Generalized optimal spatial filtering using a kernel approach with application to EEG classification

Common spatial patterns (CSP) has been widely used for finding the linear spatial filters which are able to extract the discriminative brain activities between two different mental tasks. However, the CSP is difficult to capture the nonlinearly clustered structure from the non-stationary EEG signals. To relax the presumption of strictly linear patterns in the CSP, in this paper, a generalized CSP (GCSP) based on generalized singular value decomposition (GSVD) and kernel method is proposed. Our method is able to find the nonlinear spatial filters which are formulated in the feature space defined by a nonlinear mapping through kernel functions. Furthermore, in order to overcome the overfitting problem, the regularized GCSP is developed by adding the regularized parameters. The experimental results demonstrate that our method is an effective nonlinear spatial filtering method.     

Enhancement of soft-tissue contrast in cone-beam CT using an anti-scatter grid with a sparse sampling approach

PurposeAnti-scatter grids suppress the scatter substantially thus improving image contrast in radiography. However, its active use in cone-beam CT for the purpose of improving contrast-to-noise ratio (CNR) has not been successful mainly due to the increased noise related to Poisson statistics of photons. This paper proposes a sparse-view scanning approach to address the above issue.MethodCompared to the conventional cone-beam CT imaging framework, the proposed method reduces the number of projections and increases exposure in each projection to enhance image quality without an additional cost of radiation dose to patients. For image reconstruction from sparse-view data, an adaptive-steepest-descent projection-onto-convex-sets (ASD POCS) algorithm regularized by total-variation (TV) minimization was adopted. Contrast and CNR with various scattering conditions were evaluated in projection domain by a simulation study using GATE. Then we evaluated contrast, resolution, and image uniformity in CT image domain with Catphan phantom. A head phantom with soft-tissue structures was also employed for demonstrating a realistic application. A virtual grid-based estimation and reduction of scatter has also been implemented for comparison with the real anti-scatter grid.ResultsIn the projection domain evaluation, contrast and CNR enhancement was observed when using an anti-scatter grid compared to the virtual grid. In the CT image domain, the proposed method produced substantially higher contrast and CNR of the low-contrast structures with much improved image uniformity.ConclusionWe have shown that the proposed method can provide high-quality CBCT images particularly with an increased contrast of soft-tissue at a neutral dose for image-guidance.     

Low-Dose Micro-CT Imaging for Vascular Segmentation and Analysis Using Sparse-View Acquisitions

The aim of this study is to investigate whether reliable and accurate 3D geometrical models of the murine aortic arch can be constructed from sparse-view data in vivo micro-CT acquisitions. This would considerably reduce acquisition time and X-ray dose. In vivo contrast-enhanced micro-CT datasets were reconstructed using a conventional filtered back projection algorithm (FDK), the image space reconstruction algorithm (ISRA) and total variation regularized ISRA (ISRA-TV). The reconstructed images were then semi-automatically segmented. Segmentations of high- and low-dose protocols were compared and evaluated based on voxel classification, 3D model diameters and centerline differences. FDK reconstruction does not lead to accurate segmentation in the case of low-view acquisitions. ISRA manages accurate segmentation with 1024 or more projection views. ISRA-TV needs a minimum of 256 views. These results indicate that accurate vascular models can be obtained from micro-CT scans with 8 times less X-ray dose and acquisition time, as long as regularized iterative reconstruction is used.     

PET脑血流量测量方法和临床应用进展

脑血流量测量对于脑血管疾病、脑肿瘤诊断和疗效评估具有重要的临床价值。PET是基于正电子示剂技术无创性、精确测量脑血流量的方法,正日益广泛地应用于临床。按照PET测量脑血流量的方法和使用的正电子示踪剂不同,其测量方法分为平衡法、放射自显影法和动力学方法三种。18O-H2O示踪剂PET测量脑血流量被认为测量脑血流方法的"金标准"。随着PET设备分辨率提高、新的图像重建方法使用和PET与MRI图像融合技术不断成熟,18F-FDG首次通过、采用图像衍生动脉输入函数(imagederived arterial input function,IDAIF)替代动脉抽血样精确测量脑血量方法受到广泛重视,有可能逐步取代高成本的18O-H2O测量脑血流量。PET无创、方便和精确测量脑血流量的方法在临床应使用有助于脑血管性疾病、脑肿瘤和脑退行性病变早期诊断、鉴别诊断和个性化医疗。本文介绍PET脑血流量测量原理、方法和临床应用进展。     

