Mary Kate Manhard

Bio: Mary Kate Manhard is an academic researcher from Harvard University. The author has contributed to research in topics: Cortical bone & Iterative reconstruction. The author has an hindex of 12, co-authored 23 publications receiving 451 citations. Previous affiliations of Mary Kate Manhard include Vanderbilt University & Vanderbilt University Medical Center.

Improving parallel imaging by jointly reconstructing multi-contrast data.

Abstract: PURPOSE To develop parallel imaging techniques that simultaneously exploit coil sensitivity encoding, image phase prior information, similarities across multiple images, and complementary k-space sampling for highly accelerated data acquisition. METHODS We introduce joint virtual coil (JVC)-generalized autocalibrating partially parallel acquisitions (GRAPPA) to jointly reconstruct data acquired with different contrast preparations, and show its application in 2D, 3D, and simultaneous multi-slice (SMS) acquisitions. We extend the joint parallel imaging concept to exploit limited support and smooth phase constraints through Joint (J-) LORAKS formulation. J-LORAKS allows joint parallel imaging from limited autocalibration signal region, as well as permitting partial Fourier sampling and calibrationless reconstruction. RESULTS We demonstrate highly accelerated 2D balanced steady-state free precession with phase cycling, SMS multi-echo spin echo, 3D multi-echo magnetization-prepared rapid gradient echo, and multi-echo gradient recalled echo acquisitions in vivo. Compared to conventional GRAPPA, proposed joint acquisition/reconstruction techniques provide more than 2-fold reduction in reconstruction error. CONCLUSION JVC-GRAPPA takes advantage of additional spatial encoding from phase information and image similarity, and employs different sampling patterns across acquisitions. J-LORAKS achieves a more parsimonious low-rank representation of local k-space by considering multiple images as additional coils. Both approaches provide dramatic improvement in artifact and noise mitigation over conventional single-contrast parallel imaging reconstruction. Magn Reson Med 80:619-632, 2018. © 2018 International Society for Magnetic Resonance in Medicine.

In Vivo Quantitative MR Imaging of Bound and Pore Water in Cortical Bone.

Abstract: Our study demonstrates that quantitative MR imaging that is selective for bound or pore water of cortical bone can be practically performed in vivo, providing bound and pore water maps with standard errors of approximately 2 mol hydrogen 1 per liter of bone.

Validation of quantitative bound- and pore-water imaging in cortical bone.

Abstract: Purpose To implement and validate a previously proposed ultra-short echo time method for measuring collagen-bound- and pore-water concentrations in bone based on their T2 differences Methods Clinically compatible ultra-short echo time image sequences for quantitative T2-based bound and pore-water imaging in bone were implemented and validated on a 3T human scanner and a 47T small bore system Bound- and pore-water images were generating using T2-selective adiabatic pulses In both cases, the magnetization preparation was integrated into a three-dimensional ultra-short echo time acquisition, with 16 radial spokes acquired per preparation Images were acquired from human cadaveric femoral mid-shafts from which isolated bone samples were subsequently extracted for nonimaging analysis using T2 spectroscopic measurements Results A strong correlation was found between imaging-derived concentrations of bound and pore water and those determined from the isolated bone samples Conclusions These studies demonstrate the translation of the previously developed approaches for distinguishing bound and pore water from human cortical bone using practical human MRI constraints of gradient performance and radiofrequency power deposition Magn Reson Med 71:2166–2171, 2014 © 2013 Wiley Periodicals, Inc

Highly accelerated multishot echo planar imaging through synergistic machine learning and joint reconstruction

Abstract: PURPOSE To introduce a combined machine learning (ML)- and physics-based image reconstruction framework that enables navigator-free, highly accelerated multishot echo planar imaging (msEPI) and demonstrate its application in high-resolution structural and diffusion imaging. METHODS Single-shot EPI is an efficient encoding technique, but does not lend itself well to high-resolution imaging because of severe distortion artifacts and blurring. Although msEPI can mitigate these artifacts, high-quality msEPI has been elusive because of phase mismatch arising from shot-to-shot variations which preclude the combination of the multiple-shot data into a single image. We utilize deep learning to obtain an interim image with minimal artifacts, which permits estimation of image phase variations attributed to shot-to-shot changes. These variations are then included in a joint virtual coil sensitivity encoding (JVC-SENSE) reconstruction to utilize data from all shots and improve upon the ML solution. RESULTS Our combined ML + physics approach enabled Rinplane × multiband (MB) = 8- × 2-fold acceleration using 2 EPI shots for multiecho imaging, so that whole-brain T2 and T2 * parameter maps could be derived from an 8.3-second acquisition at 1 × 1 × 3-mm3 resolution. This has also allowed high-resolution diffusion imaging with high geometrical fidelity using 5 shots at Rinplane × MB = 9- × 2-fold acceleration. To make these possible, we extended the state-of-the-art MUSSELS reconstruction technique to simultaneous multislice encoding and used it as an input to our ML network. CONCLUSION Combination of ML and JVC-SENSE enabled navigator-free msEPI at higher accelerations than previously possible while using fewer shots, with reduced vulnerability to poor generalizability and poor acceptance of end-to-end ML approaches.

