Diffusion-weighted magnetic resonance imaging (DW-MRI) is a powerful, non-invasive tool for detecting and characterizing abdominal lesions to facilitate early diagnosis, but respiratory motion during a scan reduces image quality and accuracy of quantitative biomarkers. Respiratory binning, which groups image slices into motion phase bins based on a navigator signal, can help mitigate motion artifacts. However, in DW-MRI, the standard binning technique often generates volumes with missing slices along the superior-inferior axis. Thus, longer scans are required to obtain volumes without gaps. In this study, we proposed a new binning technique to minimize missing slices without increasing scan time. We first designed an algorithm using dynamic programming and prefix sum approaches to optimize the initial binning of MR images. Then, we developed a probabilistic refinement phase, selecting some slices to belong in two neighboring bins to further reduce missing slices. We tested our two-phase technique on free-breathing abdominal DW-MRI scans from eight subjects, including one with tumors. The proposed technique significantly reduced missing slices compared to standard binning (p<1.0*10-15), yielding an average reduction of 81.74+/-7.58 also reduced motion artifacts, improving the conspicuity of malignant lesions. Apparent Diffusion Coefficient (ADC) maps generated from free-breathing scans corrected using the proposed technique had lower intra-subject variability compared to ADC maps from uncorrected free-breathing and shallow-breathing scans (p<0.001). Additionally, ADC maps from shallow-breathing scans were more consistent with corrected free-breathing maps rather than uncorrected free-breathing maps (p<0.01). The proposed technique corrects for motion while simultaneously reducing missing slices, allowing for shorter acquisition times compared to standard binning.

Accurately measuring renal function is crucial for pediatric patients with kidney conditions. Traditional methods have limitations, but dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) provides a safe and efficient approach for detailed anatomical evaluation and renal function assessment. However, motion artifacts during DCE-MRI can degrade image quality and introduce misalignments, leading to unreliable results. This study introduces a motion-compensated reconstruction technique for DCE-MRI data acquired using golden-angle radial sampling. Our proposed method achieves three key objectives: (1) identifying and removing corrupted data (outliers) using a Gaussian process model fitting with a k -space center navigator, (2) efficiently clustering the data into motion phases and performing interphase registration, and (3) utilizing a novel formulation of motion-compensated radial reconstruction. We applied the proposed motion correction (MoCo) method to DCE-MRI data affected by varying degrees of motion, including both respiratory and bulk motion. We compared the outcomes with those obtained from the conventional radial reconstruction. Our evaluation encompassed assessing the quality of images, concentration curves, and tracer kinetic model fitting, and estimating renal function. The proposed MoCo reconstruction improved the temporal signal-to-noise ratio for all subjects, with a 21.8% increase on average, while total variation values of the aorta, right, and left kidney concentration were improved for each subject, with 32.5%, 41.3%, and 42.9% increases on average, respectively. Furthermore, evaluation of tracer kinetic model fitting indicated that the median standard deviation of the estimated filtration rate ( σ F T ), mean normalized root-mean-squared error (nRMSE), and chi-square goodness-of-fit of tracer kinetic model fit were decreased from 0.10 to 0.04, 0.27 to 0.24, and, 0.43 to 0.27, respectively. The proposed MoCo technique enabled more reliable renal function assessment and improved image quality for detailed anatomical evaluation in the case of bulk and respiratory motion during the acquisition of DCE-MRI.

Radial DCE-MRI is robust to motion. However, bulk motion or heavy breathing causes 1) irrecoverably deteriorated k-space lines acquired during motion events reducing image quality, 2) misaligned volumes in a dynamic sequence. In this work we propose to solve the first problem by fitting a Gaussian process to the k-space center of each spoke over time and using it to determine outlier spokes corrupted by motion. We solve the second problem by clustering the dynamic data to respective motionless phases before and after each motion event and registering volumes between phases for computationally efficient correction of motion with fewer registrations.

Purpose Abdominal MRI scans may require breath-holding to prevent image quality degradation, which can be challenging for patients, especially children. In this study, we evaluate whether FID navigators can be used to measure and correct for motion prospectively, in real-time. Methods FID navigators were inserted into a 3D radial sequence with stack-of-stars sampling. MRI experiments were conducted on 6 healthy volunteers. A calibration scan was first acquired to create a linear motion model that estimates the kidney displacement due to respiration from the FID navigator signal. This model was then applied to predict and prospectively correct for motion in real time during deep and continuous deep breathing scans. Resultant images acquired with the proposed technique were compared with those acquired without motion correction. Dice scores were calculated between inhale/exhale motion states. Furthermore, images acquired using the proposed technique were compared with images from extra-dimensional golden-angle radial sparse parallel, a retrospective motion state binning technique. Results Images reconstructed for each motion state show that the kidneys' position could be accurately tracked and corrected with the proposed method. The mean of Dice scores computed between the motion states were improved from 0.93 to 0.96 using the proposed technique. Depiction of the kidneys was improved in the combined images of all motion states. Comparing results of the proposed technique and extra-dimensional golden-angle radial sparse parallel, high-quality images can be reconstructed from a fraction of spokes using the proposed method. Conclusion The proposed technique reduces blurriness and motion artifacts in kidney imaging by prospectively correcting their position both in-plane and through-slice.

