Quantification of the in vivo brain ultrashort-T2* component in healthy volunteers.

PURPOSE: Recent work has shown MRI is able to measure and quantify signals of phospholipid membrane-bound protons associated with myelin in the human brain. This work seeks to develop an improved technique for characterizing this brain ultrashort- T 2 ∗ $$ {\mathrm{T}}_2\ast $$ component in vivo accounting for T 1 $$ {\mathrm{T}}_1 $$ weighting. METHODS: Data from ultrashort echo time scans from 16 healthy volunteers with variable flip angles (VFA) were collected and fitted into an advanced regression model to quantify signal fraction, relaxation time, and frequency shift of the ultrashort- T 2 ∗ $$ {\mathrm{T}}_2\ast $$ component. RESULTS: The fitted components show intra-subject differences of different white matter structures and significantly elevated ultrashort- T 2 ∗ $$ {\mathrm{T}}_2\ast $$ signal fraction in the corticospinal tracts measured at 0.09 versus 0.06 in other white matter structures and significantly elevated ultrashort- T 2 ∗ $$ {\mathrm{T}}_2\ast $$ frequency shift in the body of the corpus callosum at - $$ - $$ 1.5 versus - $$ - $$ 2.0 ppm in other white matter structures. CONCLUSION: The significantly different measured components and measured T 1 $$ {\mathrm{T}}_1 $$ relaxation time of the ultrashort- T 2 ∗ $$ {\mathrm{T}}_2\ast $$ component suggest that this method is picking up novel signals from phospholipid membrane-bound protons.

Scan-Specific Self-Supervised Bayesian Deep Non-Linear Inversion for Undersampled MRI Reconstruction.

Magnetic resonance imaging is subject to slow acquisition times due to the inherent limitations in data sampling. Recently, supervised deep learning has emerged as a promising technique for reconstructing sub-sampled MRI. However, supervised deep learning requires a large dataset of fully-sampled data. Although unsupervised or self-supervised deep learning methods have emerged to address the limitations of supervised deep learning approaches, they still require a database of images. In contrast, scan-specific deep learning methods learn and reconstruct using only the sub-sampled data from a single scan. Here, we introduce Scan-Specific Self-Supervised Bayesian Deep Non-Linear Inversion (DNLINV) that does not require an auto calibration scan region. DNLINV utilizes a Deep Image Prior-type generative modeling approach and relies on approximate Bayesian inference to regularize the deep convolutional neural network. We demonstrate our approach on several anatomies, contrasts, and sampling patterns and show improved performance over existing approaches in scan-specific calibrationless parallel imaging and compressed sensing.

Synthesizing Complex-Valued Multicoil MRI Data from Magnitude-Only Images.

Despite the proliferation of deep learning techniques for accelerated MRI acquisition and enhanced image reconstruction, the construction of large and diverse MRI datasets continues to pose a barrier to effective clinical translation of these technologies. One major challenge is in collecting the MRI raw data (required for image reconstruction) from clinical scanning, as only magnitude images are typically saved and used for clinical assessment and diagnosis. The image phase and multi-channel RF coil information are not retained when magnitude-only images are saved in clinical imaging archives. Additionally, preprocessing used for data in clinical imaging can lead to biased results. While several groups have begun concerted efforts to collect large amounts of MRI raw data, current databases are limited in the diversity of anatomy, pathology, annotations, and acquisition types they contain. To address this, we present a method for synthesizing realistic MR data from magnitude-only data, allowing for the use of diverse data from clinical imaging archives in advanced MRI reconstruction development. Our method uses a conditional GAN-based framework to generate synthetic phase images from input magnitude images. We then applied ESPIRiT to derive RF coil sensitivity maps from fully sampled real data to generate multi-coil data. The synthetic data generation method was evaluated by comparing image reconstruction results from training Variational Networks either with real data or synthetic data. We demonstrate that the Variational Network trained on synthetic MRI data from our method, consisting of GAN-derived synthetic phase and multi-coil information, outperformed Variational Networks trained on data with synthetic phase generated using current state-of-the-art methods. Additionally, we demonstrate that the Variational Networks trained with synthetic k-space data from our method perform comparably to image reconstruction networks trained on undersampled real k-space data.

