Significance: Single-molecule localization-based super-resolution microscopy has enabled the imaging of microscopic objects beyond the diffraction limit. However, this technique is limited by the requirements of imaging an extremely large number of frames of biological samples to generate a super-resolution image, thus requiring a longer acquisition time. Additionally, the processing of such a large image sequence leads to longer data processing time. Therefore, accelerating image acquisition and processing in single-molecule localization microscopy (SMLM) has been of perennial interest.
Aim: To accelerate three-dimensional (3D) SMLM imaging by leveraging a computational approach without compromising the resolution.
Approach: We used blind sparse inpainting to reconstruct high-density 3D images from low-density ones. The low-density images are generated using much fewer frames than usually needed, thus requiring a shorter acquisition and processing time. Therefore, our technique will accelerate 3D SMLM without changing the existing standard SMLM hardware system and labeling protocol.
Results: The performance of the blind sparse inpainting was evaluated on both simulation and experimental datasets. Superior reconstruction results of 3D SMLM images using up to 10-fold fewer frames in simulation and up to 50-fold fewer frames in experimental data were achieved.
Conclusions: We demonstrate the feasibility of fast 3D SMLM imaging leveraging a computational approach to reduce the number of acquired frames. We anticipate our technique will enable future real-time live-cell 3D imaging to investigate complex nanoscopic biological structures and their functions.
Compressed sensing (CS) is a technology to acquire and reconstruct sparse signals below the Nyquist rate. For images, total variation of the signal is usually minimized to promote sparseness of the image in gradient. However, similar to all L1-minimization algorithms, total variation has the issue of penalizing large gradient, thus causing large errors on image edges. Many non-convex penalties have been proposed to address the issue of L1 minimization. For example, homotopic L0 minimization algorithms have shown success in reconstructing images from magnetic resonance imaging (MRI). Homotopic L0 minimization may suffer from local minimum which may not be sufficiently robust when the signal is not strictly sparse or the measurements are contaminated by noise. In this paper, we propose a hybrid total variation minimization algorithm to integrate the benefits of both L1 and homotopic L0 minimization algorithms for image recovery from reduced measurements. The algorithm minimizes the conventional total variation when the gradient is small, and minimizes the L0 of gradient when the gradient is large. The transition between L1 and L0 of the gradients is determined by an auto-adaptive threshold. The proposed algorithm has the benefits of L1 minimization being robust to noise/approximation errors, and also the benefits of L0 minimization requiring fewer measurements for recovery. Experimental results using MRI data are presented to demonstrate the proposed hybrid total variation minimization algorithm yields improved image quality over other existing methods in terms of the reconstruction accuracy.
KEYWORDS: Magnetic resonance imaging, Picosecond phenomena, Data acquisition, Data centers, Image restoration, Data modeling, Temporal resolution, Fourier transforms, Computer programming, Data analysis
This paper presents a new approach to highly accelerated dynamic parallel MRI using low rank matrix completion, partial separability (PS) model. In data acquisition, k-space data is moderately randomly undersampled at the center kspace navigator locations, but highly undersampled at the outer k-space for each temporal frame. In reconstruction, the navigator data is reconstructed from undersampled data using structured low-rank matrix completion. After all the unacquired navigator data is estimated, the partial separable model is used to obtain partial k-t data. Then the parallel imaging method is used to acquire the entire dynamic image series from highly undersampled data. The proposed method has shown to achieve high quality reconstructions with reduction factors up to 31, and temporal resolution of 29ms, when the conventional PS method fails.
Photoacoustic-computed microscopy (PACM) differs from conventional photoacoustic microscopy (PAM) imaging techniques in a way that thousands of optical foci are generated simultaneously using a two-dimensional microlens array, and raster-scanning these optical foci provides wide-field images. A major limitation of PACM is the slow imaging speed caused by the high power pulsed lasers and large amount of acoustic detectors. Here, we addressed this problem through compressed sensing and image inpainting. Compressed sensing minimizes the number of transducer elements used to acquire each frame, while inpainting minimizes the scanning steps. Combining these two approaches, we improved the imaging speed by sixteen times.