Kidney DCE-MRI aims at both qualitative assessment of kidney anatomy and quantitative assessment of kidney function by estimating the tracer kinetic (TK) model parameters. Accurate estimation of TK model parameters requires an accurate measurement of the arterial input function (AIF) with high temporal resolution. Accelerated imaging is used to achieve high temporal resolution, which yields under-sampling artifacts in the reconstructed images. Compressed sensing (CS) methods offer a variety of reconstruction options. Most commonly, sparsity of temporal differences is encouraged for regularization to reduce artifacts. Increasing regularization in CS methods removes the ambient artifacts but also over-smooths the signal temporally which reduces the parameter estimation accuracy. In this work, we propose a single image trained deep neural network to reduce MRI under-sampling artifacts without reducing the accuracy of functional imaging markers. Instead of regularizing with a penalty term in optimization, we promote regularization by generating images from a lower dimensional representation. In this manuscript we motivate and explain the lower dimensional input design. We compare our approach to CS reconstructions with multiple regularization weights. Proposed approach results in kidney biomarkers that are highly correlated with the ground truth markers estimated using the CS reconstruction which was optimized for functional analysis. At the same time, the proposed approach reduces the artifacts in the reconstructed images.

In tomoelastography, to achieve a final wave speed map by combining reconstructions obtained from all spatial directions and excitation frequencies, the use of weights is inevitable. Here, a new weighting scheme, which maximizes the signal-to-noise ratio (SNR) of the final wave speed map, has been proposed. To maximize the SNR of the final wave speed map, the use of squares of estimated SNR values of reconstructed individual maps has been proposed. Therefore, derivations of the SNR of the reconstructed wave speed maps have become necessary. Considering the noise on the complex MRI signal, the SNR of the reconstructed wave speed map was formulated by an analytical approach assuming a high SNR, and the results were verified using Monte Carlo simulations (MCSs). It has been assumed that the noise remains approximately Gaussian when the image SNR is high enough, despite the nonlinear operations in tomoelastography inversion. Hence, the SNR threshold was determined by comparing the SNR computed by MCSs and analytical approximations. The weighting scheme was evaluated for accuracy, spatial resolution and SNR performances on simulated phantoms. MR elastography (MRE) experiments on two different phantoms were conducted. Wave speed maps were generated for simulated 3D human abdomen MRE data and experimental human abdomen MRE data. The simulation results demonstrated that the SNR-weighted inversion improved the SNR performance of the wave speed map by a factor of two compared to the performance of the original (i.e., amplitude-weighted) reconstruction. In the case of a low SNR, no bias occurred in the wave speed map when SNR weighting was used, whereas 10% bias occurred when the original weighting (i.e., amplitude weighting) was used. Thus, while not altering the accuracy or spatial resolution of the wave speed map with the proposed weighting method, the SNR of the wave speed map has been significantly improved.

MRI phase contrast imaging methods that assemble slice-wise acquisitions into volumes can contain interslice phase discontinuities (IPDs) over the course of the scan from sources, including unavoidable physiological activity. In magnetic resonance elastography (MRE), this can alter wavelength and tissue stiffness estimates, invalidating the analysis. We first model this behavior as jitter along the z-axis of the phase of 3D complex-valued wave volumes. A two-step image processing pipeline is then proposed that removes IPDs. First, constant slicewise phase shift is removed with a novel, non-convex dejittering algorithm. Then, regional physiological noise artifacts are removed with novel filtering of 3D wavelet coefficients. Calibration of two pipeline coefficients, the dejitter parameter $\alpha $ and the wavelet band high-pass coefficient $\omega _{c}$ , was first performed on a finite-element method brain phantom. A comparative investigation was then performed, on a cohort of 48 brain acquisitions, of four approaches to IPDs: 1) the proposed method; 2) a “control” condition of neglect of IPDs; 3) an anisotropic wavelet-based method; and 4) a method of in-plane (2D) processing. The present method showed medians of $\lvert {G}^{*} \rvert = \textsf {1873}$ Pa for a multifrequency wave inversion centered at 40 Hz which was within 6% of methods 3) and 4), while neglect produced $\lvert {G}^{*} \rvert $ estimates a mean of 17% lower. The proposed method reduced the value range of the cohort against methods 3) and 4) by 29% and 31%, respectively. Such reduction in variance enhances the ability of brain MRE to predict subtler physiological changes. Our theoretical approach further enables more powerful applications of fundamental findings in noise and denoising to MRE.

