Constrained alternating minimization for parameter mapping (CAMP).

OBJECTIVE: To improve image quality in highly accelerated parameter mapping by incorporating a linear constraint that relates consecutive images. APPROACH: In multi-echo T1 or T2 mapping, scan time is often shortened by acquiring undersampled but complementary measures of k-space at each TE or TI. However, residual undersampling artifacts from the individual images can then degrade the quality of the final parameter maps. In this work, a new reconstruction method, dubbed Constrained Alternating Minimization for Parameter mapping (CAMP), is introduced. This method simultaneously extracts T2 or T1* maps in addition to an image for each TE or TI from accelerated datasets, leveraging the constraints of the decay to improve the reconstructed image quality. The model enforces exponential decay through a linear constraint, resulting in a biconvex objective function that lends itself to alternating minimization. The method was tested in four in vivo volunteer experiments and validated in phantom studies and healthy subjects, using T2 and T1 mapping, with accelerations of up to 12. MAIN RESULTS: CAMP is demonstrated for accelerated radial and Cartesian acquisitions in T2 and T1 mapping. The method is even applied to generate an entire T2 weighted image series from a single TSE dataset, despite the blockwise k-space sampling at each echo time. Experimental undersampled phantom and in vivo results processed with CAMP exhibit reduced artifacts without introducing bias. SIGNIFICANCE: For a wide array of applications, CAMP linearizes the model cost function without sacrificing model accuracy so that the well-conditioned and highly efficient reconstruction algorithm improves the image quality of accelerated parameter maps.

Clinical physiology: the crucial role of MRI in evaluation of peripheral artery disease.

Peripheral artery disease (PAD) is a common vascular disease that primarily affects the lower limbs and is defined by the constriction or blockage of peripheral arteries and may involve microvascular dysfunction and tissue injury. Patients with diabetes have more prominent disease of microcirculation and develop peripheral neuropathy, autonomic dysfunction, and medial vascular calcification. Early and accurate diagnosis of PAD and disease characterization are essential for personalized management and therapy planning. Magnetic resonance imaging (MRI) provides excellent soft tissue contrast and multiplanar imaging capabilities and is useful as a noninvasive imaging tool in the comprehensive physiological assessment of PAD. This review provides an overview of the current state of the art of MRI in the evaluation and characterization of PAD, including an analysis of the many applicable MR imaging techniques, describing the advantages and disadvantages of each approach. We also present recent developments, future clinical applications, and future MRI directions in assessing PAD. The development of new MR imaging technologies and applications in preclinical models with translation to clinical research holds considerable potential for improving the understanding of the pathophysiology of PAD and clinical applications for improving diagnostic precision, risk stratification, and treatment outcomes in patients with PAD.

A Physics-Based Algorithm to Universally Standardize Routinely Obtained Clinical T2-Weighted Images.

RATIONALE AND OBJECTIVES: MR images can be challenging for machine learning and other large-scale analyses because most clinical images, for example, T2-weighted (T2w) images, reflect not only the biologically relevant T2 of tissue but also hardware and acquisition parameters that vary from site to site. Quantitative T2 mapping avoids these confounds because it quantitatively isolates the biological parameter of interest, thus representing a universal standardization across sites. However, efforts to incorporate quantitative mapping sequences into routine clinical practice have seen slow adoption. Here we show, for the first time, that the routine T2w complex raw dataset can be successfully regarded as a quantitative mapping sequence that can be reconstructed with classical optimization methods and physics-based constraints. MATERIALS AND METHODS: While previous constrained reconstruction methods are unable to reconstruct a T2 map based on this data, the expanding-constrained alternating minimization for parameter mapping (e-CAMP), which employs stepwise initialization, a linearized version of the exponential model and a phase conjugacy constraint, is demonstrated to provide useful quantitative maps directly from a vendor T2w single image data. RESULTS: This paper introduces the method and demonstrates its performance using simulations, retrospectively undersampled brain images, and prospectively acquired T2w images taken on both phantom and brain. CONCLUSION: Because T2w scans are included in nearly every protocol, this approach could open the door to creating large, standardized datasets without requiring widespread changes in clinical protocols.

