limits of agreement of -0.20 ± 2.07 mmHg and 0.19 ± 0.45 mmHg for CFD-simulated velocities and in vitro 4D flow velocities. In patients, overestimation of Doppler echocardiography relative to TVPG from 4Dflow-VP-Net (10.99 ± 6.77 mmHg) was observed. CONCLUSION: The proposed approach can predict relative pressure in both in vitro and in vivo 4D flow MRI of aortic stenotic patients with high fidelity.

FlowRAU-Net: Accelerated 4D Flow MRI of Aortic Valvular Flows With a Deep 2D Residual Attention Network.

In this work, we propose a novel deep learning reconstruction framework for rapid and accurate reconstruction of 4D flow MRI data. Reconstruction is performed on a slice-by-slice basis by reducing artifacts in zero-filled reconstructed complex images obtained from undersampled k-space. A deep residual attention network FlowRAU-Net is proposed, trained separately for each encoding direction with 2D complex image slices extracted from complex 4D images at each temporal frame and slice position. The network was trained and tested on 4D flow MRI data of aortic valvular flow in 18 human subjects. Performance of the reconstructions was measured in terms of image quality, 3-D velocity vector accuracy, and accuracy in hemodynamic parameters. Reconstruction performance was measured for three different k-space undersamplings and compared with one state of the art compressed sensing reconstruction method and three deep learning-based reconstruction methods. The proposed method outperforms state of the art methods in all performance measures for all three different k-space undersamplings. Hemodynamic parameters such as blood flow rate and peak velocity from the proposed technique show good agreement with reference flow parameters. Visualization of the reconstructed image and velocity magnitude also shows excellent agreement with the fully sampled reference dataset. Moreover, the proposed method is computationally fast. Total 4D flow data (including all slices in space and time) for a subject can be reconstructed in 69 seconds on a single GPU. Although the proposed method has been applied to 4D flow MRI of aortic valvular flows, given a sufficient number of training samples, it should be applicable to other arterial flows.

Relative pressure estimation from 4D flow MRI using generalized Bernoulli equation in a phantom model of arterial stenosis.

OBJECTIVE: Arterial stenosis is a significant cardiovascular disease requiring accurate estimation of the pressure gradients for determining hemodynamic significance. In this paper, we propose Generalized Bernoulli Equation (GBE) utilizing interpolated-based method to estimate relative pressures using streamlines and pathlines from 4D Flow MRI. METHODS: 4D Flow MRI data in a stenotic phantom model and computational fluid dynamics simulated velocities generated under identical flow conditions were processed by Generalized Bernoulli Equation (GBE), Reduced Bernoulli Equations (RBE), as well as the Simple Bernoulli Equation (SBE) which is clinically prevalent. Pressures derived from 4D flow MRI and noise corrupted CFD velocities were compared with pressures generated directly with CFD as well as pressures obtained using Millar catheters under identical flow conditions. RESULTS: It was found that SBE and RBE methods underestimated the relative pressure for lower flow rates while overestimating the relative pressure at higher flow rates. Specifically, compared to the reference pressure, SBE underestimated the maximum relative pressure by 22[Formula: see text] for a pulsatile flow data with peak flow rate [Formula: see text] and overestimated by around 40[Formula: see text] when [Formula: see text]. In contrast, for GBE method the relative pressure values were overestimated by 15[Formula: see text] with [Formula: see text]and around 10[Formula: see text] with [Formula: see text]. CONCLUSION: GBE methods showed robust performance to additive image noise compared to other methods. Our findings indicate that GBE pressure estimation over pathlines attains the highest level of accuracy compared to GBE over streamlines, and the SBE and RBE methods.

Lung Nodule Malignancy Prediction in Sequential CT Scans: Summary of ISBI 2018 Challenge.

Lung cancer is by far the leading cause of cancer death in the US. Recent studies have demonstrated the effectiveness of screening using low dose CT (LDCT) in reducing lung cancer related mortality. While lung nodules are detected with a high rate of sensitivity, this exam has a low specificity rate and it is still difficult to separate benign and malignant lesions. The ISBI 2018 Lung Nodule Malignancy Prediction Challenge, developed by a team from the Quantitative Imaging Network of the National Cancer Institute, was focused on the prediction of lung nodule malignancy from two sequential LDCT screening exams using automated (non-manual) algorithms. We curated a cohort of 100 subjects who participated in the National Lung Screening Trial and had established pathological diagnoses. Data from 30 subjects were randomly selected for training and the remaining was used for testing. Participants were evaluated based on the area under the receiver operating characteristic curve (AUC) of nodule-wise malignancy scores generated by their algorithms on the test set. The challenge had 17 participants, with 11 teams submitting reports with method description, mandated by the challenge rules. Participants used quantitative methods, resulting in a reporting test AUC ranging from 0.698 to 0.913. The top five contestants used deep learning approaches, reporting an AUC between 0.87 - 0.91. The team's predictor did not achieve significant differences from each other nor from a volume change estimate (p =.05 with Bonferroni-Holm's correction).

