Data consistency based translational motion artifact reduction in fan-beam CT

MRCON-Net: Multiscale reweighted convolutional coding neural network for low-dose CT imaging

BACKGROUND AND OBJECTIVE

Low-dose computed tomography (LDCT) has become increasingly important for alleviating X-ray radiation damage. However, reducing the administered radiation dose may lead to degraded CT images with amplified mottle noise and nonstationary streak artifacts. Previous studies have confirmed that deep learning (DL) is promising for improving LDCT imaging. However, most DL-based frameworks are built intuitively, lack interpretability, and suffer from image detail information loss, which has become a general challenging issue.

METHODS

A multiscale reweighted convolutional coding neural network (MRCON-Net) is developed to address the above problems. MRCON-Net is compact and more explainable than other networks. First, inspired by the learning-based reweighted iterative soft thresholding algorithm (ISTA), we extend traditional convolutional sparse coding (CSC) to its reweighted convolutional learning form. Second, we use dilated convolution to extract multiscale image features, allowing our single model to capture the correlations between features of different scales. Finally, to automatically adjust the elements in the feature code to correct the obtained solution, a channel attention (CA) mechanism is utilized to learn appropriate weights.

RESULTS

The visual results obtained based on the American Association of Physicians in Medicine (AAPM) Challenge and United Image Healthcare (UIH) clinical datasets confirm that the proposed model significantly reduces serious artifact noise while retaining the desired structures. Quantitative results show that the average structural similarity index measurement (SSIM) and peak signal-to-noise ratio (PSNR) achieved on the AAPM Challenge dataset are 0.9491 and 40.66, respectively, and the SSIM and PSNR achieved on the UIH clinical dataset are 0.915 and 42.44, respectively; these are promising quantitative results.

CONCLUSION

Compared with recent state-of-the-art methods, the proposed model achieves subtle structure-enhanced LDCT imaging. In addition, through ablation studies, the components of the proposed model are validated to achieve performance improvements.

Estimating the Center of Rotation of Tomographic Imaging Systems with a Limited Number of Projections

For a tomographic imaging system, image reconstruction quality is dependent on the accurate determination of coordinates for the true center of rotation (COR). A significant COR offset error may introduce ringing, streaking, or other artifacts, while smaller error in determining COR may blur the reconstructed image. Well known COR correction techniques including image registration, center of mass calculation, or reconstruction evaluation work well under certain conditions. However, many of these methods do not consider various real-world cases such as a tilted sensor or non-parallel projections. Furthermore, a limited number of projections introduces stripe artifacts into the image reconstruction that interfere with many of these classic COR correction techniques. In this paper, we propose a revised variance-based algorithm to find the correct COR position automatically prior to tomographic reconstruction. The algorithm was tested on both simulated phantoms and acquired datasets, and our results show improved reconstruction accuracy.

Motion estimation and correction in SPECT, PET and CT

Patient motion impacts single photon emission computed tomography (SPECT), positron emission tomography (PET) and X-ray computed tomography (CT) by giving rise to projection data inconsistencies that can manifest as reconstruction artifacts, thereby degrading image quality and compromising accurate image interpretation and quantification. Methods to estimate and correct for patient motion in SPECT, PET and CT have attracted considerable research effort over several decades. The aims of this effort have been two-fold: to estimate relevant motion fields characterizing the various forms of voluntary and involuntary motion; and to apply these motion fields within a modified reconstruction framework to obtain motion-corrected images. The aims of this review are to outline the motion problem in medical imaging and to critically review published methods for estimating and correcting for the relevant motion fields in clinical and preclinical SPECT, PET and CT. Despite many similarities in how motion is handled between these modalities, utility and applications vary based on differences in temporal and spatial resolution. Technical feasibility has been demonstrated in each modality for both rigid and non-rigid motion, but clinical feasibility remains an important target. There is considerable scope for further developments in motion estimation and correction, and particularly in data-driven methods that will aid clinical utility. State-of-the-art machine learning methods may have a unique role to play in this context.