A new viscoelastic wave inversion method for MRE, called Heterogeneous Multifrequency Direct Inversion (HMDI), was developed which accommodates heterogeneous elasticity within a direct inversion (DI) by incorporating first-order gradients and combining results from a narrow band of multiple frequencies. The method is compared with a Helmholtz-type DI, Multifrequency Dual Elasto-Visco inversion (MDEV), both on ground-truth Finite Element Method simulations at varied noise levels and a prospective in vivo brain cohort of 48 subjects ages 18-65. In simulated data, MDEV recovered background material within 5% and HMDI within 1% of prescribed up to SNR of 20 dB. In vivo HMDI and MDEV were then combined with segmentation from SPM to create a fully automated "brain palpation" exam for both whole brain (WB), and brain white matter (WM), measuring two parameters, the complex modulus magnitude vertical bar G*vertical bar, which measures tissue "stiffness", and the slope of vertical bar G vertical bar values across frequencies, a measure of viscous dispersion. vertical bar G*vertical bar values for MDEV and HMDI were comparable to the literature (for a 3-frequency set centered at 50 Hz, WB means were 2.17 and 2.15 kPa respectively, and WM means were 2.47 and 2.49 kPa respectively). Both methods showed moderate correlation to age in both WB and WM, for both vertical bar G*vertical bar and vertical bar G*vertical bar slope, with Pearson's r >= 0.4 in the most sensitive frequency sets. In comparison to MDEV, HMDI showed better preservation of recovered target shapes, more noise-robustness, and stabler recovery values in regions with rapid property change, however summary statistics for both methods were quite similar. By eliminating homogeneity assumptions within a fast, fully automatic, regularization-free direct inversion, HMDI appears to be a worthwhile addition to the MRE image reconstruction repertoire. In addition to supporting the literature showing decrease in brain viscoelasticity with age, our work supports a wide range of inter-individual variation in brain MRE results. (C) 2018 Elsevier B.V. All rights reserved.

In high intensity focused ultrasound (HIFU) the choice of transducer frequency depends on the target depth and tissue type. At high frequencies attenuation does not permit enough acoustical power to be transmitted to the target whereas at low frequencies the transmitted power is not absorbed efficiently. Hence, there exists an optimum frequency at which the power deposited at the target is maximum. In this study, we verified this relation experimentally using MR compatible focused transducers, ex-vivo tissue samples and magnetic resonance (MR) thermometry.

(c) (d) Figure 2: Simulation results for displacement versus frequency swept and eigenfrequencies for different motions (a) nodding, (b) naying, (c) Indian head bobble, (d) human experiment results with bite actuator for normalized displacement versus frequency swept. Frequency domain analysis in normal brain (red), 10% reduced Young’s modulus (green), eigenfrequency (black), human experiment (blue). 0 50 100 0 100 200

Respiratory motion substantially affects the accuracy of quantitative DWI-MR techniques in the upper abdomen. A conventional approach for motion correction in abdominal MRI uses a respiratory belt or other navigators for prospective triggering. This study explores use of a new approach - PilotTone (PT) navigator for binning.

MRI literature shows that it is possible to increase image reconstruction quality by removing coils that cause majority of the streaking artifacts. We introduce to measure the coil quality based on mutual information between a reference and each coil image. Specifically we apply this technique to DCE-MRI reconstruction from under-sampled radial stack of stars trajectory for kidney imaging and calculate mutual dependence between a dynamic image from each coil and a reference image to assess coil contribution to reconstruction. Experiments show mutual dependence based coil selection reduces artifacts and increases reconstructed image SNR by $$$16.84\%$$$ using $$$1/3$$$ of the coils.

Abdominal MRI scans often require breath-holding to prevent image quality degradation, which can be challenging for patients. XD-GRASP enables generation of motion-robust images for free-breathing abdominal MRI by binning the data into respiratory phases. In this study, we compared three navigation techniques, namely k-space center, free induction decay navigators (FIDnavs) and pilot tone (PT), for XD-GRASP reconstruction. FIDnavs and PT have advantages over k-space center navigation since they are insensitive to gradient delays, independent of the imaging plane and acquired more frequently. The image quality ranking means (1=best,2=moderate,3=worst) were 1.4, 1.6 and 2 for FIDnavs, PT and k-space center, respectively.