Recurrent attention network for false positive reduction in the detection of pulmonary nodules in thoracic CT scans.

PURPOSE: Multiview two-dimensional (2D) convolutional neural networks (CNNs) and three-dimensional (3D) CNNs have been successfully used for analyzing volumetric data in many state-of-the-art medical imaging applications. We propose an alternative modular framework that analyzes volumetric data with an approach that is analogous to radiologists' interpretation, and apply the framework to reduce false positives that are generated in computer-aided detection (CADe) systems for pulmonary nodules in thoracic computed tomography (CT) scans. METHODS: In our approach, a deep network consisting of 2D CNNs first processes slices individually. The features extracted in this stage are then passed to a recurrent neural network (RNN), thereby modeling consecutive slices as a sequence of temporal data and capturing the contextual information across all three dimensions in the volume of interest. Outputs of the RNN layer are weighed before the final fully connected layer, enabling the network to scale the importance of different slices within a volume of interest in an end-to-end training framework. RESULTS: We validated the proposed architecture on the false positive reduction track of the lung nodule analysis (LUNA) challenge for pulmonary nodule detection in chest CT scans, and obtained competitive results compared to 3D CNNs. Our results show that the proposed approach can encode the 3D information in volumetric data effectively by achieving a sensitivity >0.8 with just 1/8 false positives per scan. CONCLUSIONS: Our experimental results demonstrate the effectiveness of temporal analysis of volumetric images for the application of false positive reduction in chest CT scans and show that state-of-the-art 2D architectures from the literature can be directly applied to analyzing volumetric medical data. As newer and better 2D architectures are being developed at a much faster rate compared to 3D architectures, our approach makes it easy to obtain state-of-the-art performance on volumetric data using new 2D architectures.

Lung Nodule Malignancy Prediction From Longitudinal CT Scans With Siamese Convolutional Attention Networks.

Goal: We propose a convolutional attention-based network that allows for use of pre-trained 2-D convolutional feature extractors and is extendable to multi-time-point classification in a Siamese structure. Methods: Our proposed framework is evaluated for single- and multi-time-point classification to explore the value that temporal information, such as nodule growth, adds to malignancy prediction. Results: Our results show that the proposed method outperforms a comparable 3-D network with less than half the parameters on single-time-point classification and further achieves performance gains on multi-time-point classification. Conclusions: Attention-based, Siamese 2-D pre-trained CNNs lead to fast training times and are effective for malignancy prediction from single-time-point or multiple-time-point imaging data.

Dual-Venc acquisition for 4D flow MRI in aortic stenosis with spiral readouts.

BACKGROUND: Single Venc 4D flow MRI with Cartesian readout is hampered by poor velocity resolution and noise when imaging during diastole. Dual Venc acquisitions typically require the acquisition of two distinct datasets, which leads to longer scan times. PURPOSE/HYPOTHESIS: To design and develop a 4D Spiral Dual Venc sequence. The sequence allows for separate systolic and diastolic Venc s as part of a single acquisition with a prescribed switch time. The implemented sequence was hypothesized to be comparable to Cartesian 4D flow, but with increased velocity resolution in the diastolic phase and with better scan efficiency and reduced noise. STUDY TYPE: Prospective. POPULATION: The studied populations were two phantoms-a straight pipe with a stenotic narrowing and a phantom of the aortic arch which included a calcific polymeric valve-under both steady and pulsatile flows, six healthy volunteers, and eight patients with severe aortic stenosis (AS). FIELD STRENGTH/SEQUENCE: 1.5T, Dual Venc 4D flow with spiral readouts. ASSESSMENT: Data from the proposed sequence were compared with data from 4D Cartesian Dual Venc and Single Venc acquisitions. Noise was assessed from the acquired velocity data with the pump turned off and by varying Venc . Steady acquisitions were compared to the proximal slice of the lowest Single Venc acquisition. STATISTICAL TESTS: Steady flows were compared using relative-root-mean-squared-error (RRMSE). For in vivo flows and pulsatile in vitro flows, net flow for corresponding timepoints were compared with the Pearson correlation test (P < 0.01). RESULTS: For steady flows, RRMSEs for Single Venc s ranged from 17.6% to 19.4%, and 9.6% to 16.5% for Dual Venc s. The net flow correlation coefficient for the aortic arch phantom was 0.975, and 0.995 for the stenotic phantom. Normal volunteer and patient comparisons yielded a correlation of 0.970 and 0.952, respectively. in vitro and in vivo pulsatile flow waveforms closely matched. DATA CONCLUSION: The Dual Venc offers improved noise properties and velocity resolution, while the spiral trajectory offers a scan efficient acquisition with short echo time yielding reduced flow artifacts. LEVEL OF EVIDENCE: 2 Technical Efficacy Stage: 1 J. Magn. Reson. Imaging 2020;52:117-128.