UCSF tomography: an integrated software suite for real-time electron microscopic tomographic data collection, alignment, and reconstruction

A real-time alignment and reconstruction scheme for electron microscopic tomography (EMT) has been developed and integrated within our UCSF tomography data collection software. This newly integrated software suite provides full automation from data collection to real-time reconstruction by which the three-dimensional (3D) reconstructed volume is immediately made available at the end of each data collection. Real-time reconstruction is achieved by calculating a weighted back-projection on a small Linux cluster (five dual-processor compute nodes) concurrently with the UCSF tomography data collection running on the microscope's computer, and using the fiducial-marker free alignment data generated during the data collection process. The real-time reconstructed 3D volume provides users with immediate feedback to fully asses all aspects of the experiment ranging from sample choice, ice thickness, experimental parameters to the quality of specimen preparation. This information can be used to guide subsequent data collections. Access to the reconstruction is especially useful in low-dose cryo EMT where such information is very difficult to obtain due to extraordinary low signal to noise ratio in each 2D image. In our environment, we generally collect 2048 x 2048 pixel images which are subsequently computationally binned four-fold for the on-line reconstruction. Based upon experiments performed with thick and cryo specimens at various CCD magnifications (50000x-80000x), alignment accuracy is sufficient to support this reduced resolution but should be refined before calculating a full resolution reconstruction. The reduced resolution has proven to be quite adequate to assess sample quality, or to screen for the best data set for full-resolution reconstruction, significantly improving both productivity and efficiency of system resources. The total time from start of data collection to a final reconstructed volume (512 x 512 x 256 pixels) is about 50 min for a +/-70 degrees 2k x 2k pixel tilt series acquired at every 1 degrees.     

Fast rotational matching of single-particle images

The presence of noise and absence of contrast in electron micrographs lead to a reduced resolution of the final 3D reconstruction, due to the inherent limitations of single-particle image alignment. The fast rotational matching (FRM) algorithm was introduced recently for an accurate alignment of 2D images under such challenging conditions. Here, we implemented this algorithm for the first time in a standard 3D reconstruction package used in electron microscopy. This allowed us to carry out exhaustive tests of the robustness and reliability in iterative orientation determination, classification, and 3D reconstruction on simulated and experimental image data. A classification test on GroEL chaperonin images demonstrates that FRM assigns up to 13% more images to their correct reference orientation, compared to the classical self-correlation function method. Moreover, at sub-nanometer resolution, GroEL and rice dwarf virus reconstructions exhibit a remarkable resolution gain of 10-20% that is attributed to the novel image alignment kernel.     

CT iterative reconstruction in image space: A phantom study

Although iterative reconstruction is widely applied in SPECT/PET, its introduction in clinical CT is quite recent, in the past the demand for extensive computer power and long image reconstruction times have stopped the diffusion of this technique. Recently Iterative Reconstruction in Image Space (IRIS) has been introduced on Siemens top CT scanners. This recon method works on image data area, reducing the time-consuming loops on raw data and noise removal is obtained in subsequent iterative steps with a smoothing process. We evaluated image noise, low contrast resolution, CT number linearity and accuracy, transverse and z-axis spatial resolution using some dedicated phantoms in single, dual source and cardiac mode. We reconstructed images with a traditional filtered back-projection algorithm and with IRIS. The iterative procedure preserves spatial resolution, CT number accuracy and linearity moreover decreases image noise. These preliminary results support the idea that dose reduction with preserved image quality is possible with IRIS, even if studies on patients are necessary to confirm these data.     

A 11.5 A single particle reconstruction of GroEL using EMAN.