3D FRONSAC with PSF reconstruction.

PURPOSE: This study extends the Fast ROtary Nonlinear Spatial ACquisition (FRONSAC) method to include 3D acquisitions and reconstructions. It uses a transform domain reconstruction which is needed to make 3D reconstructions practical and provides new insights into how parallel imaging performance is enhanced by FRONSAC encoding. METHODS: This work developed the first examples of FRONSAC incorporated into a 3D acquisition. 3D FRONSAC was tested on human subjects with both simple gradient echo and MPRAGE Cartesian acquisitions. The quality of the 3D FRONSAC images was evaluated using structural similarity index measure (SSIM), and normalized root mean square error (NRMSE). RESULTS: FRONSAC encoding did not significantly modify the contrast obtained in either sequence, but it substantially improves the image quality of undersampled reconstruction. FRONSAC images have reduced undersampling ghosts and consistently improved SSIM and NRMSE. CONCLUSIONS: Acquisition and reconstruction of 3D FRONSAC images are feasible, and the additional FRONSAC encoding improves image quality in highly undersampled images.

Validation of Muscle Fiber Architecture of the Human Tongue Revealed by Diffusion Magnetic Resonance Imaging With Histology Verification.

PURPOSE: The goal of this study is to validate the muscle architecture derived from both ex vivo and in vivo diffusion-weighted magnetic resonance imaging (dMRI) of the human tongue with histology of an ex vivo tongue. METHOD: dMRI was acquired with a 200-direction high angular resolution diffusion imaging (HARDI) diffusion scheme for both a postmortem head (imaged within 48 hr after death) and a healthy volunteer. After MRI, the postmortem head was fixed and the tongue excised for hematoxylin and eosin (H&E) staining and histology imaging. Structure tensor images were generated from the stained images to better demonstrate muscle fiber orientations. The tongue muscle fiber orientations, estimated from dMRI, were visualized using the tractogram, a novel representation of crossing fiber orientations, and compared against the histology images of the ex vivo tongue. RESULTS: Muscle fibers identified in the tractograms showed good correspondence with those appearing in the histology images. We further demonstrated tongue muscle architecture in in vivo tractograms for the entire tongue. CONCLUSION: The study demonstrates that dMRI can accurately reveal the complex muscle architecture of the human tongue and may potentially benefit planning and evaluation of oral surgery and research on speech and swallowing.

Structural connectivity mapping in human hippocampal-subfields using super-resolution hybrid diffusion imaging: a feasibility study.

PURPOSE: The goal of the current study was to introduce a new methodology that holds a promise to be used in hippocampus-aging studies using sub-millimeter super-resolution hybrid diffusion imaging (HYDI) MRI. METHODS: HYDI diffusion data were acquired in two groups of older and younger healthy participants recruited from the Indiana Alzheimer's Disease Research Center and community. These data were then transformed into super-resolution diffusion images before the hippocampal subfield analyses. We studied the correlation between the subjects' age and the structural connectivity involving the hippocampal subfields and the connectivity between the whole hippocampus and the cerebral cortex. RESULTS: Structural integrity derived from the tractography streamlines between the hippocampal subfields was reduced in older than younger adults. CONCLUSION: The findings offered a new promising framework, and they opened avenues for future studies to explore the relationship between the structural connectivity in the hippocampal area and different types of dementia.

Prospective motion detection and re-acquisition in diffusion MRI using a phase image-based method-Application to brain and tongue imaging.