Segmentation and tracking of lung nodules via graph-cuts incorporating shape prior and motion from 4D CT.

PURPOSE: We have developed a robust tool for performing volumetric and temporal analysis of nodules from respiratory gated four-dimensional (4D) CT. The method could prove useful in IMRT of lung cancer. METHODS: We modified the conventional graph-cuts method by adding an adaptive shape prior as well as motion information within a signed distance function representation to permit more accurate and automated segmentation and tracking of lung nodules in 4D CT data. Active shape models (ASM) with signed distance function were used to capture the shape prior information, preventing unwanted surrounding tissues from becoming part of the segmented object. The optical flow method was used to estimate the local motion and to extend three-dimensional (3D) segmentation to 4D by warping a prior shape model through time. The algorithm has been applied to segmentation of well-circumscribed, vascularized, and juxtapleural lung nodules from respiratory gated CT data. RESULTS: In all cases, 4D segmentation and tracking for five phases of high-resolution CT data took approximately 10 min on a PC workstation with AMD Phenom II and 32 GB of memory. The method was trained based on 500 breath-held 3D CT data from the LIDC data base and was tested on 17 4D lung nodule CT datasets consisting of 85 volumetric frames. The validation tests resulted in an average Dice Similarity Coefficient (DSC) = 0.68 for all test data. An important by-product of the method is quantitative volume measurement from 4D CT from end-inspiration to end-expiration which will also have important diagnostic value. CONCLUSION: The algorithm performs robust segmentation of lung nodules from 4D CT data. Signed distance ASM provides the shape prior information which based on the iterative graph-cuts framework is adaptively refined to best fit the input data, preventing unwanted surrounding tissue from merging with the segmented object.

3-D Active Contour Segmentation Based on Sparse Linear Combination of Training Shapes (SCoTS).

SCoTS captures a sparse representation of shapes in an input image through a linear span of previously delineated shapes in a training repository. The model updates shape prior over level set iterations and captures variabilities in shapes by a sparse combination of the training data. The level set evolution is therefore driven by a data term as well as a term capturing valid prior shapes. During evolution, the shape prior influence is adjusted based on shape reconstruction, with the assigned weight determined from the degree of sparsity of the representation. For the problem of lung nodule segmentation in X-ray CT, SCoTS offers a unified framework, capable of segmenting nodules of all types. Experimental validations are demonstrated on 542 3-D lung nodule images from the LIDC-IDRI database. Despite its generality, SCoTS is competitive with domain specific state of the art methods for lung nodule segmentation.

4D spiral imaging of flows in stenotic phantoms and subjects with aortic stenosis.

