Comparison of ring artifact removal methods using flat panel detector based CT images

Background

Ring artifacts are the concentric rings superimposed on the tomographic images often caused by the defective and insufficient calibrated detector elements as well as by the damaged scintillator crystals of the flat panel detector. It may be also generated by objects attenuating X-rays very differently in different projection direction. Ring artifact reduction techniques so far reported in the literature can be broadly classified into two groups. One category of the approaches is based on the sinogram processing also known as the pre-processing techniques and the other category of techniques perform processing on the 2-D reconstructed images, recognized as the post-processing techniques in the literature. The strength and weakness of these categories of approaches are yet to be explored from a common platform.

Method

In this paper, a comparative study of the two categories of ring artifact reduction techniques basically designed for the multi-slice CT instruments is presented from a common platform. For comparison, two representative algorithms from each of the two categories are selected from the published literature. A very recently reported state-of-the-art sinogram domain ring artifact correction method that classifies the ring artifacts according to their strength and then corrects the artifacts using class adaptive correction schemes is also included in this comparative study. The first sinogram domain correction method uses a wavelet based technique to detect the corrupted pixels and then using a simple linear interpolation technique estimates the responses of the bad pixels. The second sinogram based correction method performs all the filtering operations in the transform domain, i.e., in the wavelet and Fourier domain. On the other hand, the two post-processing based correction techniques actually operate on the polar transform domain of the reconstructed CT images. The first method extracts the ring artifact template vector using a homogeneity test and then corrects the CT images by subtracting the artifact template vector from the uncorrected images. The second post-processing based correction technique performs median and mean filtering on the reconstructed images to produce the corrected images.

Results

The performances of the comparing algorithms have been tested by using both quantitative and perceptual measures. For quantitative analysis, two different numerical performance indices are chosen. On the other hand, different types of artifact patterns, e.g., single/band ring, artifacts from defective and mis-calibrated detector elements, rings in highly structural object and also in hard object, rings from different flat-panel detectors are analyzed to perceptually investigate the strength and weakness of the five methods. An investigation has been also carried out to compare the efficacy of these algorithms in correcting the volume images from a cone beam CT with the parameters determined from one particular slice. Finally, the capability of each correction technique in retaining the image information (e.g., small object at the iso-center) accurately in the corrected CT image has been also tested.

Conclusions

The results show that the performances of the algorithms are limited and none is fully suitable for correcting different types of ring artifacts without introducing processing distortion to the image structure. To achieve the diagnostic quality of the corrected slices a combination of the two approaches (sinogram- and post-processing) can be used. Also the comparing methods are not suitable for correcting the volume images from a cone beam flat-panel detector based CT.     

Bone-induced streak artifact suppression in sparse-view CT image reconstruction

ABSTRACT: BACKGROUND: In sparse-view CT imaging, strong streak artifacts may appear around bony structures and they often compromise the image readability. Compressed sensing (CS) or total variation (TV) minimization-based image reconstruction method has reduced the streak artifacts to a great extent, but, sparse-view CT imaging still suffers from residual streak artifacts. We introduce a new bone-induced streak artifact reduction method in the CS-based image reconstruction. METHODS: We firstly identify the high-intensity bony regions from the image reconstructed by the filtered backprojection (FBP) method, and we calculate the sinogram stemming from the bony regions only. Then, we subtract the calculated sinogram, which stands for the bony regions, from the measured sinogram before performing the CS-based image reconstruction. The image reconstructed from the subtracted sinogram will stand for the soft tissues with little streak artifacts on it. To restore the original image intensity in the bony regions, we add the bony region image, which has been identified from the FBP image, to the soft tissue image to form a combined image. Then, we perform the CS-based image reconstruction again on the measured sinogram using the combined image as the initial condition of the iteration. For experimental validation of the proposed method, we take images of a contrast phantom and a rat using a micro-CT and we evaluate the reconstructed images based on two figures of merit, relative mean square error and total variation caused by the streak artifacts. RESULTS: The images reconstructed by the proposed method have been found to have smaller streak artifacts than the ones reconstructed by the original CS-based method when visually inspected. The quantitative image evaluation studies have also shown that the proposed method outperforms the conventional CS-based method. CONCLUSIONS: The proposed method can effectively suppress streak artifacts stemming from bony structures in sparse-view CT imaging.     