In compressed sensing MRI, it is very important to design sampling pattern for random sampling. For example, SAKE (simultaneous auto-calibrating and k-space estimation) is a parallel MRI reconstruction method using random undersampling. It formulates image reconstruction as a structured low-rank matrix completion problem. Variable density (VD) Poisson discs are typically adopted for 2D random sampling. The basic concept of Poisson disc generation is to guarantee samples are neither too close to nor too far away from each other. However, it is difficult to meet such a condition especially in the high density region. Therefore the sampling becomes inefficient. In this paper, we present an improved random sampling pattern for SAKE reconstruction. The pattern is generated based on a conflict cost with a probability model. The conflict cost measures how many dense samples already assigned are around a target location, while the probability model adopts the generalized Gaussian distribution which includes uniform and Gaussian-like distributions as special cases. Our method preferentially assigns a sample to a k-space location with the least conflict cost on the circle of the highest probability. To evaluate the effectiveness of the proposed random pattern, we compare the performance of SAKEs using both VD Poisson discs and the proposed pattern. Experimental results for brain data show that the proposed pattern yields lower normalized mean square error (NMSE) than VD Poisson discs.
Perfusion imaging is the most applied modality for the assessment of acute stroke. Parameters such as Cerebral Blood Flow (CBF), Cerebral Blood volume (CBV) and Mean Transit Time (MTT) are used to distinguish the tissue infarct core and ischemic penumbra. Due to lack of standardization these parameters vary significantly between vendors and software even when provided with the same data set. There is a critical need to standardize the systems and make them more reliable. We have designed a uniform phantom to test and verify the perfusion systems. We implemented a flow loop with different flow rates (250, 300, 350 ml/min) and injected the same amount of contrast. The images of the phantom were acquired using a Digital Angiographic system. Since this phantom is uniform, projection images obtained using DSA is sufficient for initial validation. To validate the phantom we measured the contrast concentration at three regions of interest (arterial input, venous output, perfused area) and derived time density curves (TDC). We then calculated the maximum slope, area under the TDCs and flow. The maximum slope calculations were linearly increasing with increase in flow rate, the area under the curve decreases with increase in flow rate. There was 25% error between the calculated flow and measured flow. The derived TDCs were clinically relevant and the calculated flow, maximum slope and areas under the curve were sensitive to the measured flow. We have created a systematic way to calibrate existing perfusion systems and assess their reliability.
KEYWORDS: Magnetic resonance imaging, Temporal resolution, 3D modeling, Picosecond phenomena, Spatial resolution, Liver, Fourier transforms, Integrated modeling, Data acquisition, In vivo imaging
Dynamic contrast enhanced MRI requires high spatial resolution for morphological information and high temporal
resolution for contrast pharmacokinetics. The current techniques usually have to compromise the spatial information for
the required temporal resolution. This paper presents a novel method that effectively integrates sparse sampling, parallel
imaging, partial separable (PS) model, and sparsity constraints for highly accelerated DCE-MRI. Phased array coils were
used to continuously acquire data from a stack of variable-density spiral trajectory with a golden angle. In reconstruction,
the sparsity constraints, the coil sensitivities, spatial and temporal bases of the PS model are jointly estimated through
alternating optimization. Experimental results from in vivo DCE liver imaging data show that the proposed method is
able to achieve high spatial and temporal resolutions at the same time.
KEYWORDS: Image quality, Magnetic resonance imaging, Visualization, Magnetism, Medicine, Data acquisition, Image analysis, Digital filtering, Visual process modeling, Data modeling
Parallel magnetic resonance imaging through sensitivity encoding using multiple receiver coils has emerged as an effective tool to reduce imaging time or improve the image quality. Reconstructed image quality is limited by the noise in the acquired k-space data, inaccurate estimation of the sensitivity map, and the ill-conditioned nature of the coefficient matrix. Tikhonov Regularization is currently the most popular method to solve the ill-condition problem. Selections of the regularization map and the regularization parameter are very important. The Perceptual Difference Model (PDM) is a quantitative image quality evaluation tool which has been successfully applied to varieties of MR applications. High correlation between the human rating and the PDM score shows that PDM could be suitable for evaluating image quality in parallel MR imaging. By applying PDM, we compared four methods of selecting the regularization map and four methods of selecting regularization parameter. We find that generalized series (GS) method to select the regularization map together with spatially adaptive method to select the regularization parameter gives the best solution to reconstruct the image. PDM also work as a quantitative image quality index to optimize two important free parameters in spatially adaptive method. We conclude that PDM is an effective tool in helping design and optimize reconstruction methods in parallel MR imaging.
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