PURPOSE: The utility of four-dimensional (4D) spiral flow in imaging of stenotic flows in both phantoms and human subjects with aortic stenosis is investigated. METHODS: The method performs 4D flow acquisitions through a stack of interleaved spiral k-space readouts. Relative to conventional 4D flow, which performs Cartesian readout, the method has reduced echo time. Thus, reduced flow artifacts are observed when imaging high-speed stenotic flows. Four-dimensional spiral flow also provides significant savings in scan times relative to conventional 4D flow. RESULTS: In vitro experiments were performed under both steady and pulsatile flows in a phantom model of severe stenosis (one inch diameter at the inlet, with 87% area reduction at the throat of the stenosis) while imaging a 6-cm axial extent of the phantom, which included the Gaussian-shaped stenotic narrowing. In all cases, gradient strength and slew rate for standard clinical acquisitions, and identical field of view and resolution were used. For low steady flow rates, quantitative and qualitative results showed a similar level of accuracy between 4D spiral flow (echo time [TE] = 2 ms, scan time = 40 s) and conventional 4D flow (TE = 3.6 ms, scan time = 1:01 min). However, in the case of high steady flow rates, 4D spiral flow (TE = 1.57 ms, scan time = 38 s) showed better visualization and accuracy as compared to conventional 4D flow (TE = 3.2 ms, scan time = 51 s). At low pulsatile flow rates, a good agreement was observed between 4D spiral flow (TE = 2 ms, scan time = 10:26 min) and conventional 4D flow (TE = 3.6 ms, scan time = 14:20 min). However, in the case of high flow-rate pulsatile flows, 4D spiral flow (TE = 1.57 ms, scan time = 10:26 min) demonstrated better visualization as compared to conventional 4D flow (TE = 3.2 ms, scan time = 14:20 min). The feasibility of 4D spiral flow was also investigated in five normal volunteers and four subjects with mild-to-moderate aortic stenosis. The approach achieved TE = 1.68 ms and scan time = 3:44 min. The conventional sequence achieved TE = 2.9 ms and scan time = 5:23 min. In subjects with aortic stenosis, we also compared both MRI methods with Doppler ultrasound (US) in the measurement of peak velocity, time to peak systolic velocity, and eject time. Bland-Altman analysis revealed that, when comparing peak velocities, the discrepancy between Doppler US and 4D spiral flow was significantly less than the discrepancy between Doppler and 4D Cartesian flow (2.75 cm/s vs. 10.25 cm/s), whereas the two MR methods were comparable (-5.75 s vs. -6 s) for time to peak. However, for the estimation of eject time, relative to Doppler US, the discrepancy for 4D conventional flow was smaller than that of 4D spiral flow (-16.25 s vs. -20 s). CONCLUSION: Relative to conventional 4D flow, 4D spiral flow achieves substantial reductions in both the TE and scan times; therefore, utility for it should be sought in a variety of in vivo and complex flow imaging applications.

Incorporating shape prior into active contours with a sparse linear combination of training shapes: application to corpus callosum segmentation.

In this paper, a novel method of embedding shape information into level set image segmentation is proposed. Our method is based on inferring shape variations by a sparse linear combination of instances in the shape repository. Given a sufficient number of training shapes with variations, a new shape can be approximated by a linear span of training shapes associated with those variations. At each step of curve evolution the curve is moved to minimize Chan-Vese energy functional as well as toward the best approximation based on a linear combination of training samples. Although the method is general, in this paper it has been applied to the problem of segmentation of corpus callosum from 2D sagittal MR images.

4D UTE flow: a phase-contrast MRI technique for assessment and visualization of stenotic flows.

PURPOSE: Inaccuracy of conventional four-dimensional (4D) flow MR imaging in the presence of random unsteady and turbulent blood flow distal to a narrowing has been an important challenge. Previous investigations have revealed that shorter echo times (TE) decrease the errors, leading to more accurate flow assessments. METHODS: In this study, as part of a 4D flow acquisition, an Ultra-Short TE (UTE) method was adopted. UTE works based on a center-out radial k-space trajectory that inherently has a short TE. By employing free induction decay sampling starting from read-out gradient ramp-up, and by combining the refocusing lobe of the slice select gradient with the bipolar flow encoding gradient, TEs of ≈1 msec may be achieved. RESULTS: Both steady and pulsatile flow regimes, and in each case a range of Reynolds numbers, were studied in an in-vitro model. Flow assessment at low and medium flow rates demonstrated a good agreement between 4D UTE and conventional 4D flow techniques. However, 4D UTE flow significantly outperformed conventional 4D flow, at high flow rates for both steady and pulsatile flow regimes. Feasibility of the method in one patient with Aortic Stenosis was also demonstrated. CONCLUSION: For both steady and pulsatile high flow rates, the measured flow distal to the stenotic narrowing using conventional 4D flow revealed more than 20% error compared to the ground-truth flow. This error was reduced to less than 5% using the 4D UTE flow technique.

Tissue Doppler imaging optical flow (TDIOF): a combined B-mode and tissue Doppler approach for cardiac motion estimation in echocardiographic images.