Reconstruction for 3D PET Based on Total Variation Constrained Direct Fourier Method

This paper presents a total variation (TV) regularized reconstruction algorithm for 3D positron emission tomography (PET). The proposed method first employs the Fourier rebinning algorithm (FORE), rebinning the 3D data into a stack of ordinary 2D data sets as sinogram data. Then, the resulted 2D sinogram are ready to be reconstructed by conventional 2D reconstruction algorithms. Given the locally piece-wise constant nature of PET images, we introduce the total variation (TV) based reconstruction schemes. More specifically, we formulate the 2D PET reconstruction problem as an optimization problem, whose objective function consists of TV norm of the reconstructed image and the data fidelity term measuring the consistency between the reconstructed image and sinogram. To solve the resulting minimization problem, we apply an efficient methods called the Bregman operator splitting algorithm with variable step size (BOSVS). Experiments based on Monte Carlo simulated data and real data are conducted as validations. The experiment results show that the proposed method produces higher accuracy than conventional direct Fourier (DF) (bias in BOSVS is 70% of ones in DF, variance of BOSVS is 80% of ones in DF).     

Iterative image reconstruction using modified non-local means filtering for limited-angle computed tomography

PurposeLimited-angle CT imaging is an effective technique to reduce radiation. However, existing image reconstruction methods can effectively reduce streak artifacts but fail to suppress those artifacts around edges due to incomplete projection data. Thus, a modified NLM (mNLM) based reconstruction method is proposed.MethodsSince the artifacts around edges mainly exist in local position, it is possible to restore the true pixels in artifacts using pixels located in artifacts-free regions. In each iteration, mNLM is performed on image reconstructed by ART followed by positivity constraint. To solve the problem caused by ART-mNLM that there is undesirable information that may appear in the image, ART-TV is then utilized in the following iterative process after ART-mNLM iterates for a number of iterations. The proposed algorithm is named as ART-mNLM/TV.ResultsSimulation experiments are performed to validate the feasibility of algorithm. When the scanning range is [0, 150°], our algorithm outperforms the ART-NLM and ART-TV with more than 40% and 29% improvement in terms of SNR and with more than 58% and 49% reduction in terms of MAE. Consistently, reconstructed images from real projection data also demonstrate the effectiveness of presented algorithm.ConclusionThis paper uses mNLM which benefits from redundancy of information across the whole image, to recover the true value of pixels in artifacts region by utilizing pixels from artifact-free regions, and artifacts around the edges can be mitigated effectively. Experiments show that the proposed ART-mNLM/TV is able to achieve better performances compared to traditional methods.     

Removing high contrast artifacts via digital inpainting in cryo-electron tomography: an application of compressed sensing

To cope with poor quality in cryo-electron tomography images, electron-dense markers, such as colloidal goldbeads, are often used to assist image registration and analysis algorithms. However, these markers can create artifacts that occlude a specimen due to their high contrast, which can also cause failure of some image processing algorithms. One way of reducing these artifacts is to replace high contrast objects with pixel densities that blend into the surroundings in the projection domain before volume reconstruction. In this paper, we propose digital inpainting via compressed sensing (CS) as a new method to achieve this goal. We show that cryo-ET projections are sparse in the discrete cosine transform (DCT) domain, and, by finding the sparsest DCT domain decompositions given uncorrupted pixels, we can fill in the missing pixel values that are occluded by high contrast objects without discontinuities. Our method reduces visual artifacts both in projections and in tomograms better than conventional algorithms, such as polynomial interpolation and random noise inpainting.     

