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Low-dose X-ray computed tomography (CT) simulation from a high-dose scan is required in optimizing radiation dose to patients. In this paper, we propose a simple low-dose CT simulation strategy in the sinogram domain using the raw data from high-dose scan. Specially, a relationship between the incident fluxes of low- and high-dose scans is first determined according to the repeated projection measurements and analysis. Second, the incident flux level of the simulated low-dose scan is generated by properly scaling the incident flux level of the high-dose scan via the determined relationship in the first step. Third, the low-dose CT transmission data by energy integrating detection is simulated by adding a statistically independent Poisson noise distribution plus a statistically independent Gaussian noise distribution. Finally, a filtered back-projection (FBP) algorithm is implemented to reconstruct the resultant low-dose CT images. The present low-dose simulation strategy is verified on the simulations and real scans by comparing it with the existing low-dose CT simulation tool. Experimental results demonstrated that the present low-dose CT simulation strategy can generate accurate low-dose CT sinogram data from high-dose scans in terms of qualitative and quantitative measurements.

Helical computed tomography (HCT) has several advantages over conventional step-and-shoot CT for imaging a relatively large object, especially for dynamic studies. However, HCT may increase X-ray exposure significantly. This work aims to reduce the radiation by lowering X-ray tube current (mA) and filtering low-mA (or dose) sinogram noise of HCT. The noise reduction method is based on three observations on HCT: (1) the axial sampling of HCT projections is nearly continuous as detection system rotates; (2) the noise distribution in sinogram space is nearly a Gaussian after system calibration (including logarithmic transform); and (3) the relationship between the calibrated data mean and variance can be expressed as an exponential functional across the field-of-view. Based on the second and third observations, a penalized weighted least-squares (PWLS) solution is an optimal choice, where the weight is given by the mean-variance relationship. The first observation encourages the use of Karhunen-Loève (KL) transform along the axial direction because of the associated correlation. In the KL domain, the eigenvalue of each principal component and the derived data variance provide the signal-to-noise ratio (SNR) information, resulting in a SNR-adaptive noise reduction. The KL-PWLS noise-reduction method was implemented analytically for efficient restoration of large volume HCT sinograms. Simulation studies showed a noticeable improvement, in terms of image quality and defect detectability, of the proposed noise-reduction method over the Ordered-Subsets Expectation-Maximization reconstruction and the conventional low-pass noise filtering with optimal cutoff frequency and/or other filter parameters.

When excessive quantum noise is present in extremely low dose X-ray CT imaging, statistical properties of the data has to be considered to achieve a satisfactory image reconstruction. Statistical iterative reconstruction with accurate modeling of the noise, rather than a filtered back-projection (FBP) with low-pass filtering, is one way to deal with the problem. Estimating a noise-free sinogram to satisfy the FBP reconstruction for the Radon transform is another way. The benefits of the latter include a higher computation efficiency, more uniform spatial resolution in the reconstructed image, and less modification of the current machine configurations. In a clinic X-ray CT system, the acquired raw data must be calibrated, in addition to the logarithmic transform, to achieve the high diagnostic image quality. The calibrated projection data or sinogram no longer follow a compound Poisson distribution in general, but are close to a Gaussian distribution with signal-dependent variance. In this paper, we first investigated a relatively accurate statistical model for the sinogram data, based on several phantom experiments. Then we developed a penalized likelihood method to smooth the sinogram, which led to a set of nonlinear equations that can be solved by iterated conditional mode (ICM) algorithm within a reasonable computing time. The method was applied to several experimental datasets acquired at 120 kVp, 10 mA/20 mA/50 mA protocols with a GE HiSpeed multi-slice detector CT scanner and demonstrated a significant noise suppression without noticeable sacrifice of the spatial resolution.