The quantitative analysis of cardiac motion from echocardiographic images helps clinicians in the diagnosis and therapy of patients suffering from heart disease. Quantitative analysis is usually based on tissue Doppler imaging (TDI) or speckle tracking. These methods are based on two techniques which to a large degree are independent: the Doppler phenomenon and image sequence processing. Herein, to increase the accuracy of the speckle tracking technique and to cope with the angle dependence of TDI, a combined approach dubbed tissue Doppler imaging optical flow (TDIOF) is proposed. TDIOF is formulated based on the combination of B-mode and Doppler energy terms minimized using algebraic equations and is validated on simulated images, and in vivo data. It was observed that the additional Doppler term is able to increase the accuracy of speckle tracking, compared to two popular motion estimation and speckle tracking techniques (Horn-Schunck and block matching methods). This observation was more pronounced when noise was present. The magnitude and angular error for TDIOF applied to simulated images, when comparing estimated motion with ground-truth motion, were 15% and 9.2°/frame, respectively. As an additional validation, echocardiography-derived strains were compared to tagged MRI-derived myocardial strains in the same subjects. The correlation coefficient (r) between the TDIOF-derived radial strains and tagged MRI-derived radial strains value was 0.83 (P < 0.001). The correlation coefficient ( r) for the TDIOF-derived circumferential strains compared to the tagged MRI-derived circumferential strains was 0.86 (P < 0.001). The comparison of TDIOF-derived and block matching speckle tracking and Horn-Schunck optical flow strain values using student t-test demonstrated superiority of TDIOF (95% confidence interval, P < 0.001).

In vitro validation of flow measurement with phase contrast MRI at 3 tesla using stereoscopic particle image velocimetry and stereoscopic particle image velocimetry-based computational fluid dynamics.

PURPOSE: To validate conventional phase-contrast MRI (PC-MRI) measurements of steady and pulsatile flows through stenotic phantoms with various degrees of narrowing at Reynolds numbers mimicking flows in the human iliac artery using stereoscopic particle image velocimetry (SPIV) as gold standard. MATERIALS AND METHODS: A series of detailed experiments are reported for validation of MR measurements of steady and pulsatile flows with SPIV and CFD on three different stenotic models with 50%, 74%, and 87% area occlusions at three sites: two diameters proximal to the stenosis, at the throat, and two diameters distal to the stenosis. RESULTS: Agreement between conventional spin-warp PC-MRI with Cartesian read-out and SPIV was demonstrated for both steady and pulsatile flows with mean Reynolds numbers of 130, 160, and 190 at the inlet by evaluating the linear regression between the two methods. The analysis revealed a correlation coefficient of > 0.99 and > 0.96 for steady and pulsatile flows, respectively. Additionally, it was found that the most accurate measures of flow by the sequence were at the throat of the stenosis (error < 5% for both steady and pulsatile mean flows). The flow rate error distal to the stenosis was primarily found to be a function of narrowing severity including dependence on proper Venc selection. CONCLUSION: SPIV and CFD provide excellent approaches to in vitro validation of new or existing PC-MRI flow measurement techniques.

Assessment of flow and hemodynamics in the carotid artery using a reduced TE 4D flow spiral phase-contrast MRI.

4D flow MRI is a powerful technique for quantitative flow assessment and visualization of complex flow patterns and hemodynamics of cardiovascular flows. This technique results in more anatomical information and comprehensive assessment of blood flow. However, conventional 4D PC MRI suffers from a few obstacles for clinical applications. The total scan time is long, especially in large volumes with high spatial resolutions. Inaccuracy of conventional Cartesian PC MRI in the setting of atherosclerosis and in general, disturbed and turbulent blood flow is another important challenge. This inaccuracy is the consequence of signal loss, intravoxel dephasing and flow-related artifact in the presence of disturbed and turbulent flow. Spiral k-space trajectory has valuable attributes which can help overcome some of the problems with 4D flow Cartesian acquisitions. Spiral trajectory benefits from shorter TE and reduces the flow-related artifacts. In addition, short spiral readouts with spiral interleaves can significantly reduce the total scan time, reducing the chances of patient motion which may also corrupt the data in the form of motion artifacts. In this paper, the accuracy of flow assessment and flow visualization with reduced TE 4D Spiral PC was investigated and good agreement was observed between the spiral and conventional technique. The systolic mean velocity, peak flow and the average flow in CCA and ICA of normal volunteers using 4D spiral PC MRI showed errors less than 10% compared to conventional 4D PC MRI. In addition, the scan time using spiral sequence was 3∶31 min which is half of the time using conventional sequence.