Pulmonary Ventilation Analysis Using 1H Ultra-Short Echo Time (UTE) Lung MRI: A Reproducibility Study.

Purpose: To evaluate methods for quantification of pulmonary ventilation with ultrashort echo time (UTE) MRI. Methods: We performed a reproducibility study, acquiring two free-breathing 1H UTE lung MRIs on the same day for six healthy volunteers. The 1) 3D + t cyclic b-spline and 2) symmetric image normalization (SyN) methods for image registration were applied after respiratory phase-resolved image reconstruction. Ventilation maps were calculated using 1) Jacobian determinant of the deformation fields minus one, termed regional ventilation, and 2) intensity percentage difference between the registered and fixed image, termed specific ventilation. We compared the reproducibility of all four method combinations via statistical analysis. Results: Split violin plots and Bland-Altman plots are shown for whole lungs and lung sections. The cyclic b-spline registration and Jacobian determinant regional ventilation quantification provide total ventilation volumes that match the segmentation tidal volume, smooth and uniform ventilation maps. The cyclic b-spline registration and specific ventilation combination yields the smallest standard deviation in the Bland-Altman plot. Conclusion: Cyclic registration performs better than SyN for respiratory phase-resolved 1H UTE MRI ventilation quantification. Regional ventilation correlates better with segmentation lung volume, while specific ventilation is more reproducible.

Metabolically driven maturation of human-induced-pluripotent-stem-cell-derived cardiac microtissues on microfluidic chips.

The immature physiology of cardiomyocytes derived from human induced pluripotent stem cells (hiPSCs) limits their utility for drug screening and disease modelling. Here we show that suitable combinations of mechanical stimuli and metabolic cues can enhance the maturation of hiPSC-derived cardiomyocytes, and that the maturation-inducing cues have phenotype-dependent effects on the cells' action-potential morphology and calcium handling. By using microfluidic chips that enhanced the alignment and extracellular-matrix production of cardiac microtissues derived from genetically distinct sources of hiPSC-derived cardiomyocytes, we identified fatty-acid-enriched maturation media that improved the cells' mitochondrial structure and calcium handling, and observed divergent cell-source-dependent effects on action-potential duration (APD). Specifically, in the presence of maturation media, tissues with abnormally prolonged APDs exhibited shorter APDs, and tissues with aberrantly short APDs displayed prolonged APDs. Regardless of cell source, tissue maturation reduced variabilities in spontaneous beat rate and in APD, and led to converging cell phenotypes (with APDs within the 300-450 ms range characteristic of human left ventricular cardiomyocytes) that improved the modelling of the effects of pro-arrhythmic drugs on cardiac tissue.

Miniaturized iPS-Cell-Derived Cardiac Muscles for Physiologically Relevant Drug Response Analyses.

Tissue engineering approaches have the potential to increase the physiologic relevance of human iPS-derived cells, such as cardiomyocytes (iPS-CM). However, forming Engineered Heart Muscle (EHM) typically requires >1 million cells per tissue. Existing miniaturization strategies involve complex approaches not amenable to mass production, limiting the ability to use EHM for iPS-based disease modeling and drug screening. Micro-scale cardiospheres are easily produced, but do not facilitate assembly of elongated muscle or direct force measurements. Here we describe an approach that combines features of EHM and cardiospheres: Micro-Heart Muscle (µHM) arrays, in which elongated muscle fibers are formed in an easily fabricated template, with as few as 2,000 iPS-CM per individual tissue. Within µHM, iPS-CM exhibit uniaxial contractility and alignment, robust sarcomere assembly, and reduced variability and hypersensitivity in drug responsiveness, compared to monolayers with the same cellular composition. µHM mounted onto standard force measurement apparatus exhibited a robust Frank-Starling response to external stretch, and a dose-dependent inotropic response to the ß-adrenergic agonist isoproterenol. Based on the ease of fabrication, the potential for mass production and the small number of cells required to form µHM, this system provides a potentially powerful tool to study cardiomyocyte maturation, disease and cardiotoxicology in vitro.