Single-particle analysis has become an increasingly important method for structural determination of large macromolecular assemblies. GroEL is an 800 kDa molecular chaperone, which, along with its co-chaperonin GroES, promotes protein folding both in vitro and in the bacterial cell. EMAN is a single-particle analysis software package, which was first publicly distributed in 2000. We present a three-dimensional reconstruction of native naked GroEL to approximately 11.5 A performed entirely with EMAN. We demonstrate that the single-particle reconstruction, X-ray scattering data and X-ray crystal structure all agree well at this resolution. These results validate the specific methods of image restoration, reconstruction and evaluation techniques implemented in EMAN. It also demonstrates that the single-particle reconstruction technique and X-ray crystallography will yield consistent structure factors, even at low resolution, when image restoration is performed correctly. A detailed comparison of the single-particle and X-ray structures exhibits some small variations in the equatorial domain of the molecule, likely due to the absence of crystal packing forces in the single-particle reconstruction.     

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).     

Structural similarity regularized and sparse coding based super-resolution for medical images

Recently the single image super-resolution reconstruction (SISR) via sparse coding has attracted increasing interests. Considering that there are obviously repetitive image structures in medical images, in this study we propose a regularized SISR method via sparse coding and structural similarity. The pixel based recovery is incorporated as a regularization term to exploit the non-local structural similarities of medical images, which is very helpful in further improving the quality of recovered medical images. An alternative variables optimization algorithm is proposed and some medical images including CT, MRI and ultrasound images are used to investigate the performance of our proposed method. The results show the superiority of our method to its counterparts.     

Attenuation correction for rotating multi-segment slant-hole SPECT in breast imaging

Rotating multi-segment slant-hole (RMSSH) SPECT is suitable for detecting small and low-contrast breast lesions since it has much higher detection efficiency compared with parallel-hole SPECT for the same resolution and can image the breast at a closer distance.Our RMSSH SPECT image reconstruction extends a previous rotation-shear transformation based 3D iterative reconstruction method to include non-uniform attenuation compensation. Reconstructed RMSSH SPECT images with attenuation compensation show much improved quantitative accuracy and less image artifacts than without. We conclude that attenuation compensation provides RMSSH SPECT images with improved quality and quantitative accuracy.     

Imaging neuronal structure dynamics using 2‐photon super‐resolution patterned excitation reconstruction microscopy

Visualizing fine neuronal structures deep inside strongly light‐scattering brain tissue remains a challenge in neuroscience. Recent nanoscopy techniques have reached the necessary resolution but often suffer from limited imaging depth, long imaging time or high light fluence requirements. Here, we present two‐photon super‐resolution patterned excitation reconstruction (2P‐SuPER) microscopy for 3‐dimensional imaging of dendritic spine dynamics at a maximum demonstrated imaging depth of 130 μm in living brain tissue with approximately 100 nm spatial resolution. We confirmed 2P‐SuPER resolution using fluorescence nanoparticle and quantum dot phantoms and imaged spiny neurons in acute brain slices. We induced hippocampal plasticity and showed that 2P‐SuPER can resolve increases in dendritic spine head sizes on CA1 pyramidal neurons following theta‐burst stimulation of Schaffer collateral axons. 2P‐SuPER further revealed nanoscopic increases in dendritic spine neck widths, a feature of synaptic plasticity that has not been thoroughly investigated due to the combined limit of resolution and penetration depth in existing imaging technologies.      

3D multicolor super-resolution imaging offers improved accuracy in neuron tracing

The connectivity among neurons holds the key to understanding brain function. Mapping neural connectivity in brain circuits requires imaging techniques with high spatial resolution to facilitate neuron tracing and high molecular specificity to mark different cellular and molecular populations. Here, we tested a three-dimensional (3D), multicolor super-resolution imaging method, stochastic optical reconstruction microscopy (STORM), for tracing neural connectivity using cultured hippocampal neurons obtained from wild-type neonatal rat embryos as a model system. Using a membrane specific labeling approach that improves labeling density compared to cytoplasmic labeling, we imaged neural processes at 44 nm 2D and 116 nm 3D resolution as determined by considering both the localization precision of the fluorescent probes and the Nyquist criterion based on label density. Comparison with confocal images showed that, with the currently achieved resolution, we could distinguish and trace substantially more neuronal processes in the super-resolution images. The accuracy of tracing was further improved by using multicolor super-resolution imaging. The resolution obtained here was largely limited by the label density and not by the localization precision of the fluorescent probes. Therefore, higher image resolution, and thus higher tracing accuracy, can in principle be achieved by further improving the label density.     