A major task in quantitative SPECT (single photon emission computed tomography) reconstruction is compensation for object-specific attenuation, which is usually nonuniform. Mathematically this task is expressed as the inversion of the attenuated Radon transform. Novikov had derived an explicit inversion formula for the attenuated Radon transform for parallel-beam collimation geometry. In our previous work, we extended his work to variable focusing fan-beam (VFF) collimators. A ray-driven analytical inversion formula for VFF reconstruction with nonuniform attenuation was derived. The drawback of ray-driven methods is that they are time consuming. In this work, we proposed a fast implementation method, which includes algorithm optimization and acceleration by the texture-mapping architecture of PC graphics/video card. We further investigated the noise properties and associated artifacts of the analytical inversion formula. The artifacts were remarkably reduced when more projections were sampled to mitigate the problem of wide bandwidth of the discrete Hilbert transform. The reconstruction from noisy data demonstrated the accuracy and robustness of the presented ray-driven analytical inversion formula with dramatic speed acceleration by the PC graphics/video card.

Proton computed tomography (pCT) has the potential to improve the accuracy of dose calculations for proton treatment planning, and will also be useful for pretreatment verification of patient positioning relative to the proton beam. A design study was performed to define the optimal approach to a pCT system based on specifications for applications in proton therapy. Conceptual and detailed design of a pCT system is presented; the system consists of a silicon-based particle tracking system and a crystal calorimeter to measure energy loss of individual protons. We discuss the formation of pCT images based on the reconstruction of volume electron density maps and the suitability of analytic and statistical algorithms for image reconstruction.

We present an automatic mixture-based algorithm for segmentation of brain tissues (white and gray matters-WM and GM), cerebral spinal fluid (CSF), and brain lesions to quantitatively analyze multiple sclerosis. The method performs intensity-based tissue classification using multispectral magnetic resonance (MR) images based on a stochastic model. With the existence of white Gaussian noise and spatially invariant blurring in acquired MR images, a Karhunen-Loeve (K-L) domain Wiener filter is applied for accurate noise reduction and resolution restoration on blurred and noisy images to minimize the partial volume effect (PVE), which is a major limiting factor for the quantitative analysis. Following that, we utilize a Markov random field Gibbs model to integrate the local spatial information into the well-established expectation-maximization model-fitting algorithm. Each voxel is then classified by a maximum a posterior (MAP) criterion, indicating its probabilities of belonging to each class, i.e., each voxel is labeled as a mixel with different tissue percentages, leading to further minimization of the PVE. The volumes of WM, GM, CSF, and brain lesions are extracted from the mixture-based segmentation and the corresponding brain atrophies are computed. In this study, we have investigated the accuracy and repeatability of the algorithm with inclusion of noise analysis and point spread function for image resolution enhancement. Experimental results on phantom, healthy volunteer, and patient studies are presented.

Single photon emission computed tomography (SPECT) is based on the measurement of radiation emitted by a radiotracer injected into the patient. Because of photoelectric absorption and Compton scatter, the gamma photons are attenuated inside the body before arriving at the detector. A quantitative reconstruction must consider the attenuation, which is usually nonuniform. Novikov has derived an explicit inversion formula for the nonuniformly attenuated Radon transform for SPECT reconstruction of parallel-beam collimated projections. In this paper, we extend his research to variable focal-length fan-beam VFF collimator geometry. A ray-driven analytical formula for VFF reconstruction with nonuniform attenuation was derived. As a unified framework, this formula can be used for parallel-beam, fan-beam, and VFF collimators. Its accuracy is demonstrated by computer simulation experiments.

The authors present a new method to compensate for the spatially variant detector response in single photon emission computed tomography (SPECT) with varying focal-length fan-beam (VFF) geometry. In this method, the deblurred projection is explicitly expressed as an integral of the collimator blurred data and the fully calibrated system kernel matrix with certain transforms. It needs a little more computation time than the conventional filtering techniques but provides much more accurate compensation. This method also represents a unified framework for detector response compensation in parallel-hole fan-beam and VFF SPECT with a circular scan orbit.

