KEYWORDS: Compressed sensing, Magnetic resonance imaging, Image restoration, Medical image reconstruction, Brain, Model based design, Matrices, Wavelets, Neuroimaging, Data acquisition
PurposeWe present a method that combines compressed sensing with parallel imaging that takes advantage of the structure of the sparsifying transformation.ApproachPrevious work has combined compressed sensing with parallel imaging using model-based reconstruction but without taking advantage of the structured sparsity. Blurry images for each coil are reconstructed from the fully sampled center region. The optimization problem of compressed sensing is modified to take these blurry images into account, and it is solved to estimate the missing details.ResultsUsing data of brain, ankle, and shoulder anatomies, the combination of compressed sensing with structured sparsity and parallel imaging reconstructs an image with a lower relative error than does sparse SENSE or L1 ESPIRiT, which do not use structured sparsity.ConclusionsTaking advantage of structured sparsity improves the image quality for a given amount of data as long as a fully sampled region centered on the zero frequency of the appropriate size is acquired.
The discrete curvelet transform decomposes an image into a set of fundamental components that are distinguished by direction and size and a low-frequency representation. The curvelet representation of a natural image is approximately sparse; thus, it is useful for compressed sensing. However, with natural images, the low-frequency portion is seldom sparse. This manuscript presents a method to modify the redundant sparsifying transformation comprised of the wavelet and curvelet transforms to take advantage of this fact for compressed sensing image reconstruction. Instead of relying on sparsity for this low-frequency estimate, the Nyquist–Shannon sampling theorem specifies a rectangular region centered on the 0 frequency to be collected, which is used to generate a blurry estimate. A basis pursuit denoising problem is solved to determine the details with a modified sparsifying transformation. Improvements in quality are shown on magnetic resonance and optical images.
We present a new strategy for incorporating high resolution structural information from MRI into the reconstruction of PET imagery via deep domain translated image priors. The strategy involves two steps: (1) predicting a PET uptake volume directly from MRI without requiring a radiation dose, and (2) using the predicted dose-free PET volume to impose sparsity constraints on the PET reconstruction from measured sinograms. The key idea of our approach is that domain translated PET imagery can capture the true spatial and sparsity patterns of PET imagery, which can be used to guide the convergence of the statistics-limited inverse problem. This scheme can be superior to joint-sparsity reconstruction, among other methods, since the mismatch between PET and MRI features is significantly reduced by using the domain translated zero-dose PET as the prior instead. We evaluate this technique on a wholebody 18F-FDG-PET dataset, demonstrating that dichromatic interpolation can recover high quality PET imagery from noisy and low dose PET/MRI, with no observed failure cases.
KEYWORDS: Biological detection systems, Nanosensors, Cell phones, Error analysis, Light sources and illumination, Video processing, Medical diagnostics, Opacity, Data processing, Video
Urinalysis dipsticks were designed to revolutionize urine-based medical diagnosis. They are cheap, extremely portable, and have multiple assays patterned on a single platform. They were also meant to be incredibly easy to use. Unfortunately, there are many aspects in both the preparation and the analysis of the dipsticks that are plagued by user error. This high error is one reason that dipsticks have failed to flourish in both the at-home market and in low-resource settings. Sources of error include: inaccurate volume deposition, varying lighting conditions, inconsistent timing measurements, and misinterpreted color comparisons. We introduce a novel manifold and companion software for dipstick urinalysis that eliminates the aforementioned error sources. A micro-volume slipping manifold ensures precise sample delivery, an opaque acrylic box guarantees consistent lighting conditions, a simple sticker-based timing mechanism maintains accurate timing, and custom software that processes video data captured by a mobile phone ensures proper color comparisons. We show that the results obtained with the proposed device are as accurate and consistent as a properly executed dip-and-wipe method, the industry gold-standard, suggesting the potential for this strategy to enable confident urinalysis testing. Furthermore, the proposed all-acrylic slipping manifold is reusable and low in cost, making it a potential solution for at-home users and low-resource settings.
Fusing a lower resolution color image with a higher resolution monochrome image is a common practice in medical imaging. By incorporating spatial context and/or improving the signal-to-noise ratio, it provides clinicians with a single frame of the most complete information for diagnosis. In this paper, image fusion is formulated as a convex optimization problem that avoids image decomposition and permits operations at the pixel level. This results in a highly efficient and embarrassingly parallelizable algorithm based on widely available robust and simple numerical methods that realizes the fused image as the global minimizer of the convex optimization problem.
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