Rigid and non-rigid motion artifact reduction in X-ray CT using attention module

Motion artifacts are a major factor that can degrade the diagnostic performance of computed tomography (CT) images. In particular, the motion artifacts become considerably more severe when an imaging system requires a long scan time such as in dental CT or cone-beam CT (CBCT) applications, where patients generate rigid and non-rigid motions. To address this problem, we proposed a new real-time technique for motion artifacts reduction that utilizes a deep residual network with an attention module. Our attention module was designed to increase the model capacity by amplifying or attenuating the residual features according to their importance. We trained and evaluated the network by creating four benchmark datasets with rigid motions or with both rigid and non-rigid motions under a step-and-shoot fan-beam CT (FBCT) or a CBCT. Each dataset provided a set of motion-corrupted CT images and their ground-truth CT image pairs. The strong modeling power of the proposed network model allowed us to successfully handle motion artifacts from the two CT systems under various motion scenarios in real-time. As a result, the proposed model demonstrated clear performance benefits. In addition, we compared our model with Wasserstein generative adversarial network (WGAN)-based models and a deep residual network (DRN)-based model, which are one of the most powerful techniques for CT denoising and natural RGB image deblurring, respectively. Based on the extensive analysis and comparisons using four benchmark datasets, we confirmed that our model outperformed the aforementioned competitors. Our benchmark datasets and implementation code are available at https://github.com/youngjun-ko/ct_mar_attention.

Spectrum Estimation-Guided Iterative Reconstruction Algorithm for Dual Energy CT

X-ray spectrum plays a very important role in dual energy computed tomography (DECT) reconstruction. Because it is difficult to measure x-ray spectrum directly in practice, efforts have been devoted into spectrum estimation by using transmission measurements. These measurement methods are independent of the image reconstruction, which bring extra cost and are time consuming. Furthermore, the estimated spectrum mismatch would degrade the quality of the reconstructed images. In this paper, we propose a spectrum estimation-guided iterative reconstruction algorithm for DECT which aims to simultaneously recover the spectrum and reconstruct the image. The proposed algorithm is formulated as an optimization framework combining spectrum estimation based on model spectra representation, image reconstruction, and regularization for noise suppression. To resolve the multi-variable optimization problem of simultaneously obtaining the spectra and images, we introduce the block coordinate descent (BCD) method into the optimization iteration. Both the numerical simulations and physical phantom experiments are performed to verify and evaluate the proposed method. The experimental results validate the accuracy of the estimated spectra and reconstructed images under different noise levels. The proposed method obtains a better image quality compared with the reconstructed images from the known exact spectra and is robust in noisy data applications.

Optimization based beam-hardening correction in CT under data integral invariant constraint

In computed tomography (CT), the polychromatic characteristics of x-ray photons, which are emitted from a source, interact with materials and are absorbed by a detector, may lead to beam-hardening effect in signal detection and image formation, especially in situations where materials of high attenuation (e.g. the bone or metal implants) are in the x-ray beam. Usually, a beam-hardening correction (BHC) method is used to suppress the artifacts induced by bone or other objects of high attenuation, in which a calibration-oriented iterative operation is carried out to determine a set of parameters for all situations. Based on the Helgasson-Ludwig consistency condition (HLCC), an optimization based method has been proposed by turning the calibration-oriented iterative operation of BHC into solving an optimization problem sustained by projection data. However, the optimization based HLCC-BHC method demands the engagement of a large number of neighboring projection views acquired at relatively high and uniform angular sampling rate, hindering its application in situations where the angular sampling in projection view is sparse or non-uniform. By defining an objective function based on the data integral invariant constraint (DIIC), we again turn BHC into solving an optimization problem sustained by projection data. As it only needs a pair of projection views at any view angle, the proposed BHC method can be applicable in the challenging scenarios mentioned above. Using the projection data simulated by computer, we evaluate and verify the proposed optimization based DIIC-BHC method's performance. Moreover, with the projection data of a head scan by a multi-detector row MDCT, we show the proposed DIIC-BHC method's utility in clinical applications.