Magnetic Resonance Imaging Physical Principles And Sequence Design

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MR fingerprinting Deep RecOnstruction NEtwork (DRONE)

Abstract: Demonstrate a novel fast method for reconstruction of multi-dimensional MR fingerprinting (MRF) data using deep learning methods.A neural network (NN) is defined using the TensorFlow framework and trained on simulated MRF data computed with the extended phase graph formalism. The NN reconstruction accuracy for noiseless and noisy data is compared to conventional MRF template matching as a function of training data size and is quantified in simulated numerical brain phantom data and International Society for Magnetic Resonance in Medicine/National Institute of Standards and Technology phantom data measured on 1.5T and 3T scanners with an optimized MRF EPI and MRF fast imaging with steady state precession (FISP) sequences with spiral readout. The utility of the method is demonstrated in a healthy subject in vivo at 1.5T.Network training required 10 to 74 minutes; once trained, data reconstruction required approximately 10 ms for the MRF EPI and 76 ms for the MRF FISP sequence. Reconstruction of simulated, noiseless brain data using the NN resulted in a RMS error (RMSE) of 2.6 ms for T1 and 1.9 ms for T2 . The reconstruction error in the presence of noise was less than 10% for both T1 and T2 for SNR greater than 25 dB. Phantom measurements yielded good agreement (R2 = 0.99/0.99 for MRF EPI T1 /T2 and 0.94/0.98 for MRF FISP T1 /T2 ) between the T1 and T2 estimated by the NN and reference values from the International Society for Magnetic Resonance in Medicine/National Institute of Standards and Technology phantom.Reconstruction of MRF data with a NN is accurate, 300- to 5000-fold faster, and more robust to noise and dictionary undersampling than conventional MRF dictionary-matching.

The role of water compartments in the material properties of cortical bone

Abstract: Comprising ~20% of the volume, water is a key determinant of the mechanical behavior of cortical bone. It essentially exists in two general compartments: within pores and bound to the matrix. The amount of pore water-residing in the vascular-lacunar-canalicular space-primarily reflects intracortical porosity (i.e., open spaces within the matrix largely due to Haversian canals and resorption sites) and as such is inversely proportional to most mechanical properties of bone. Movement of water according to pressure gradients generated during dynamic loading likely confers hydraulic stiffening to the bone as well. Nonetheless, bound water is a primary contributor to the mechanical behavior of bone in that it is responsible for giving collagen the ability to confer ductility or plasticity to bone (i.e., allows deformation to continue once permanent damage begins to form in the matrix) and decreases with age along with fracture resistance. Thus, dehydration by air-drying or by solvents with less hydrogen bonding capacity causes bone to become brittle, but interestingly, it also increases stiffness and strength across the hierarchical levels of organization. Despite the importance of matrix hydration to fracture resistance, little is known about why bound water decreases with age in hydrated human bone. Using (1)H nuclear magnetic resonance (NMR), both bound and pore water concentrations in bone can be measured ex vivo because the proton relaxation times differ between the two water compartments, giving rise to two distinct signals. There are also emerging techniques to measure bound and pore water in vivo with magnetic resonance imaging (MRI). The NMR/MRI-derived bound water concentration is positively correlated with both the strength and toughness of hydrated bone and may become a useful clinical marker of fracture risk.

Cortical Bone Porosity: What Is It, Why Is It Important, and How Can We Detect It?

Abstract: There is growing recognition of the role of micro-architecture in osteoporotic bone loss and fragility. This trend has been driven by advances in imaging technology, which have enabled a transition from measures of mass to micro-architecture. Imaging trabecular bone has been a key research focus, but advances in resolution have also enabled the detection of cortical bone micro-architecture, particularly the network of vascular canals, commonly referred to as 'cortical porosity.' This review aims to provide an overview of what this level of porosity is, why it is important, and how it can be characterized by imaging. Moving beyond a 'trabeculocentric' view of bone loss holds the potential to improve diagnosis and monitoring of interventions. Furthermore, cortical porosity is intimately linked to the remodeling process, which underpins bone loss, and thus a larger potential exists to improve our fundamental understanding of bone health through imaging of both humans and animal models.