Improved visualization of X-ray phase contrast volumetric data through artifact-free integrated differential images

Artifacts arising when differential phase images are integrated is a common problem to several X-ray phase-based experimental techniques. The combination of noise and insufficient sampling of the high-frequency differential phase signal leads to the formation of streak artifacts in the projections, translating into poor image quality in the tomography slices. In this work, we apply a non-iterative integration algorithm proven to reduce streak artifacts in planar (2D) images to a differential phase tomography scan. We report on how the reduction of streak artifacts in the projections improves the quality of the tomography slices, especially in the directions different from the reconstruction plane. Importantly, the method is compatible with large tomography datasets in terms of computation time.     

Sparse-view CT reconstruction based on multi-level wavelet convolution neural network

Sparse-view computed tomography (CT) is a recent approach to reducing the radiation dose in patients and speeding up the data acquisition. Consequently, sparse-view CT has been of particular interest among researchers within the CT community. Advanced reconstruction algorithms for sparse-view CT, such as iterative algorithms with total-variation (TV), have been studied along with the problem of increasing computational burden and the blurring of artifacts in the reconstructed images. Studies on deep-learning-based approaches applying U-NET have recently achieved remarkable outcomes in various domains including low-dose CT. In this study, we propose a new method for sparse-view CT reconstruction based on a multi-level wavelet convolutional neural network (MWCNN). First, a filtered backprojection (FBP) was used to reconstruct a sparsely sampled sinogram from 60, 120, and 180 projections. Subsequently, the sparse-view data obtained from FBP were fed to a deep-learning network, i.e., the MWCNN. Our network architecture combines a wavelet transform and modified U-NET without pooling. By replacing the pooling function with the wavelet transform, the receptive field is enlarged to improve the performance. We qualitatively and quantitatively evaluated the interpolation, iterative TV method, and standard U-NET in terms of a reduction in the streaking artifacts and a preservation of the anatomical structures. When compared with other methods, the proposed method showed the highest performance based on various evaluation parameters such as the structural similarity, root mean square error, and resolution. These results indicate that the MWCNN possesses a powerful potential for achieving a sparse-view CT reconstruction.     

A Novel Weighted Total Difference Based Image Reconstruction Algorithm for Few-View Computed Tomography

In practical applications of computed tomography (CT) imaging, due to the risk of high radiation dose imposed on the patients, it is desired that high quality CT images can be accurately reconstructed from limited projection data. While with limited projections, the images reconstructed often suffer severe artifacts and the edges of the objects are blurred. In recent years, the compressed sensing based reconstruction algorithm has attracted major attention for CT reconstruction from a limited number of projections. In this paper, to eliminate the streak artifacts and preserve the edge structure information of the object, we present a novel iterative reconstruction algorithm based on weighted total difference (WTD) minimization, and demonstrate the superior performance of this algorithm. The WTD measure enforces both the sparsity and the directional continuity in the gradient domain, while the conventional total difference (TD) measure simply enforces the gradient sparsity horizontally and vertically. To solve our WTD-based few-view CT reconstruction model, we use the soft-threshold filtering approach. Numerical experiments are performed to validate the efficiency and the feasibility of our algorithm. For a typical slice of FORBILD head phantom, using 40 projections in the experiments, our algorithm outperforms the TD-based algorithm with more than 60% gains in terms of the root-mean-square error (RMSE), normalized root mean square distance (NRMSD) and normalized mean absolute distance (NMAD) measures and with more than 10% gains in terms of the peak signal-to-noise ratio (PSNR) measure. While for the experiments of noisy projections, our algorithm outperforms the TD-based algorithm with more than 15% gains in terms of the RMSE, NRMSD and NMAD measures and with more than 4% gains in terms of the PSNR measure. The experimental results indicate that our algorithm achieves better performance in terms of suppressing streak artifacts and preserving the edge structure information of the object.     