Correction of center of rotation and projection angle in synchrotron X-ray computed tomography

An error in tomographic reconstruction parameters can result considerable artifacts in the reconstructed image, particularly in micro-computed tomography and nano-computed tomography. This study involved designing an automatic method for efficiently correcting errors resulting from incorrectly determined rotational axes and projection angles. In this method, errors are corrected by minimizing the “total variation” of a reconstructed image, and minimization is accomplished by using the gradient descent method. Compared with two previous methods, the proposed method achieved the best reconstruction results.

Geometry and energy constrained projection extension

BACKGROUND

In clinical computed tomography (CT) applications, when a patient is obese or improperly positioned, the final tomographic scan is often partially truncated. Images directly reconstructed by the conventional reconstruction algorithms suffer from severe cupping and direct current bias artifacts. Moreover, the current methods for projection extension have limitations that preclude incorporation from clinical workflows, such as prohibitive computational time for iterative reconstruction, extra radiation dose, hardware modification, etc.METHOD:In this study, we first established a geometrical constraint and estimated the patient habitus using a modified scout configuration. Then, we established an energy constraint using the integral invariance of fan-beam projections. Two constraints were extracted from the existing CT scan process with minimal modification to the clinical workflows. Finally, we developed a novel dual-constraint based optimization model that can be rapidly solved for projection extrapolation and accurate local reconstruction.

RESULTS

Both numerical phantom and realistic patient image simulations were performed, and the results confirmed the effectiveness of our proposed approach.

CONCLUSION

We establish a dual-constraint-based optimization model and correspondingly develop an accurate extrapolation method for partially truncated projections. The proposed method can be readily integrated into the clinical workflow and efficiently solved by using a one-dimensional optimization algorithm. Moreover, it is robust for noisy cases with various truncations and can be further accelerated by GPU based parallel computing.

Discriminative Feature Representation to Improve Projection Data Inconsistency for Low Dose CT Imaging

In low dose computed tomography (LDCT) imaging, the data inconsistency of measured noisy projections can significantly deteriorate reconstruction images. To deal with this problem, we propose here a new sinogram restoration approach, the sinogram- discriminative feature representation (S-DFR) method. Different from other sinogram restoration methods, the proposed method works through a 3-D representation-based feature decomposition of the projected attenuation component and the noise component using a well-designed composite dictionary containing atoms with discriminative features. This method can be easily implemented with good robustness in parameter setting. Its comparison to other competing methods through experiments on simulated and real data demonstrated that the S-DFR method offers a sound alternative in LDCT.

Motion compensation for cone-beam CT using Fourier consistency conditions

In cone-beam CT, involuntary patient motion and inaccurate or irreproducible scanner motion substantially degrades image quality. To avoid artifacts this motion needs to be estimated and compensated during image reconstruction. In previous work we showed that Fourier consistency conditions (FCC) can be used in fan-beam CT to estimate motion in the sinogram domain. This work extends the FCC to [Formula: see text] cone-beam CT. We derive an efficient cost function to compensate for [Formula: see text] motion using [Formula: see text] detector translations. The extended FCC method have been tested with five translational motion patterns, using a challenging numerical phantom. We evaluated the root-mean-square-error and the structural-similarity-index between motion corrected and motion-free reconstructions. Additionally, we computed the mean-absolute-difference (MAD) between the estimated and the ground-truth motion. The practical applicability of the method is demonstrated by application to respiratory motion estimation in rotational angiography, but also to motion correction for weight-bearing imaging of knees. Where the latter makes use of a specifically modified FCC version which is robust to axial truncation. The results show a great reduction of motion artifacts. Accurate estimation results were achieved with a maximum MAD value of 708 μm and 1184 μm for motion along the vertical and horizontal detector direction, respectively. The image quality of reconstructions obtained with the proposed method is close to that of motion corrected reconstructions based on the ground-truth motion. Simulations using noise-free and noisy data demonstrate that FCC are robust to noise. Even high-frequency motion was accurately estimated leading to a considerable reduction of streaking artifacts. The method is purely image-based and therefore independent of any auxiliary data.