Analysis of 3D cardiac deformations with 3D SinMod.

In this paper, we propose a novel 3D sine wave modeling (3D SinMod) approach to automatic analysis of 3D cardiac deformations. An accelerated 3D complementary spatial modulation of magnetization (CSPAMM) tagging technique was used to modulate the myocardial tissue and to acquire 3D MR data sets of the whole-heart including three orthog- onal tags within three breath-holds. Each tag set is able to assess the motion along a direction perpendicular to the tag lines. With the application of CSPAMM, the effect of tag fading encountered in SPAMM tagging due to T1 relaxation is mitigated and tag deformations can be visualized for the entire cardiac cycle, including diastolic phases. In the proposed approach, the environment around each voxel in the 3D volume is modeled as a moving sine wavefront with local frequency and amplitude. The biggest advantage of the proposed technique is that the entire framework, from data acquisition to data analysis is in the 3D domain, which permits quantification of both the in-plane and through-plane motion components. The accuracy and the effectiveness of the proposed method has been validated using both simulated and in vivo tag data.

Cardiac motion and deformation recovery from MRI: a review.

Magnetic resonance imaging (MRI) is a highly advanced and sophisticated imaging modality for cardiac motion tracking and analysis, capable of providing 3D analysis of global and regional cardiac function with great accuracy and reproducibility. In the past few years, numerous efforts have been devoted to cardiac motion recovery and deformation analysis from MR image sequences. Many approaches have been proposed for tracking cardiac motion and for computing deformation parameters and mechanical properties of the heart from a variety of cardiac MR imaging techniques. In this paper, an updated and critical review of cardiac motion tracking methods including major references and those proposed in the past ten years is provided. The MR imaging and analysis techniques surveyed are based on cine MRI, tagged MRI, phase contrast MRI, DENSE, and SENC. This paper can serve as a tutorial for new researchers entering the field.

A novel phase-corrected 3D cine ultra-short te (UTE) phase-contrast MRI technique.

Phase-contrast (PC) MRI is a non-invasive technique to assess cardiovascular blood flow. However, this technique is not accurate for instance at the carotid bifurcation due to turbulent and disturbed blood flow in atherosclerotic disease. Flow quantification using conventional PC MRI distal to stenotic vessels suffers from intravoxel dephasing and flow artifacts. Previous studies have shown that short echo time (TE) potentially decreases the phase errors. In this work, a novel 3D cine UTE-PC imaging method is designed to measure the blood velocity in the carotid bifurcation using a UTE center-out radial trajectory and short TE time compared to standard PC MRI sequences. With a new phase error correction technique based on autocorrelation method, the proposed 3D cine UTE-PC has the potential to achieve high accuracy for quantification and visualization of velocity jet distal to a stenosis. Herein, we test the feasibility of the method in determining accurate flow waveforms in normal volunteers.

Validation of 3D ultra-short TE (UTE) Phase-Contrast MRI for imaging of steady flow: initial phantom experiments.

Assessment of blood flow is an important factor in diagnosis of cardiovascular disease. Vascular stenosis result in disturbed blood flow, flow recirculation, turbulence, and flow jet. These types of flows cause erroneous quantification of blood flow using conventional Phase contrast (PC) MRI techniques. Previous investigations have revealed that shorter Echo Times (TE) can decrease the quantification errors. In this paper, we performed phantom studies under steady flow to validate the UTE technique. Investigation of three different constant flow rates revealed a significant improvement in flow quantification and reduction of flow artifacts in comparison to Cartesian Phase-Contrast MRI.

A biventricular multimodal (MRI/ultrasound) cardiac phantom.

A cardiac phantom can be of crucial importance in the development and validation of ultrasound and cardiac magnetic resonance (MR) imaging and image analysis methods. A biventricular multimodal cardiac phantom has been manufactured in-house that can simulate normal and pathologic hearts with different degrees of infarction. The two-chamber structure can simulate the asymmetric left ventricular motion. Poly Vinyl Alcohol (PVA) is utilized as the basic material since it can simulate the shape, elasticity, and MR and ultrasound properties of the heart. The cardiac shape is simulated using a two-chamber acrylic mold. An additional pathologic heart phantom has been built to simulate aneurysm and infarction. Segmental dyskinesis is modeled based on three inclusions of different shapes and different degrees of elasticity. The cardiac elasticity is adjusted based on freeze-thaw cycles of the PVA cryogel for normal and scarred regions.