Convolutional neural networks for reconstruction of undersampled optical projection tomography data applied to in vivo imaging of zebrafish

Optical projection tomography (OPT) is a 3D mesoscopic imaging modality that can utilize absorption or fluorescence contrast. 3D images can be rapidly reconstructed from tomographic data sets sampled with sufficient numbers of projection angles using the Radon transform, as is typically implemented with optically cleared samples of the mm‐to‐cm scale. For in vivo imaging, considerations of phototoxicity and the need to maintain animals under anesthesia typically preclude the acquisition of OPT data at a sufficient number of angles to avoid artifacts in the reconstructed images. For sparse samples, this can be addressed with iterative algorithms to reconstruct 3D images from undersampled OPT data, but the data processing times present a significant challenge for studies imaging multiple animals. We show here that convolutional neural networks (CNN) can be used in place of iterative algorithms to remove artifacts—reducing processing time for an undersampled in vivo zebrafish dataset from 77 to 15 minutes. We also show that using CNN produces reconstructions of equivalent quality to compressed sensing with 40% fewer projections. We further show that diverse training data classes, for example, ex vivo mouse tissue data, can be used for CNN‐based reconstructions of OPT data of other species including live zebrafish.      

Statistical learning methods as a preprocessing step for survival analysis: evaluation of concept using lung cancer data

Background

Despite its superb lateral resolution, flat-panel-detector (FPD) based tomosynthesis suffers from low contrast and inter-plane artifacts caused by incomplete cancellation of the projection components stemming from outside the focal plane. The incomplete cancellation of the projection components, mostly due to the limited scan angle in the conventional tomosynthesis scan geometry, often makes the image contrast too low to differentiate the malignant tissues from the background tissues with confidence.

Methods

In this paper, we propose a new method to suppress the inter-plane artifacts in FPD-based tomosynthesis. If 3D whole volume CT images are available before the tomosynthesis scan, the CT image data can be incorporated into the tomosynthesis image reconstruction to suppress the inter-plane artifacts, hence, improving the image contrast. In the proposed technique, the projection components stemming from outside the region-of-interest (ROI) are subtracted from the measured tomosynthesis projection data to suppress the inter-plane artifacts. The projection components stemming from outside the ROI are calculated from the 3D whole volume CT images which usually have lower lateral resolution than the tomosynthesis images. The tomosynthesis images are reconstructed from the subtracted projection data which account for the x-ray attenuation through the ROI. After verifying the proposed method by simulation, we have performed both CT scan and tomosynthesis scan on a phantom and a sacrificed rat using a FPD-based micro-CT.

Results

We have measured contrast-to-noise ratio (CNR) from the tomosynthesis images which is an indicator of the residual inter-plane artifacts on the focal-plane image. In both cases of the simulation and experimental imaging studies of the contrast evaluating phantom, CNRs have been significantly improved by the proposed method. In the rat imaging also, we have observed better visual contrast from the tomosynthesis images reconstructed by the proposed method.

Conclusions

The proposed tomosynthesis technique can improve image contrast with aids of 3D whole volume CT images. Even though local tomosynthesis needs extra 3D CT scanning, it may find clinical applications in special situations in which extra 3D CT scan is already available or allowed.     

A fully automatic 3D reconstruction method using simulated annealing enables accurate posterioric angular assignment of protein projections

Single-particle analysis is a structure determining method using electron microscopic (EM) images, which does not require protein crystal. In this method, projections are picked up and used to reconstruct a three-dimensional (3D) structure. When the conical tilting method is not available, the particle images are usually classified and averaged to improve the signal-to-noise ratio. The Euler angles of these average images must be posteriorically assigned to create a primary 3D model. We developed a new, fully automatic unsupervised Euler angle assignment method, which does not require an initial 3D reference and which is applicable to asymmetric molecules. In this method, the Euler angle of each average image is initially set randomly and then automatically corrected in relation to those of the other averages by iterated optimizations using the Simulated Annealing (SA) algorithm. At each iteration, the 3D structure is reconstructed based on the current Euler angles and reprojected back in the average-input directions. A modified cross-correlation between each reprojection and its corresponding original average is then calculated. The correlations are summed as a total 3D echo-correlation score to evaluate the Euler angles at this iteration. Then, one of the projections is selected, its Euler angle is changed randomly, and the score is also calculated. Based on the score change, judgment of whether to accept or reject the new angle is made using the SA algorithm, which is introduced to overcome the local minimums. After a certain number of iterations of this process, the angles of all averages converge so as to create a reliable primary 3D model. This echo-correlated 3D reconstruction with simulated annealing also has potential for wide application to general 3D reconstruction from various types of 2D images.     