An Improved Extrapolation Scheme for Truncated CT Data Using 2D Fourier-Based Helgason-Ludwig Consistency Conditions

We improve data extrapolation for truncated computed tomography (CT) projections by using Helgason-Ludwig (HL) consistency conditions that mathematically describe the overlap of information between projections. First, we theoretically derive a 2D Fourier representation of the HL consistency conditions from their original formulation (projection moment theorem), for both parallel-beam and fan-beam imaging geometry. The derivation result indicates that there is a zero energy region forming a double-wedge shape in 2D Fourier domain. This observation is also referred to as the Fourier property of a sinogram in the previous literature. The major benefit of this representation is that the consistency conditions can be efficiently evaluated via 2D fast Fourier transform (FFT). Then, we suggest a method that extrapolates the truncated projections with data from a uniform ellipse of which the parameters are determined by optimizing these consistency conditions. The forward projection of the optimized ellipse can be used to complete the truncation data. The proposed algorithm is evaluated using simulated data and reprojections of clinical data. Results show that the root mean square error (RMSE) is reduced substantially, compared to a state-of-the-art extrapolation method.

Translational motion correction algorithm for truncated cone-beam CT using opposite projections

BACKGROUND

Cone-beam computed tomography (CBCT) is widely used in various medical imaging applications, including dental examinations. Dental CBCT images often suffer from motion artifacts caused by involuntary rigid motion of patients. However, earlier motion compensation studies are not applicable for dental CBCT systems using truncated detectors.

OBJECTIVE

This study proposes a novel motion correction algorithm that can be applied for truncated dental CBCT images.

METHODS

We propose a two-step method for motion correction. First, we estimate the relative displacement of each pair of opposite projections by finding the motion vector that maximizes the two-dimensional correlation coefficients of the opposite projections. Second, we convert the relative displacement into the absolute coordinate motion that yields the highest image sharpness of the reconstruction image. Using the motion vectors in the absolute coordinate system, motion artifacts are then compensated by modifying the trajectory of the source and detector during the back-projection step of the image reconstruction process.

RESULTS

In simulation, the proposed method successfully estimated the true relative displacement. After converting to the absolute coordinate motions, the motion-compensated image was close to the ground-truth image and exhibited a lower mean-square-error than that of the uncompensated image. The results from the real data experiment also confirmed that the proposed method successfully compensated for the motion artifacts.

CONCLUSIONS

The experimental results confirmed that the proposed method was applicable to most dental CBCT systems using a truncated detector without any use of an additional motion tracking system nor prior knowledge.

Rigid motion artifact reduction in CT using frequency domain analysis

BACKGROUND

It is often unrealistic to assume that the subject remains stationary during a computed tomography (CT) imaging scan. A patient rigid motion can be decomposed into a translation and a rotation around an origin. How to minimize the motion impact on image quality is important.

OBJECTIVE

To eliminate artifacts caused by patient rigid motion during a CT scan, this study investigated a new method based on frequency domain analysis to estimate and compensate motion impact.

METHODS

Motion parameters was first determined by the magnitude correlation of projections in frequency domain. Then, the estimated parameters were applied to compensate for the motion effects in the reconstruction process. Finally, this method was extended to helical CT.

RESULTS

In fan-beam CT experiments, the simulation results showed that the proposed method was more accurate and faster on the performance of motion estimation than using Helgason-Ludwig consistency condition method (HLCC). Furthermore, the reconstructed images on both simulated and human head experiments indicated that the proposed method yielded superior results in artifact reduction.

CONCLUSIONS

The proposed method is a new tool for patient motion compensation, with a potential for practical application. It is not only applicable to motion correction in fan-beam CT imaging, but also to helical CT.

Study of the Microfocus X-Ray Tube Based on a Point-Like Target Used for Micro-Computed Tomography

For a micro-Computed Tomography (Micro-CT) system, the microfocus X-ray tube is an essential component because the spatial resolution of CT images, in theory, is mainly determined by the size and stability of the X-ray focal spot of the microfocus X-ray tube. However, many factors, including voltage fluctuations, mechanical vibrations, and temperature changes, can cause the size and the stability of the X-ray focal spot to degrade. A new microfocus X-ray tube based on a point-like micro-target in which the X-ray target is irradiated with an unfocused electron beam was investigated. EGS4 Monte Carlo simulation code was employed for the calculation of the X-ray intensity produced from the point-like micro-target and the substrate. The effects of several arrangements of the target material, target and beam size were studied. The simulation results demonstrated that if the intensity of X-rays generated at the point-like target is greater than half of the X-ray intensity produced on the substrate, the X-ray focal spot is determined in part by the point-like target rather than by the electron beam in the conventional X-ray tube. In theory, since it is able to reduce those unfavorable effects such as the electron beam trajectory swinging and the beam size changing for the microfocus X-ray tube, it could alleviate CT image artifacts caused by the X-ray focal spot shift and size change.