Dynamic contrast enhanced MRI (DCE-MRI) is capable of quantitative assessment of renal function and evaluation of the detailed anatomy of the urinary tract and renal arteries in patients. To reconstruct the dynamic volumes in DCE-MRI, Golden-angle RAdial Sparse Parallel (GRASP) reconstruction algorithm is commonly used. In this software, we have developed an efficient open-source, purely Python-based, standalone GRASP reconstruction library called pyGRASP that allows researchers to access the source code for development, facilitating flexible deployment with readable code and no compilation, and easy utilization without requirement of a steep learning curve.

Voxel misalignment due to unavoidable respiratory motion and bulk motion introduce large errors in DW-MRI quantitative parameter fitting. Apparent diffusion coefficient (ADC) is an effective tool for characterization of malignant tumors. In this study we evaluate the use of a motion correction method for DW-MRI imaging based on 3D slice level motion tracking using a rigid slice to volume registration and Kalman filtering. We show improvement in robustness of parameter estimation and reduction of blurring in b-value images for assessment of pediatric hepatoblastoma lesions.

Radial acquisitions are inherently motion-robust and facilitate self-navigation, however the frequency of motion updates from navigator images is limited. Pilot tone (PT) enables continuous motion sensing, but estimation of quantitative motion parameters requires a subject-specific calibration. In this work, we propose (i) using PT motion detection to guide navigator-based motion estimation from a 3D radial acquisition and (ii) using these measurements to calibrate a PT motion model in order to provide high temporal resolution quantitative motion tracking. This hybrid approach demonstrates improved retrospective correction results with reduced blurring and facilitates PT motion tracking for subsequent scans.

Diffusion-weighted MRI (DW-MRI) is capable of detecting and characterizing liver tumors and following-up treatments. Unfortunately, respiratory motion during DW-MRI scan causes misalignments between slices and reduces image quality. 3D slice-to-volume registration (SVR) can be employed to correct for motion. However, motion estimates may be inaccurate for high b-value images where SNR decreases. In this work, we propose to use PT estimated motion correction, which is calibrated on 3D SVR motion parameters obtained from low b-value images. We showed misalignments between slices are reduced by the proposed PT-based motion correction compared to SVR-based motion correction and no correction.

In MR enterography, diffusion-weighted MRI (DW-MRI) is commonly used to evaluate Crohn’s disease (CD). Echo-planar imaging (EPI) used in DW-MRI suffers from geometric distortion due to time-varying susceptibility field changes in the bowel. Hence, we implemented a DW-MRI sequence using a dual-echo EPI with opposing encoding polarities. Thus, the distortion field can be estimated dynamically and corrected retrospectively. The proposed method was evaluated in DW-MRI data acquired on suspected CD patients by visually and quantitatively assessing distortion corrected volumes based on anatomical similarity with T2-HASTE and uncertainty of estimated diffusion parameters of the intravoxel incoherent motion model.

Motivation: Addressing the challenge of respiratory motion in abdominal DCE-MRI, especially in pediatric patients, to improve image quality and enhance quantitative DCE-MRI analysis. Goal(s): To develop a motion correction method using PilotTone navigators (PTnavs) to enhance DCE-MRI quality and reliability. Approach: We extract PTnav, create a linear motion model using binning based reference motion parameters. We then apply the motion model to the PTnav for each spoke to estimate its motion and correct for it. We evaluate the method on non-contrast volunteer and pediatric DCE-MRI data. Results: Successful elimination of motion artifacts and improved image quality, reduced image alignment and improved signal-time-intensity curves. Impact: The proposed PT-based motion correction effectively overcomes the challenges of previous motion correction methods, eliminating respiratory motion artifacts and enhancing image quality and misalignment in high-temporal-resolution DCE-MRI. This advancement improves diagnostic accuracy, particularly in pediatric cases with unpredictable breathing patterns.

Diffusion-weighted MRI is increasingly used for detection and characterization of Crohn’s disease. However, unavoidable respiratory motion and bowel motility reduces accuracy and precision of quantitative parameter fitting, which hinders clinical applicability DW-MRI. We use a 3D slice-to-volume registration approach that sequentially tracks rigid motion parameters for each slice and regularises the parameters with a Kalman filter in the order of acquisition of each slice. We assess the quality of images and estimated parameter maps and the precision of IVIM parameters in the areas of disease using the proposed motion correction technique, and compare them with results from the uncorrected data.