Trzasko, Joshua D.

Purpose Noninvasive stiffness imaging techniques (elastography) can image myocardial tissue biomechanics in vivo. For cardiac MR elastography (MRE) techniques, the optimal vibration frequency for in vivo experiments is unknown. Furthermore, the accuracy of cardiac MRE has never been evaluated in a geometrically accurate phantom. Therefore, the purpose of this study was to determine the necessary driving frequency to obtain accurate three‐dimensional (3D) cardiac MRE stiffness estimates in a geometrically accurate diastolic cardiac phantom and to determine the optimal vibration frequency that can be introduced in healthy volunteers. Methods The 3D cardiac MRE was performed on eight healthy volunteers using 80 Hz, 100 Hz, 140 Hz, 180 Hz, and 220 Hz vibration frequencies. These frequencies were tested in a geometrically accurate diastolic heart phantom and compared with dynamic mechanical analysis (DMA). Results The 3D Cardiac MRE was shown to be feasible in volunteers at frequencies as high as 180 Hz. MRE and DMA agreed within 5% at frequencies greater than 180 Hz in the cardiac phantom. However, octahedral shear strain signal to noise ratios and myocardial coverage was shown to be highest at a frequency of 140 Hz across all subjects. Conclusion This study motivates future evaluation of high‐frequency 3D MRE in patient populations. Magn Reson Med 77:351–360, 2017. © 2016 Wiley Periodicals, Inc.

PurposeTo describe how integrated gradient nonlinearity (GNL) correction can be used within noniterative partial Fourier (homodyne) and parallel (SENSE and GRAPPA) MR image reconstruction strategies, and demonstrate that performing GNL correction during, rather than after, these routines mitigates the image blurring and resolution loss caused by postreconstruction image domain based GNL correction.MethodsStarting from partial Fourier and parallel magnetic resonance imaging signal models that explicitly account for GNL, noniterative image reconstruction strategies for each accelerated acquisition technique are derived under the same core mathematical assumptions as their standard counterparts. A series of phantom and in vivo experiments on retrospectively undersampled data were performed to investigate the spatial resolution benefit of integrated GNL correction over conventional postreconstruction correction.ResultsPhantom and in vivo results demonstrate that the integrated GNL correction reduces the image blurring introduced by the conventional GNL correction, while still correcting GNL‐induced coarse‐scale geometrical distortion. Images generated from undersampled data using the proposed integrated GNL strategies offer superior depiction of fine image detail, for example, phantom resolution inserts and anatomical tissue boundaries.ConclusionNoniterative partial Fourier and parallel imaging reconstruction methods with integrated GNL correction reduce the resolution loss that occurs during conventional postreconstruction GNL correction while preserving the computational efficiency of standard reconstruction techniques. Magn Reson Med 75:2534–2544, 2016. © 2015 Wiley Periodicals, Inc.

PurposeTo describe a model‐based reconstruction strategy for routine magnetic resonance imaging that accounts for gradient nonlinearity (GNL) during rather than after transformation to the image domain, and demonstrate that this approach reduces the spatial resolution loss that occurs during strictly image‐domain GNL‐correction.MethodsAfter reviewing conventional GNL‐correction methods, we propose a generic signal model for GNL‐affected magnetic resonance imaging acquisitions, discuss how it incorporates into contemporary image reconstruction platforms, and describe efficient nonuniform fast Fourier transform‐based computational routines for these. The impact of GNL‐correction on spatial resolution by the conventional and proposed approaches is investigated on phantom data acquired at varying offsets from gradient isocenter, as well as on fully sampled and (retrospectively) undersampled in vivo acquisitions.ResultsPhantom results demonstrate that resolution loss that occurs during GNL‐correction is significantly less for the proposed strategy than for the standard approach at distances >10 cm from isocenter with a 35 cm field‐of‐view gradient coil. The in vivo results suggest that the proposed strategy better preserves fine anatomical detail than retrospective GNL‐correction while offering comparable geometric correction.ConclusionAccounting for GNL during image reconstruction allows geometric distortion to be corrected with less spatial resolution loss than is typically observed with the conventional image domain correction strategy. Magn Reson Med 74:1019–1031, 2015. © 2014 Wiley Periodicals, Inc.

PurposeThe purpose of this work is to compare the behavior of the signal‐to‐noise ratio (SNR) and contrast‐to‐noise ratio (CNR) in contrast‐enhanced MR angiography with background suppression performed by either a Dixon‐type or subtraction‐type method.Theory and MethodsTheoretical expressions for the SNR and CNR for both background suppression techniques were derived. The theoretical Dixon:subtraction SNR and CNR ratios were compared to empirical ratios measured from phantom and in vivo studies for Dixon techniques utilizing one, two, and three echoes. Specifically, the SNR and CNR ratios were compared as the concentration of contrast material in the blood changed.ResultsEmpirical measurements of the SNR and CNR ratios compared favorably with the ratios predicted by theory. As the contrast concentration was reduced, the SNR advantage of the Dixon techniques increased asymptotically. In the ideal case, the SNR improvement over subtraction contrast‐enhanced MR angiography was at least twofold for one‐ and two‐echo Dixon techniques and at least a factor of for the three‐echo Dixon technique.ConclusionExpressions showing a contrast concentration‐dependent SNR and CNR improvement of at least a factor of two when Dixon‐type contrast‐enhanced MR angiography is used in place of subtraction‐type contrast‐enhanced MR angiography were derived and validated with phantom and in vivo experiments. Magn Reson Med 74:81–92, 2015. © 2014 Wiley Periodicals, Inc.

Compressive sensing (CS) describes how sparse signals can be accurately reconstructed from many fewer samples than required by the Nyquist criterion. Since MRI scan duration is proportional to the number of acquired samples, CS has been gaining significant attention in MRI. However, the computationally intensive nature of CS reconstructions has precluded their use in routine clinical practice. In this work, we investigate how different throughput-oriented architectures can benefit one CS algorithm and what levels of acceleration are feasible on different modern platforms. We demonstrate that a CUDA-based code running on an NVIDIA Tesla C2050 GPU can reconstruct a 256 × 160 × 80 volume from an 8-channel acquisition in 19 seconds, which is in itself a significant improvement over the state of the art. We then show that Intel's Knights Ferry can perform the same 3D MRI reconstruction in only 12 seconds, bringing CS methods even closer to clinical viability.