The effect of overabundant projection directions on 3D reconstruction algorithms

The experimental process of collecting images from macromolecules in an electron microscope is such that it does not allow for prior specification of the angular distribution of the projection images. As a consequence, an uneven distribution of projection directions may occur. Concerns have been raised recently about the behavior of 3D reconstruction algorithms for the case of unevenly distributed projections. It has been illustrated on experimental data that in the case of a heavily uneven distribution of projection directions some algorithms tend to elongate the reconstructed volumes along the overloaded direction so much as to make a quantitative biological analysis impossible. In answer to these concerns we have developed a strategy for quantitative comparison and optimization of 3D reconstruction algorithms. We apply this strategy to quantitatively analyze algebraic reconstruction techniques (ART) with blobs, simultaneous iterative reconstruction techniques (SIRT) with voxels, and weighted backprojection (WBP). We show that the elongation artifacts that had been previously reported can be strongly reduced. With our specific choices for the free parameters of the three algorithms, WBP reconstructions tend to be inferior to those obtained with either SIRT or ART and the results obtained with ART are comparable to those with SIRT, but at a very small fraction of the computational cost of SIRT.     

Structure of holo-chaperonin studied with electron microscopy. Oligomeric cpn10 on top of two layers of cpn60 rings with two stripes each.

A structural model of holo-chaperonin, known as a protein-folding control protein comprising 60 kDa (cpn60) and 10 kDa polypeptides (cpn10), is proposed based on the electron microscopic images of holo-chaperonin from Thermus thermophilus and cpn60 from Paracoccus denitrificans. Isolated Paracoccus cpn60 shows very similar images to those of Escherichia coli tetradecameric cpn60, a seven-membered ring in the top view and a rectangular shape with four stripes in the side view. However, a small number of half-thick rectangles with two stripes are also seen which indicates that a single cpn60-heptamer ring has two stripes parallel to the plane of the ring. Thermus holo-chaperonin shows a bullet-like shape in the side view, and antibody against cpn10 binds only to the round side of the bullet. We conclude that a single cpn60-heptamer ring with two stripes stacks into two layers, and a cpn10 oligomer binds to one side of the layers.     

Analysis of a novel offset cone-beam computed mammotomography system geometry for accomodating various breast sizes

We evaluate a newly developed dedicated cone-beam transmission computed mammotomography (CmT) system configuration using an optimized quasi-monochromatic cone beam technique for attenuation correction of SPECT in a planned dual-modality emission and transmission system for pendant, uncompressed breasts. In this study, we perform initial CmT acquisitions using various sized breast phantoms to evaluate an offset cone-beam geometry. This offset geometry provides conjugate projections through a full 360 degree gantry rotation, and thus yields a greatly increased effective field of view, allowing a much wider range of breast sizes to be imaged without truncation in reconstructed images. Using a tungsten X-ray tube and digital flat-panel X-ray detector in a compact geometry, we obtained initial CmT scans without shift and with the offset geometry, using geometrical frequency/resolution phantoms and two different sizes of breast phantoms. Acquired data were reconstructed using an ordered subsets transmission iterative algorithm. Projection images indicate that the larger, 20 cm wide, breast requires use of a half-cone-beam offset scan to eliminate truncation artifacts. Reconstructed image results illustrate elimination of truncation artifacts, and that the novel quasi-monochromatic beam yields reduced beam hardening. The offset geometry CmT system can indeed potentially be used for structural imaging and accurate attenuation correction for the functional dedicated breast SPECT system.     