Toward a dedicated system for quantitative SPECT mammotomography

This work extends our previous quantitative brain SPECT research to breast imaging-SPECT mammotomography. A cost-effective dedicated SPECT mammotomography system is presented, which aims to acquire sufficient information for efficient reconstruction of large volumetric images. A very short focal-length fan-beam collimation is designed to maximize the information collected from a relitively small vital organ that has a uniform attenuation property. Data noise of a Poisson nature is accurately modeled and effectively treated in sinogram space, followed by efficient compensation for Compton scattering, uniform attenuation, and collimater response. This sinogram-space statistical approach has the potential to reconstruct high-resolution images at a very fast speed. Yet it provides the same image quality as iterative reconstruction algorithms did, in terms of abnormality detectability by observer studies.     

A Monte Carlo investigation of the spatial resolution performance of a small-animal PET scanner designed for mouse brain imaging studies

Our laboratory has developed PET detectors with depth-encoding accuracy of ∼2 mm based on finely pixelated crystals with a tapered geometry, readout at both ends with position-sensitive avalanche photodiodes (PSAPDs). These detectors are currently being used in our laboratory to build a one-ring high resolution PET scanner for mouse brain imaging studies. Due to the inactive areas around the PSAPDs, large gaps exist between the detector modules which can degrade the image spatial resolution obtained using analytical reconstruction with filtered backprojection (FBP). In this work, the Geant4-based GATE Monte Carlo package was used to assist in determining whether gantry rotation was necessary and to assess the expected spatial resolution of the system. The following factors were investigated: rotating vs. static gantry modes with and without compensation of missing data using the discrete cosine transform (DCT) method, two levels of depth-encoding, and positron annihilation effects for ¹⁸F. Our results indicate that while the static scanner produces poor quality FBP images with streak and ring artifacts, the image quality was greatly improved after compensation of missing data. The simulation indicates that the expected FWHM system spatial resolution is 0.70 ± 0.05 mm, which approaches the predicted limit of 0.5 mm FWHM due to positron range, photon non-colinearity and physical detector element size effects. We conclude that excellent reconstructed resolution without gantry rotation is possible even using FBP if the gaps are appropriately handled and that this design can approach the resolution limits set by positron annihilation physics.     

The 2.3-angstrom structure of porcine circovirus 2

Porcine circovirus 2 (PCV2) is a T=1 nonenveloped icosahedral virus that has had severe impact on the swine industry. Here we report the crystal structure of an N-terminally truncated PCV2 virus-like particle at 2.3-Å resolution, and the cryo-electron microscopy (cryo-EM) image reconstruction of a full-length PCV2 virus-like particle at 9.6-Å resolution. This is the first atomic structure of a circovirus. The crystal structure revealed that the capsid protein fold is a canonical viral jelly roll. The loops connecting the strands of the jelly roll define the limited features of the surface. Sulfate ions interacting with the surface and electrostatic potential calculations strongly suggest a heparan sulfate binding site that allows PCV2 to gain entry into the cell. The crystal structure also allowed previously determined epitopes of the capsid to be visualized. The cryo-EM image reconstruction showed that the location of the N terminus, absent in the crystal structure, is inside the capsid. As the N terminus was previously shown to be antigenic, it may externalize through viral “breathing.”     

Comparison of Deconvolution Filters for Photoacoustic Tomography

In this work, we compare the merits of three temporal data deconvolution methods for use in the filtered backprojection algorithm for photoacoustic tomography (PAT). We evaluate the standard Fourier division technique, the Wiener deconvolution filter, and a Tikhonov L-2 norm regularized matrix inversion method. Our experiments were carried out on subjects of various appearances, namely a pencil lead, two man-made phantoms, an in vivo subcutaneous mouse tumor model, and a perfused and excised mouse brain. All subjects were scanned using an imaging system with a rotatable hemispherical bowl, into which 128 ultrasound transducer elements were embedded in a spiral pattern. We characterized the frequency response of each deconvolution method, compared the final image quality achieved by each deconvolution technique, and evaluated each method’s robustness to noise. The frequency response was quantified by measuring the accuracy with which each filter recovered the ideal flat frequency spectrum of an experimentally measured impulse response. Image quality under the various scenarios was quantified by computing noise versus resolution curves for a point source phantom, as well as the full width at half maximum (FWHM) and contrast-to-noise ratio (CNR) of selected image features such as dots and linear structures in additional imaging subjects. It was found that the Tikhonov filter yielded the most accurate balance of lower and higher frequency content (as measured by comparing the spectra of deconvolved impulse response signals to the ideal flat frequency spectrum), achieved a competitive image resolution and contrast-to-noise ratio, and yielded the greatest robustness to noise. While the Wiener filter achieved a similar image resolution, it tended to underrepresent the lower frequency content of the deconvolved signals, and hence of the reconstructed images after backprojection. In addition, its robustness to noise was poorer than that of the Tikhonov filter. The performance of the Fourier filter was found to be the poorest of all three methods, based on the reconstructed images’ lowest resolution (blurriest appearance), generally lowest contrast-to-noise ratio, and lowest robustness to noise. Overall, the Tikhonov filter was deemed to produce the most desirable image reconstructions.     