Data consistency conditions for truncated fanbeam and parallel projections

PURPOSE

In image reconstruction from projections, data consistency conditions (DCCs) are mathematical relationships that express the overlap of information between ideal projections. DCCs have been incorporated in image reconstruction procedures for positron emission tomography, single photon emission computed tomography, and x-ray computed tomography (CT). Building on published fanbeam DCCs for nontruncated projections along a line, the authors recently announced new DCCs that can be applied to truncated parallel projections in classical (two-dimensional) image reconstruction. These DCCs take the form of polynomial expressions for a weighted backprojection of the projections. The purpose of this work was to present the new DCCs for truncated parallel projections, to extend these conditions to truncated fanbeam projections on a circular trajectory, to verify the conditions with numerical examples, and to present a model of how DCCs could be applied with a toy problem in patient motion estimation with truncated projections.

METHODS

A mathematical derivation of the new parallel DCCs was performed by substituting the underlying imaging equation into the mathematical expression for the weighted backprojection and demonstrating the resulting polynomial form. This DCC result was extended to fanbeam projections by a substitution of parallel to fanbeam variables. Ideal fanbeam projections of a simple mathematical phantom were simulated and the DCCs for these projections were evaluated by fitting polynomials to the weighted backprojection. For the motion estimation problem, a parametrized motion was simulated using a dynamic version of the mathematical phantom, and both noiseless and noisy fanbeam projections were simulated for a full circular trajectory. The fanbeam DCCs were applied to extract the motion parameters, which allowed the motion contamination to be removed from the projections. A reconstruction was performed from the corrected projections.

RESULTS

The mathematical derivation revealed the anticipated polynomial behavior. The conversion to fanbeam variables led to a straight-forward weighted fanbeam backprojection which yielded the same function and therefore the same polynomial behavior as occurred in the parallel case. Plots of the numerically calculated DCCs showed polynomial behavior visually indistinguishable from the fitted polynomials. For the motion estimation problem, the motion parameters were satisfactorily recovered and ten times more accurately for the noise-free case. The reconstructed images showed that only a faint trace of the motion blur was still visible after correction from the noisy motion-contaminated projections.

CONCLUSIONS

New DCCs have been established for fanbeam and parallel projections, and these conditions have been validated using numerical experiments with truncated projections. It has been shown how these DCCs could be applied to extract parameters of unwanted physical effects in tomographic imaging, even with truncated projections.

Full data consistency conditions for cone-beam projections with sources on a plane

Cone-beam consistency conditions (also known as range conditions) are mathematical relationships between different cone-beam projections, and they therefore describe the redundancy or overlap of information between projections. These redundancies have often been exploited for applications in image reconstruction. In this work we describe new consistency conditions for cone-beam projections whose source positions lie on a plane. A further restriction is that the target object must not intersect this plane. The conditions require that moments of the cone-beam projections be polynomial functions of the source positions, with some additional constraints on the coefficients of the polynomials. A precise description of the consistency conditions is that the four parameters of the cone-beam projections (two for the detector, two for the source position) can be expressed with just three variables, using a certain formulation involving homogeneous polynomials. The main contribution of this work is our demonstration that these conditions are not only necessary, but also sufficient. Thus the consistency conditions completely characterize all redundancies, so no other independent conditions are possible and in this sense the conditions are full. The idea of the proof is to use the known consistency conditions for 3D parallel projections, and to then apply a 1996 theorem of Edholm and Danielsson that links parallel to cone-beam projections. The consistency conditions are illustrated with a simulation example.

