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Register a new userPrecision limits of tissue microstructure characterization by Magnetic Resonance Imaging
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Added by
Gonzalo Agustin Alvarez
Publication date
2019
fields
Physics
and research's language is
English
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Characterization of microstructures in live tissues is one of the keys to diagnosing early stages of pathology and understanding disease mechanisms. However, the extraction of reliable information on biomarkers based on microstructure details is still a challenge, as the size of features that can be resolved with non-invasive Magnetic Resonance Imaging (MRI) is orders of magnitude larger than the relevant structures. Here we derive from quantum information theory the ultimate precision limits for obtaining such details by MRI probing of water-molecule diffusion. We show that already available MRI pulse sequences can be optimized to attain the ultimate precision limits by choosing control parameters that are uniquely determined by the expected size, the diffusion coefficient and the spin relaxation time $T_{2}$. By attaining the ultimate precision limit per measurement, the number of measurements and the total acquisition time may be drastically reduced compared to the present state of the art. These results will therefore allow MRI to advance towards unravelling a wealth of diagnostic information.
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Over the past few decades, researchers have developed several approaches such as the Reference Phantom Method (RPM) to estimate ultrasound attenuation coefficient (AC) and backscatter coefficient (BSC). AC and BSC can help to discriminate pathology from normal tissue during in-vivo imaging. In this paper, we propose a new RPM model to simultaneously compute AC and BSC for harmonic imaging and a normalized score that combines the two parameters as a measure of disease progression. The model utilizes the spectral difference between two regions of interest, the first, a proximal, close to the probe and second, a distal, away from the probe. We have implemented an algorithm based on the model and shown that it provides accurate and stable estimates to within 5% of AC and BSC for simulated received echo from post-focal depths of a homogeneous liver-like medium. For practical applications with time gain and time frequency compensated in-phase and quadrature (IQ) data from ultrasound scanner, the method has been approximated and generalized to estimate AC and BSC for tissue layer underlying a more attenuative subcutaneous layer. The angular spectrum approach for ultrasound propagation in biological tissue is employed as a virtual Reference Phantom (VRP). The VRP is calibrated with a fixed probe and scanning protocol for application to liver tissue. In a feasibility study with 16 subjects, the method is able to separate 9/11 cases of progressive non-alcoholic fatty liver disease from 5 normal. In particular, it is able to separate 4/5 cases of non-alcoholic steato-hepatitis and early fibrosis (F<=2) from normal tissue. More extensive clinical studies are needed to assess the full capability of this model for screening and monitoring disease progression in liver and other tissues.
We present a magnet and high power electronics for Prepolarized Magnetic Resonance Imaging (PMRI) in a home-made, special-purpose preclinical system designed for simultaneous visualization of hard and soft biological tissues. PMRI boosts the signal-to-noise ratio (SNR) by means of a long and strong magnetic pulse which must be rapidly switched off prior to the imaging pulse sequence, in timescales shorter than the spin relaxation (or T1) time of the sample. We have operated the prepolarizer at up to 0.5 T and demonstrated enhanced magnetization, image SNR and tissue contrast with PMRI of tap water, an ex vivo mouse brain and food samples. These have T1 times ranging from hundreds of milli-seconds to single seconds, while the preliminary high-power electronics setup employed in this work can switch off the prepolarization field in tens of milli-seconds. In order to make this system suitable for solid-state matter and hard tissues, which feature T1 times as short as 10 ms, we are developing new electronics which can cut switching times to ~300 us. This does not require changes in the prepolarizer module, opening the door to the first experimental demonstration of PMRI on hard biological tissues.
Purpose: To develop generic optimization strategies for image reconstruction using graphical processing units (GPUs) in magnetic resonance imaging (MRI) and to exemplarily report about our experience with a highly accelerated implementation of the non-linear inversion algorithm (NLINV) for dynamic MRI with high frame rates. Methods: The NLINV algorithm is optimized and ported to run on an a multi-GPU single-node server. The algorithm is mapped to multiple GPUs by decomposing the data domain along the channel dimension. Furthermore, the algorithm is decomposed along the temporal domain by relaxing a temporal regularization constraint, allowing the algorithm to work on multiple frames in parallel. Finally, an autotuning method is presented that is capable of combining different decomposition variants to achieve optimal algorithm performance in different imaging scenarios. Results: The algorithm is successfully ported to a multi-GPU system and allows online image reconstruction with high frame rates. Real-time reconstruction with low latency and frame rates up to 30 frames per second is demonstrated. Conclusion: Novel parallel decomposition methods are presented which are applicable to many iterative algorithms for dynamic MRI. Using these methods to parallelize the NLINV algorithm on multiple GPUs it is possible to achieve online image reconstruction with high frame rates.
Two proof-of-principle experiments towards T1-limited magnetic resonance imaging with NV centers in diamond are demonstrated. First, a large number of Rabi oscillations is measured and it is demonstrated that the hyperfine interaction due to the NVs 14N can be extracted from the beating oscillations. Second, the Rabi beats under V-type microwave excitation of the three hyperfine manifolds is studied experimentally and described theoretically.
Nuclear magnetic resonance (NMR) diffusion measurements are widely used to derive parameters indirectly related to the microstructure of biological tissues and porous media. However, a direct imaging of cell or pore shapes and sizes would be of high interest. For a long time, determining pore shapes by NMR diffusion acquisitions seemed impossible, because the necessary phase information could not be preserved. Here we demonstrate experimentally using the measurement technique which we have recently proposed theoretically that the shape of arbitrary closed pores can be imaged by diffusion acquisitions, which yield the phase information. For this purpose, we use hyperpolarized xenon gas in well-defined geometries. The signal can be collected from the whole sample which mainly eliminates the problem of vanishing signal at increasing resolution of conventional NMR imaging. This could be used to non-invasively gain structural information inaccessible so far such as pore or cell shapes, cell density or axon integrity.
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