PURPOSE: To develop an image-based motion-robust diffusion MRI (dMRI) acquisition framework that is able to minimize motion artifacts caused by rigid and nonrigid motion, applicable to both brain and tongue dMRI. METHODS: We developed a novel prospective motion-correction technique in dMRI using a phase image-based real-time motion-detection method (PITA-MDD) with re-acquisition of motion-corrupted images. The prospective PITA-MDD acquisition technique was tested in the brains and tongues of volunteers. The subjects were instructed to move their heads or swallow, to induce motion. Motion-detection efficacy was validated against visual inspection as the gold standard. The effect of the PITA-MDD technique on diffusion-parameter estimates was evaluated by comparing reconstructed fiber tracts using tractography with and without re-acquisition. RESULTS: The prospective PITA-MDD technique was able to effectively and accurately detect motion-corrupted data as compared with visual inspection. Tractography results demonstrated that PITA-MDD motion detection followed by re-acquisition helps in recovering lost and misshaped fiber tracts in the brain and tongue that would otherwise be corrupted by motion and yield erroneous estimates of the diffusion tensor. CONCLUSION: A prospective PITA-MDD technique was developed for dMRI acquisition, providing improved dMRI image quality and motion-robust diffusion estimation of the brain and tongue.

Resting state functional MRI in infants with prenatal opioid exposure-a pilot study.

PURPOSE: Exposure to prenatal opioids may adversely impact the developing brain networks. The aim of this pilot study was to evaluate alterations in amygdalar functional connectivity in human infants with prenatal opioid exposure. METHODS: In this prospective IRB approved study, we performed resting state functional MRI (rs-fMRI) in 10 infants with prenatal opioid exposure and 12 infants without prenatal drug exposure at < 48 weeks corrected gestational age. Following standard preprocessing, we performed seed-based functional connectivity analysis with the right and left amygdala as the regions of interest after correcting for maternal depression and infant sex. We compared functional connectivity of the amygdala network between infants with and without prenatal opioid exposure. RESULTS: There were significant differences in connectivity of the amygdala seed regions to the several cortical regions including the medial prefrontal cortex in infants who had prenatal opioid exposure when compared with opioid naïve infants. CONCLUSION: This finding of increased amygdala functional connectivity in infants with in utero opioid exposure suggests a potential role of maternal opioid exposure on infants' altered amygdala function. This association with prenatal exposure needs to be replicated in future larger studies.

Longitudinal white-matter abnormalities in sports-related concussion: A diffusion MRI study.

OBJECTIVE: To study longitudinal recovery trajectories of white matter after sports-related concussion (SRC) by performing diffusion tensor imaging (DTI) on collegiate athletes who sustained SRC. METHODS: Collegiate athletes (n = 219, 82 concussed athletes, 68 contact-sport controls, and 69 non-contact-sport controls) were included from the Concussion Assessment, Research and Education Consortium. The participants completed clinical assessments and DTI at 4 time points: 24 to 48 hours after injury, asymptomatic state, 7 days after return-to-play, and 6 months after injury. Tract-based spatial statistics was used to investigate group differences in DTI metrics and to identify white-matter areas with persistent abnormalities. Generalized linear mixed models were used to study longitudinal changes and associations between outcome measures and DTI metrics. Cox proportional hazards model was used to study effects of white-matter abnormalities on recovery time. RESULTS: In the white matter of concussed athletes, DTI-derived mean diffusivity was significantly higher than in the controls at 24 to 48 hours after injury and beyond the point when the concussed athletes became asymptomatic. While the extent of affected white matter decreased over time, part of the corpus callosum had persistent group differences across all the time points. Furthermore, greater elevation of mean diffusivity at acute concussion was associated with worse clinical outcome measures (i.e., Brief Symptom Inventory scores and symptom severity scores) and prolonged recovery time. No significant differences in DTI metrics were observed between the contact-sport and non-contact-sport controls. CONCLUSIONS: Changes in white matter were evident after SRC at 6 months after injury but were not observed in contact-sport exposure. Furthermore, the persistent white-matter abnormalities were associated with clinical outcomes and delayed recovery time.

Phase Image Texture Analysis for Motion Detection in Diffusion MRI (PITA-MDD).