Automated motion correction for in vivo optical projection tomography

In in vivo optical projection tomography (OPT), object motion will significantly reduce the quality and resolution of the reconstructed image. Based on the well-known Helgason-Ludwig consistency condition (HLCC), we propose a novel method for motion correction in OPT under parallel beam illumination. The method estimates object motion from projection data directly and does not require any other additional information, which results in a straightforward implementation. We decompose object movement into translation and rotation, and discuss how to correct for both translation and general motion simultaneously. Since finding the center of rotation accurately is critical in OPT, we also point out that the system's geometrical offset can be considered as object translation and therefore also calibrated through the translation estimation method. In order to verify the algorithm effectiveness, both simulated and in vivo OPT experiments are performed. Our results demonstrate that the proposed approach is capable of decreasing movement artifacts significantly thus providing high quality reconstructed images in the presence of object motion.

Automated quantification of three-dimensional subject motion to monitor image quality in high-resolution peripheral quantitative computed tomography

Subject motion during acquisition of high-resolution peripheral quantitative computed tomography (HR-pQCT) results in image artifacts and interferes with quantification of bone architecture used to study bone-related diseases such as osteoporosis. We propose an automatic method to measure physical subject motion that frequently takes place during acquisition. Three measures derived from projection data are proposed to quantify motion artifacts: in-plane translation (ε(T)) and in-plane rotation (ε(R)) utilizing projection moments and longitudinal translation (ε(z)) based on tracking projection profiles. Validation was performed using a phantom containing sections of distal human cadaver radii attached to a mechanical device to precisely control in-plane rotation and longitudinal translation that was intentionally performed during HR-pQCT data acquisition. Motion measured by the new automated technique was compared to the known applied motion, and related to percent errors in morphological parameters quantifying bone properties. It was determined that of the three proposed measures, ε(T) best captured a quantified representation of image quality. ε(T) linearly relates to true physical in-plane translational motion (r(2) = 0.95, p<0.001) and is independent from longitudinal translational motion as well as the object being scanned. Additionally, ε(z) captures large longitudinal movements and combines well with ε(T) to fully characterize physical motion artifacts. The magnitude of ε(T) corresponds to morphological parameter error and is an excellent basis to select high-quality images. Morphological parameter errors from these experiments confirmed our earlier computer simulations which showed that increased subject motion resulted in artificially higher trabecular number, and artificially lower bone mineral density and cortical thickness. The magnitude and, notably, the uncertainty of the morphological errors increased with increased physical motion, and this impedes a direct linear compensation of parameter errors. The automated method presented provides a basis for consistent and objective quality assurance for HR-pQCT scanning, and addresses an important challenge for this novel imaging modality that is rapidly becoming an important basis for assessment and monitoring of bone quality.

Fourier properties of the fan-beam sinogram

PURPOSE

P. R. Edholm, R. M. Lewitt, and B. Lindholm, "Novel properties of the Fourier decomposition of the sinogram," in Proceedings of the International Workshop on Physics and Engineering of Computerized Multidimensional Imaging and Processing [Proc. SPIE 671, 8-18 (1986)] described properties of a parallel beam projection sinogram with respect to its radial and angular frequencies. The purpose is to perform a similar derivation to arrive at corresponding properties of a fan-beam projection sinogram for both the equal-angle and equal-spaced detector sampling scenarios.

METHODS

One of the derived properties is an approximately zero-energy region in the two-dimensional Fourier transform of the full fan-beam sinogram. This region is in the form of a double-wedge, similar to the parallel beam case, but different in that it is asymmetric with respect to the frequency axes. The authors characterize this region for a point object and validate the derived properties in both a simulation and a head CT data set. The authors apply these results in an application using algebraic reconstruction.

RESULTS

In the equal-angle case, the domain of the zero region is (q,k) for which / k/(k-q) / > R/L, where q and k are the frequency variables associated with the detector and view angular positions, respectively, R is the radial support of the object, and L is the source-to-isocenter distance. A filter was designed to retain only sinogram frequencies corresponding to a specified radial support. The filtered sinogram was used to reconstruct the same radial support of the head CT data. As an example application of this concept, the double-wedge filter was used to computationally improve region of interest iterative reconstruction.