Interpolated Compressed Sensing for 2D Multiple Slice Fast MR Imaging

Sparse MRI has been introduced to reduce the acquisition time and raw data size by undersampling the k-space data. However, the image quality, particularly the contrast to noise ratio (CNR), decreases with the undersampling rate. In this work, we proposed an interpolated Compressed Sensing (iCS) method to further enhance the imaging speed or reduce data size without significant sacrifice of image quality and CNR for multi-slice two-dimensional sparse MR imaging in humans. This method utilizes the k-space data of the neighboring slice in the multi-slice acquisition. The missing k-space data of a highly undersampled slice are estimated by using the raw data of its neighboring slice multiplied by a weighting function generated from low resolution full k-space reference images. In-vivo MR imaging in human feet has been used to investigate the feasibility and the performance of the proposed iCS method. The results show that by using the proposed iCS reconstruction method, the average image error can be reduced and the average CNR can be improved, compared with the conventional sparse MRI reconstruction at the same undersampling rate.     

Automated Identification of Filaments in Cryoelectron Microscopy Images

Since the foundation for the three-dimensional image reconstruction of helical objects from electron micrographs was laid more than 30 years ago, there have been sustained developments in specimen preparation, data acquisition, image analysis, and interpretation of results. However, the boxing of filaments in large numbers of images--one of the critical steps toward the reconstruction at high resolution--is still constrained by manual processing even though interactive interfaces have been built to aid the tedious and sometimes inaccurate boxing process. This article describes an accurate approach for automated detection of filamentous structures in low-contrast images acquired in defocus pairs using cryoelectron microscopy. The performance of the approach has been evaluated across various magnifications and at a series of defocus values using tobacco mosaic virus (TMV) preserved in vitreous ice as a test specimen. By integrating the proposed approach into our automated data acquisition and reconstruction system, we are now able to generate a three-dimensional map of TMV to approximately 10-A resolution within 24 h of inserting the specimen grid into the microscope.     

SPRING – An image processing package for single-particle based helical reconstruction from electron cryomicrographs

Helical reconstruction from electron cryomicrographs has become a routine technique for macromolecular structure determination of helical assemblies since the first days of Fourier-based three-dimensional image reconstruction. In the past decade, the single-particle technique has had an important impact on the advancement of helical reconstruction. Here, we present the software package SPRING that combines Fourier based symmetry analysis and real-space helical processing into a single workflow. One of the most time-consuming steps in helical reconstruction is the determination of the initial symmetry parameters. First, we propose a class-based helical reconstruction approach that enables the simultaneous exploration and evaluation of many symmetry combinations at low resolution. Second, multiple symmetry solutions can be further assessed and refined by single-particle based helical reconstruction using the correlation of simulated and experimental power spectra. Finally, the 3D structure can be determined to high resolution. In order to validate the procedure, we use the reference specimen Tobacco Mosaic Virus (TMV). After refinement of the helical symmetry, a total of 50,000 asymmetric units from two micrographs are sufficient to reconstruct a subnanometer 3D structure of TMV at 6.4 Å resolution. Furthermore, we introduce the individual programs of the software and discuss enhancements of the helical reconstruction workflow. Thanks to its user-friendly interface and documentation, SPRING can be utilized by the novice as well as the expert user. In addition to the study of well-ordered helical structures, the development of a streamlined workflow for single-particle based helical reconstruction opens new possibilities to analyze specimens that are heterogeneous in symmetries.