PURPOSE: Pronounced spin phase artifacts appear in diffusion-weighted imaging (DWI) with only minor subject motion. While DWI data corruption is often identified as signal drop out in diffusion-weighted (DW) magnitude images, DW phase images may have higher sensitivity for detecting subtle subject motion. METHODS: This article describes a novel method to return a metric of subject motion, computed using an image texture analysis of the DW phase image. This Phase Image Texture Analysis for Motion Detection in dMRI (PITA-MDD) method is computationally fast and reliably detects subject motion from diffusion-weighted images. A threshold of the motion metric was identified to remove motion-corrupted slices, and the effect of removing corrupted slices was assessed on the reconstructed FA maps and fiber tracts. RESULTS: Using a motion-metric threshold to remove the motion-corrupted slices results in superior fiber tracts and fractional anisotropy maps. When further compared to a state-of-the-art magnitude-based motion correction method, PITA-MDD was able to detect comparable corrupted slices in a more computationally efficient manner. CONCLUSION: In this study, we evaluated the use of DW phase images to detect motion corruption. The proposed method can be a robust and fast alternative for automatic motion detection in the brain with multiple applications to inform prospective motion correction or as real-time feedback for data quality control during scanning, as well as after data is already acquired.

Super-Resolution Diffusion Tensor Imaging using SRCNN: A Feasibility Study.

High-resolution diffusion imaging with submillimeter isotropic voxels requires long scan times that are usually clinically impractical. Even with those long scans, the image quality can still suffer from low signal-to-noise ratio (SNR) and severe geometric distortion due to long echo spacing in echo-planar imaging sequences. In this study, we proposed and validated the efficacy of using a state-of-the-art deep-learning method, super-resolution convolutional neural network (SRCNN), to achieve submillimeter super-resolution diffusion-weighted (DW) images. The 2D-based deep-learning method was validated by comparing with the ground truth using numerical simulations and by studying region-of-interest (ROI) using real human data of three healthy volunteers. Furthermore, we interrogated the proposed method under different real-life SNR conditions. The results demonstrated that the proposed deep-learning method was able to reproduce sufficient details in the anatomy that can only be detected using high-resolution diffusion imaging. The percentage errors in diffusion tensor imaging (DTI) derived metrics were less than 8% when the baseline SNR larger than 20. The ROI results demonstrated that the proposed method produced comparable values of diffusion metrics to the matched high-resolution diffusion metrics of real human data. Particularly, the patterns of distributions of the subjects were similar between the proposed method and real data across whole-brain gray-matter and white-matter ROIs. A deep-learned submillimeter resolution of 0.625 mm diffusion directional image showed high image quality, particularly in the cortical gray matter. We demonstrated the feasibility of using a deep-learning algorithm based on SRCNN in DTI. This approach can be a robust alternative when acquiring the true sub-millimeter diffusion MRI is not available.

The Impact of Anisotropy on the Accuracy of Conductivity Imaging: A Quantitative Validation Study.

We present a quantitative validation study to assess the accuracy of low-frequency conductivity imaging methods, based on a testing current measured using Current Density Imaging (CDI). We tested the proposed procedure to study the influence of tissue anisotropy on the accuracy of conductivity reconstruction methods, using a finite element model of anisotropic brain tissue. Simulations were carried out for three different levels of tissue anisotropy to compare the results obtained by our recently developed anisotropic conductivity method with those obtained by our well-established conductivity method that assumes isotropic conductivity. The validation results clearly show that the conductivity imaging method which takes into account tissue anisotropy yields significantly superior accuracy.

Experimental implementation of a new method of imaging anisotropic electric conductivities.

This paper presents the first experiment of imaging anisotropic impedance using a novel technique called Diffusion Tensor Current Density Impedance Imaging (DTCD-II). A biological anisotropic tissue phantom was constructed and an experimental implementation of the new method was performed. The results show that DT-CD-II is an effective way of non-invasively measuring anisotropic conductivity in biological media. The cross-property factor between the diffusion tensor and the conductivity tensor has been carefully determined from the experimental data, and shown to be spatially inhomogeneous. The results show that this novel imaging approach has the potential to provide valuable new information on tissue properties.