CONCLUSIONS

Interesting properties of the fan-beam sinogram exist and may be exploited in some applications.

Image-based iterative compensation of motion artifacts in computed tomography

PURPOSE

This article presents an iterative method for compensation of motion artifacts for slowly rotating computed tomography (CT) systems. Patient's motion introduces inconsistencies among projections and yields severe reconstruction artifacts for free-breathing acquisitions. Streaks and doubling of structures can appear and the resolution is limited by strong blurring.

METHODS

The rationale of the proposed motion compensation method is to iteratively correct the reconstructed image by first decomposing the perceived motion in projection space, then reconstructing the motion artifacts in image space, and finally subtracting the artifacts from an initial image. The initial image is reconstructed from the acquired data and might contain motion blur artifacts but, nevertheless, is considered as a reference for estimating the reconstruction artifacts.

RESULTS

Qualitative and quantitative figures are shown for experiments based on numerically simulated projections of a sequence of clinical images resulting from a respiratory-gated helical CT acquisition. The border of the diaphragm becomes progressively sharper and the contrast improves for small structures in the lungs.

CONCLUSIONS

The originality of the technique stems from the fact that the patient motion is not explicitly estimated but the motion artifacts are reconstructed in image space. This approach could provide sharp static anatomical images on interventional C-arm systems or on slowly rotating X-ray equipments in radiotherapy.

Ultra-low dose lung CT perfusion regularized by a previous scan

RATIONALE AND OBJECTIVES

A previous scan-regularized reconstruction (PSRR) method was proposed to reduce radiation dose and applied to lung perfusion studies. Normal and ultra-low-dose lung computed tomographic perfusion studies were compared in terms of the estimation accuracy of pulmonary functional parameters.

MATERIALS AND METHODS

A sequence of sheep lung scans were performed in three prone, anesthetized sheep at normal and ultra-low doses. A scan protocol was developed for the ultra-low-dose studies with electrocardiographic gating: time point 1 for a normal x-ray dose scan (100 kV, 150 mAs) and time points 2 to 21 for low-dose scans (80 kV, 17 mAs). A nonlinear diffusion-based post-filtering method was applied to the difference images between the low-dose images and the high-quality reference image. The final images at 20 time points were generated by fusing the reference image with the filtered difference images.

RESULTS

The power spectra of perfusion images and coherences in the normal scans showed a great improvement in image quality of the ultra-low-dose scans with PSRR relative to those without RSRR. The gamma variate fitting and the repeatability of the measurements of the mean transit time demonstrated that the key parameters of lung functions can be reliably accessed using PSRR. The variability of the ultra-low-dose scan results obtained using PSRR was not substantially different from that between two normal-dose scans.

CONCLUSIONS

This study demonstrates that an approximate 90% reduction in radiation dose is achievable using PSRR without compromising quantitative computed tomographic measurements of regional lung function.

Approximate and exact cone-beam reconstruction with standard and non-standard spiral scanning

The long object problem is practically important and theoretically challenging. To solve the long object problem, spiral cone-beam CT was first proposed in 1991, and has been extensively studied since then. As a main feature of the next generation medical CT, spiral cone-beam CT has been greatly improved over the past several years, especially in terms of exact image reconstruction methods. Now, it is well established that volumetric images can be exactly and efficiently reconstructed from longitudinally truncated data collected along a rather general scanning trajectory. Here we present an overview of some key results in this area.

Data consistency based rigid motion artifact reduction in fan-beam CT

It is well known that a rigid in-plane motion can be decomposed into a translation and a rotation around an origin. Based on our previous work, we first extend the Helgason-Ludwig consistency condition (HLCC) to cover a general rigid motion in fan-beam geometry. Then, we model the general motion by several parameters, and develop an iterative scheme for estimation of the in-plane motion parameters. This scheme determines the motion parameters by numerically minimizing an objective function constructed based on the HLCC. After the motion parameters are estimated, image reconstruction can be performed to compensate for the motion effects. Finally, we implement the algorithm and evaluate its performance in numerical simulations.