Zhanli Hu

Geometric Calibration of a Micro-CT System and Performance for Insect Imaging

Micro-CT with a high spatial resolution in combination with computer-based-reconstruction techniques is considered a powerful tool for morphological study of insects. The quality of CT images crucially depends on the precise knowledge of the scan geometry of the micro-CT system. In this paper, we have proposed a method to calculate the deviation of rotating axis for compensating deficiency of existing methods. A practical application of this geometric calibration method of the micro-CT system for insect imaging is presented. We have performed the computer-simulation study and experimental study with our prototype micro-CT system. The results demonstrate that the proposed technique is accurate and robust. In addition, we have evaluated the imaging characteristics of the detector in terms of modulation-transfer function (MTF). Finally, insect imaging performance and image reconstruction from data acquired with different energies are presented.

A Novel Interactive Image Processing Approach for DICOM Medical Image Data

The development of more flexible and accurate medical image processing technique and platform is important requirement for clinical diagnosis and treatment. A new interactive image processing method is proposed in this work. Using this method, image smoothing, sharpening, histogram processing, pseudo-color processing, segmentation, reading, local amplification and measurement for medical image in DICOM format can be realized. Application of the method for processing human CT image data demonstrated the method is a convenient and flexible approach for medical image processing. This user-friendly image processing technique could be clinically useful to assist image analysis and diagnosis. Keywords-DICOM; medical image; image processing; local amplification

Image reconstruction from few-view CT data by gradient-domain dictionary learning

BACKGROUND Decreasing the number of projections is an effective way to reduce the radiation dose exposed to patients in medical computed tomography (CT) imaging. However, incomplete projection data for CT reconstruction will result in artifacts and distortions. OBJECTIVE In this paper, a novel dictionary learning algorithm operating in the gradient-domain (Grad-DL) is proposed for few-view CT reconstruction. Specifically, the dictionaries are trained from the horizontal and vertical gradient images, respectively and the desired image is reconstructed subsequently from the sparse representations of both gradients by solving the least-square method. METHODS Since the gradient images are sparser than the image itself, the proposed approach could lead to sparser representations than conventional DL methods in the image-domain, and thus a better reconstruction quality is achieved. RESULTS To evaluate the proposed Grad-DL algorithm, both qualitative and quantitative studies were employed through computer simulations as well as real data experiments on fan-beam and cone-beam geometry. CONCLUSIONS The results show that the proposed algorithm can yield better images than the existing algorithms.

Investigation of BPF algorithm in cone-beam CT with 2D general trajectories

A mathematical derivation was conducted to illustrate that exact 3D image reconstruction could be achieved for z-homogeneous phantoms from data acquired with 2D general trajectories using the back projection filtration (BPF) algorithm. The conclusion was verified by computer simulation and experimental result with a circular scanning trajectory. Furthermore, the effect of the non-uniform degree along z-axis of the phantoms on the accuracy of the 3D reconstruction by BPF algorithm was investigated by numerical simulation with a gradual-phantom and a disk-phantom. The preliminary result showed that the performance of BPF algorithm improved with the z-axis homogeneity of the scanned object.

Performance of a high-resolution depth encoding PET detector using barium sulfate reflector

Small animal positron emission tomography (PET) is a well-established imaging modality in preclinical biomedical research. The performance of current small animal PET scanners is mainly limited by the detector performance and depth-encoding detectors are required to simultaneously achieve high spatial resolution and high sensitivity. In this work, the performance of a high-resolution dual-ended readout lutetium-yttrium oxyorthosilicate (LYSO) array using barium sulfate powder (BaSO4) as the inter-crystal reflector was measured for the first time and compared to that of a LYSO array using the most commonly used enhanced specular reflector (ESR). Both LYSO arrays have 18  ×  18 crystals and the crystal size is about 0.62  ×  0.62  ×  20 mm3. The LYSO arrays are readout by two position-sensitive photomultiplier tubes (PSPMTs) from both ends. The flood histograms, energy resolution, depth of interaction (DOI) resolution and timing resolution were measured. The flood histograms of the LYSO array with BaSO4 reflector is much better than that of the LYSO array with ESR reflector. For the BaSO4 array, all crystals can be clearly resolved. For the ESR array, all crystals in one direction can be clearly resolved, but the edge 2-3 columns of the crystals in the other direction cannot be resolved. The average energy resolution of the BaSO4 and ESR arrays are 15.2% and 15.3%, respectively. The average DOI resolution of the BaSO4 array is 2.19 mm, which is 24% worse than the 1.76 mm DOI resolution of the ESR array. The timing resolution of both arrays is ~1.6 ns. The LYSO array with the new BaSO4 reflector provided an much better flood histogram in a high resolution dual-ended readout PET detectors as compared to the ESR array, and will be used to develop a small animal PET scanner that can simultaneously achieve uniform high spatial resolution, high sensitivity and low cost.

A feature refinement approach for statistical interior CT reconstruction.