A Novel Prior- and Motion-Based Compressed Sensing Method for Small-Animal Respiratory Gated CT

Low-dose protocols for respiratory gating in cardiothoracic small-animal imaging lead to streak artifacts in the images reconstructed with a Feldkamp-Davis-Kress (FDK) method. We propose a novel prior- and motion-based reconstruction (PRIMOR) method, which improves prior-based reconstruction (PBR) by adding a penalty function that includes a model of motion. The prior image is generated as the average of all the respiratory gates, reconstructed with FDK. Motion between respiratory gates is estimated using a nonrigid registration method based on hierarchical B-splines. We compare PRIMOR with an equivalent PBR method without motion estimation using as reference the reconstruction of high dose data. From these data acquired with a micro-CT scanner, different scenarios were simulated by changing photon flux and number of projections. Methods were evaluated in terms of contrast-to-noise-ratio (CNR), mean square error (MSE), streak artefact indicator (SAI), solution error norm (SEN), and correction of respiratory motion. Also, to evaluate the effect of each method on lung studies quantification, we have computed the Jaccard similarity index of the mask obtained from segmenting each image as compared to those obtained from the high dose reconstruction. Both iterative methods greatly improved FDK reconstruction in all cases. PBR was prone to streak artifacts and presented blurring effects in bone and lung tissues when using both a low number of projections and low dose. Adopting PBR as a reference, PRIMOR increased CNR up to 33% and decreased MSE, SAI and SEN up to 20%, 4% and 13%, respectively. PRIMOR also presented better compensation for respiratory motion and higher Jaccard similarity index. In conclusion, the new method proposed for low-dose respiratory gating in small-animal scanners shows an improvement in image quality and allows a reduction of dose or a reduction of the number of projections between two and three times with respect to previous PBR approaches.     

Three-dimensional biofilm image reconstruction for assessing structural parameters

Parameters representing three-dimensional (3D) biofilm structure are quantified from confocal laser-scanning microscope (CLSM) images. These 3D parameters describe the distribution of biomass pixels within the space occupied by a biofilm; however, they lack a direct connection to biofilm activity. As a result, researchers choose a handful of parameters without there being a consensus on a standard set of parameters. We hypothesized that a select 3D parameter set could be used to reconstruct a biofilm image and that the reconstructed and original biofilm images would have similar activities. To test this hypothesis, an algorithm was developed to reconstruct a biofilm image with parameters identical to those of the original CLSM image. We introduced an objective method to assess the reconstruction algorithm by comparing the activities of the original and reconstructed biofilm images. We found that biofilm images with identical structural parameters showed nearly identical activities and substrate concentration profiles. This implies that the set containing all common structural parameters can successfully describe biofilm structure. This finding is significant, as it opens the door to the next step, of finding a smaller standard set of biofilm structural parameters that can be used to compare biofilm structure.     

Noise properties of reconstructed images in a kilo-voltage on-board imaging system with iterative reconstruction techniques: A phantom study

X-ray computed tomography (CT) images obtained with a kilo-voltage (kV) on-board imaging (OBI) system improve the accuracy of patient setup and treatment planning. The use of iterative reconstruction techniques (IRTs) for CT imaging can also reduce radiation dose compared to analytic reconstruction techniques. Despite these improvements, the image quality varies with IRTs, and the noise structure of reconstructed images can be distorted by IRTs. In this study, the noise properties and spatial resolution of the images reconstructed by IRTs were evaluated in terms of conventional noise metrics, high-order statistics, noise spectral density (NSD) and modulation transfer function (MTF) at different radiation doses. A kV OBI system mounted on a Varian Trilogy machine and a CATPHAN600 phantom were used to obtain projections, and the projections were reconstructed by Feldkamp (FDK), algebraic reconstruction technique (ART), maximum-likelihood expectation–maximization (MLEM) and total variation (TV) minimization algorithms. The reconstructed images were compared according to mean, standard deviation, skewness, kurtosis, NSD and MTF at different radiation doses. The results demonstrated that the noise properties and spatial resolution of reconstructed images depend on the type of IRT and the radiation dose. The noise structures are altered by IRTs and can be characterized by high-order statistics and NSD, as well as conventional noise metrics. In conclusion, high-order statistics and NSD should be considered in order to provide detailed information for the images reconstructed by IRTs. Also, trade-off among noise properties, spatial resolution and contrast is important to optimize image quality obtained using IRTs.     