Spatial resolution variation as a function of distance from collimator surface in single photon emission computed tomography (SPECT) is a major obstacle for quantitative imaging. This work investigated two analytical inversion methods for correcting the distance-dependent resolution variation, as well as uniform attenuation in brain SPECT studies. The first one utilizes an accurately measured resolution variation kernel, but derives an approximated inversion formula. The second one derives an accurate inversion formula, but approximates the resolution variation kernel. Reconstructed images using the first method showed a better resolution recovery at the periphery of field-of-view (FOV), consistent with the theory that the inversion formula is approximated for far-field regions. The second method restored resolution better at the central area of FOV, consistent with the approach that the resolution kernel is approximated for near-field regions. The second method is more sensitive to the approximation. The first one is more robust to the approximation and, therefore, can be a better choice for quantitative SPECT imaging.

Virtual colonoscopy has been developed as a noninvasive computerized medical procedure for examining the entire colon to detect polyps. Our group has successfully implemented an interactive navigation using a physically based camera control model and a hardware-assisted visibility algorithm. Unfortunately, the pre-processing (skeleton and potential field generations) consumes too much computing time to be acceptable in an interactive clinical environment. In this work, we focus on skeleton simplification by eliminating excessive inconsistent branches. The simplification is performed before skeleton generation. The idea is to utilize a hierarchical analysis to determine principal points for the skeleton. We also propose dynamic target navigation for virtual endoscopy. We combine potentials derived from the distance between source and target positions and from the distance between source and colon surface to guide path search processes. These potentials are utilized to assign priority in choosing the paths far away from colon wall and in the direction of target positions.

Presents an innovative technology called 3D virtual colonoscopy, which incorporates several advanced visualization techniques to enable the physician to virtually examine the inner surface of the entire colon for identifying and inspecting colonic polyps. The authors first describe their unique process of acquiring and reconstructing a patient's colon model, followed by the novel visualization algorithms that they have developed to provide automatic planned navigations as well as interactive guided navigations inside the colon. Finally, the authors present their experimental results on a simulated pipe phantom, the Visible Human data set, and patient data.

The expectation maximization (EM) algorithm for the maximum likelihood (ML) image reconstruction criterion generates severe checkerboard artifacts in the presence of noise. A classical remedy is to impose an a priori constraint for a penalized ML or maximum a posteriori probability solution. The penalty reduces the checkerboard artifacts and also introduces uncertainty because a priori information is usually unknown in clinic. Recent theoretical investigation reveals that the noise can be divided into two components: one is called null-space noise and the other is range-space noise. The null-space noise can be numerically estimated using filtered backprojection (FBP) algorithm. By the FBP algorithm, the null-space noise annihilates in the reconstruction while the range-space noise propagates into the reconstructed image. The aim of this work is to investigate the relation between the null-space noise and the checkerboard artifacts in the ML-EM reconstruction from noisy projection data. Our study suggests that removing the null-space noise from the projection data could improve the signal-to-noise ratio of the projection data and, therefore, reduce the checkerboard artifacts in the ML-EM reconstructed images. This study reveals an in-depth understanding of the different noise propagations in analytical and iterative image reconstructions, which may be useful to single photon emission computed tomography, where the noise has been a major factor for image degradation. The reduction of the ML-EM checkerboard artifacts by removing the null-space noise avoids the uncertainty of using a priori penalty.

The authors compare analytically techniques for multiple-photon coincidence imaging in terms of spatial resolution, detector efficiency (or simply efficiency), and system sensitivity for a spherical water phantom (or simply sensitivity). One analysis consists of comparing photon-photon coincidence single-photon-emission computerized tomography (PP-SPECT) with the angularly unconstrained, electronically collimated triple-photon coincidence imaging technique (TPCIT). A second analysis compares positron-emission tomography using time-of-flight information (TOF-PET) with angularly constrained, electronically collimated TPCIT, The angularly unconstrained TPCIT has a similar spatial resolution, higher efficiency, and higher sensitivity when compared to PP-SPECT. The angularly constrained TPCIT has similar spatial resolution, lower efficiency, and lower sensitivity when compared to TOF-PET. However, the angularly constrained TPCIT can, for brain imaging, reduce the localization range of photon-emission sites along projection rays from about 20 cm to about 1 cm during data acquisition, while TOF-PET currently reduces the range to about 7 cm. >