Interior tomography is clinically desired to reduce the radiation dose rendered to patients. In this work, a new statistical interior tomography approach for computed tomography is proposed. The developed design focuses on taking into account the statistical nature of local projection data and recovering fine structures which are lost in the conventional total-variation (TV)-minimization reconstruction. The proposed method falls within the compressed sensing framework of TV minimization, which only assumes that the interior ROI is piecewise constant or polynomial and does not need any additional prior knowledge. To integrate the statistical distribution property of projection data, the objective function is built under the criteria of penalized weighed least-square (PWLS-TV). In the implementation of the proposed method, the interior projection extrapolation based FBP reconstruction is first used as the initial guess to mitigate truncation artifacts and also provide an extended field-of-view. Moreover, an interior feature refinement step, as an important processing operation is performed after each iteration of PWLS-TV to recover the desired structure information which is lost during the TV minimization. Here, a feature descriptor is specifically designed and employed to distinguish structure from noise and noise-like artifacts. A modified steepest descent algorithm is adopted to minimize the associated objective function. The proposed method is applied to both digital phantom and in vivo Micro-CT datasets, and compared to FBP, ART-TV and PWLS-TV. The reconstruction results demonstrate that the proposed method performs better than other conventional methods in suppressing noise, reducing truncated and streak artifacts, and preserving features. The proposed approach demonstrates its potential usefulness for feature preservation of interior tomography under truncated projection measurements.

Geant4-Based Monte Carlo Simulator for Fan-and Cone-Beam X-ray CT

An X-ray computed tomography (CT) simulator based on Geant4 toolkit was developed for simulation of both fan- and cone-beam CT scanners. There major components X-ray tube, phantom and detector are simulated. Different to analytical simulation, this simulation is accordance with imaging physics by adopting the Geant4 toolkit. Compared with ordinary statistical simulation, acceleration investigation and parameters optimization are considered. Also,experiments on beam-hardening and scatter are simulated. As expected, this simulator is not only a tool for acquiring scanning data, but also a tool for evaluating dose, the effect of physical, geometrical and potential artifacts and corresponding correction schemes in CT system.

MEASUREMENT OF TWO-PROTON CORRELATION FROM THE BREAK-UP OF (23)Al

Experiments of 23Al and 22Mg radioactive beams bombarding a 12C target at an energy of 60 ~70 A MeV have been performed at the projectile fragment separator beamline (RIPS) in the RIKEN Ring Cyclotron Facility to study the two-proton emission from 23Al and 22Mg excited states, respectively. The trajectorie of the decay products, namely 21Na + p + p from 23Al and 20Ne + p + p from 22Mg, are clean identified. The relative momentum and opening angle between two protons in the rest frame of three body decay channels are obtained by relativistic-kinematics reconstruction. The results demonstrate that there are some di-proton emission components from 2He cluster for the excited 23Al and 22Mg.

Super-resolution CT Image Reconstruction Based on Dictionary Learning and Sparse Representation

In this paper, a single-computed tomography (CT) image super-resolution (SR) reconstruction scheme is proposed. This SR reconstruction scheme is based on sparse representation theory and dictionary learning of low- and high-resolution image patch pairs to improve the poor quality of low-resolution CT images obtained in clinical practice using low-dose CT technology. The proposed strategy is based on the idea that image patches can be well represented by sparse coding of elements from an overcomplete dictionary. To obtain similarity of the sparse representations, two dictionaries of low- and high-resolution image patches are jointly trained. Then, sparse representation coefficients extracted from the low-resolution input patches are used to reconstruct the high-resolution output. Sparse representation is used such that the trained dictionary pair can reduce computational costs. Combined with several appropriate iteration operations, the reconstructed high-resolution image can attain better image quality. The effectiveness of the proposed method is demonstrated using both clinical CT data and simulation image data. Image quality evaluation indexes (root mean squared error (RMSE) and peak signal-to-noise ratio (PSNR)) indicate that the proposed method can effectively improve the resolution of a single CT image.

Performance of a SiPM based semi-monolithic scintillator PET detector

A depth encoding PET detector module using semi-monolithic scintillation crystal single-ended readout by a SiPM array was built and its performance was measured. The semi-monolithic scintillator detector consists of 11 polished LYSO slices measuring 1  ×  11.6  ×  10 mm3. The slices are glued together with enhanced specular reflector (ESR) in between and outside of the slices. The bottom surface of the slices is coupled to a 4  ×  4 SiPM array with a 1 mm light guide and silicon grease between them. No reflector is used on the top surface and two sides of the slices to reduce the scintillation photon reflection. The signals of the 4  ×  4 SiPM array are grouped along rows and columns separately into eight signals. Four SiPM column signals are used to identify the slices according to the center of the gravity of the scintillation photon distribution in the pixelated direction. Four SiPM row signals are used to estimate the y (monolithic direction) and z (depth of interaction) positions according to the center of the gravity and the width of the scintillation photon distribution in the monolithic direction, respectively. The detector was measured with 1 mm sampling interval in both the y and z directions with electronic collimation by using a 0.25 mm diameter 22Na point source and a 1  ×  1  ×  20 mm3 LYSO crystal detector. An average slice based energy resolution of 14.9% was obtained. All slices of 1 mm thick were clearly resolved and a detector with even thinner slices could be used. The y positions calculated with the center of gravity method are different for interactions happening at the same y, but different z positions due to depth dependent edge effects. The least-square minimization and the maximum likelihood positioning algorithms were developed and both methods improved the spatial resolution at the edges of the detector as compared with the center of gravity method. A mean absolute error (MAE) which is defined as the probability-weighted mean of the absolute value of the positioning error is used to evaluate the spatial resolution. An average MAE spatial resolution of ~1.15 mm was obtained in both y and z directions without rejection of the multiple scattering events. The average MAE spatial resolution was ~0.7 mm in both y and z directions after the multiple scattering events were rejected. The timing resolution of the detector is 575 ps. In the next step, long rectangle detector will be built to reduce edge effects and improve the spatial resolution of the semi-monolithic detector. Thick detector up to 20 mm will be explored and the positioning algorithms will be further optimized.