X-ray scatter data for flat-panel detector CT

In modern X-ray computed tomography (CT) a trend to increased volume coverage by using multi-row detectors is apparent. Flat-panel detector CT (FPD-CT) systems provide an even larger field of measurement which, however, results in an increased scatter fraction. We investigated the scatter intensities registered in the case of FPD-CT. A hybrid model for the simulation of scatter combining deterministic and Monte Carlo methods was used for the scatter calculations. The influence of imaging parameters on the registered scatter intensity was examined both in single projections and reconstructed images. Scatter-to-primary ratios (SPRs) are given for various values of object thickness, field size, object-to-detector distance, incident energy and projection angle. For the simulations, homogeneous water phantoms and realistic patient data sets were used to produce scatter data representative for clinical situations. The SPR increases with object size, collimation and z-extent resulting in SPR ≫ 1 and respective scatter artifacts in the reconstructed images. In contrary, the scatter intensity decreases non-linearly with the object-to-detector distance. The angular and spatial distributions of scatter form a flat function as compared to the distribution of the primary signal. Single scatter appears to determine the distribution and magnitude of the total-scatter intensity at the detector.     

Accelerated Optical Projection Tomography Applied to In Vivo Imaging of Zebrafish

Optical projection tomography (OPT) provides a non-invasive 3-D imaging modality that can be applied to longitudinal studies of live disease models, including in zebrafish. Current limitations include the requirement of a minimum number of angular projections for reconstruction of reasonable OPT images using filtered back projection (FBP), which is typically several hundred, leading to acquisition times of several minutes. It is highly desirable to decrease the number of required angular projections to decrease both the total acquisition time and the light dose to the sample. This is particularly important to enable longitudinal studies, which involve measurements of the same fish at different time points. In this work, we demonstrate that the use of an iterative algorithm to reconstruct sparsely sampled OPT data sets can provide useful 3-D images with 50 or fewer projections, thereby significantly decreasing the minimum acquisition time and light dose while maintaining image quality. A transgenic zebrafish embryo with fluorescent labelling of the vasculature was imaged to acquire densely sampled (800 projections) and under-sampled data sets of transmitted and fluorescence projection images. The under-sampled OPT data sets were reconstructed using an iterative total variation-based image reconstruction algorithm and compared against FBP reconstructions of the densely sampled data sets. To illustrate the potential for quantitative analysis following rapid OPT data acquisition, a Hessian-based method was applied to automatically segment the reconstructed images to select the vasculature network. Results showed that 3-D images of the zebrafish embryo and its vasculature of sufficient visual quality for quantitative analysis can be reconstructed using the iterative algorithm from only 32 projections—achieving up to 28 times improvement in imaging speed and leading to total acquisition times of a few seconds.     

Single particle reconstruction of membrane proteins: A tool for understanding the 3D structure of disease-related macromolecules

Membrane proteins play important roles in cell functions such as neurotransmission, muscle contraction, and hormone secretion, but their structures are mostly undetermined. Several techniques have been developed to elucidate the structure of macromolecules; X-ray or electron crystallography, nuclear magnetic resonance spectroscopy, and high-resolution electron microscopy. Electron microscopy-based single particle reconstruction, a computer-aided structure determination method, reconstructs a three-dimensional (3D) structure from projections of monodispersed protein. A large number of particle images are picked up from EM films, aligned and classified to generate two-dimensional (2D) averages, and, using the Euler angle of each 2D average, reconstructed into a 3D structure. This method is challenging due to the necessity for close collaboration between classical biochemistry and innovative information technology, including parallel computing. However, recent progress in electron microscopy, mathematical algorithms, and computational ability has greatly increased the subjects that are considered to be primarily addressable using single particle reconstruction. Membrane proteins are one of these targets to which the single particle reconstruction is successfully applied for understanding of their structures. In this paper, we will introduce recently reconstructed channel-related proteins and discuss the applicability of this technique in understanding molecular structures and their roles in pathology.