David G. Politte

A method for the reconstruction of four-dimensional synchronized CT scans acquired during free breathing

Breathing motion is a significant source of error in radiotherapy treatment planning for the thorax and upper abdomen. Accounting for breathing motion has a profound effect on the size of conformal radiation portals employed in these sites. Breathing motion also causes artifacts and distortions in treatment planning computed tomography (CT) scans acquired during free breathing and also causes a breakdown of the assumption of the superposition of radiation portals in intensity-modulated radiation therapy, possibly leading to significant dose delivery errors. Proposed voluntary and involuntary breath-hold techniques have the potential for reducing or eliminating the effects of breathing motion, however, they are limited in practice, by the fact that many lung cancer patients cannot tolerate holding their breath. We present an alternative solution to accounting for breathing motion in radiotherapy treatment planning, where multislice CT scans are collected simultaneously with digital spirometry over many free breathing cycles to create a four-dimensional (4-D) image set, where tidal lung volume is the additional dimension. An analysis of this 4-D data leads to methods for digital-spirometry, based elimination or accounting of breathing motion artifacts in radiotherapy treatment planning for free breathing patients. The 4-D image set is generated by sorting free-breathing multislice CT scans according to user-defined tidal-volume bins. A multislice CT scanner is operated in the ciné mode, acquiring 15 scans per couch position, while the patient undergoes simultaneous digital-spirometry measurements. The spirometry is used to retrospectively sort the CT scans by their correlated tidal lung volume within the patients normal breathing cycle. This method has been prototyped using data from three lung cancer patients. The actual tidal lung volumes agreed with the specified bin volumes within standard deviations ranging between 22 and 33 cm3. An analysis of sagittal and coronal images demonstrated relatively small (<1 cm) motion artifacts along the diaphragm, even for tidal volumes where the rate of breathing motion is greatest. While still under development, this technology has the potential for revolutionizing the radiotherapy treatment planning for the thorax and upper abdomen.

Noise and Edge Artifacts in Maximum-Likelihood Reconstructions for Emission Tomography

Images produced in emission tomography with the expectation-maximization algorithm have been observed to become more noisy and to have large distortions near edges as iterations proceed and the images converge towards the maximum-likelihood estimate. It is our conclusion that these artifacts are fundamental to reconstructions based on maximum-likelihood estimation as it has been applied usually; they are not due to the use of the expectation-maximization algorithm, which is but one numerical approach for finding the maximum-likelihood estimate. In this paper, we develop a mathematical approach for suppressing both the noise and edge artifacts by modifying the maximum-likelihood approach to include constraints which the estimate must satisfy.

Corrections for accidental coincidences and attenuation in maximum-likelihood image reconstruction for positron-emission tomography

Reconstruction procedures that account for attenuation in forming maximum-likelihood estimates of activity distributions in positron-emission tomography are extended to include regularization constraints and accidental coincidences. A mathematical model is used for these effects. The corrections are incorporated into the iterations of an expectation-maximization algorithm for numerically producing the maximum-likelihood estimate of the distribution of radioactivity within a patient. The images reconstructed with this procedure are unbiased and exhibit lower variance than those reconstructed from precorrected data.

Image Reconstruction from List-Mode Data in an Emission Tomography System Having Time-of-Flight Measurements

List-mode data collected in a positron-emission tomography system having time-of-flight measurements are three dimensional, but all algorithms which have been published to date operate on two-dimensional data derived from these three-dimensional data. We argue in this paper that the additional information present in the three-dimensional data is useful for improving reconstructions of images.

Prospects for quantitative computed tomography imaging in the presence of foreign metal bodies using statistical image reconstruction.

X-ray computed tomography (CT) images of patients bearing metal intracavitary applicators or other metal foreign objects exhibit severe artifacts including streaks and aliasing. We have systematically evaluated via computer simulations the impact of scattered radiation, the polyenergetic spectrum, and measurement noise on the performance of three reconstruction algorithms: conventional filtered backprojection (FBP), deterministic iterative deblurring, and a new iterative algorithm, alternating minimization (AM), based on a CT detector model that includes noise, scatter, and polyenergetic spectra. Contrary to the dominant view of the literature, FBP streaking artifacts are due mostly to mismatches between FBPs simplified model of CT detector response and the physical process of signal acquisition. Artifacts on AM images are significantly mitigated as this algorithm substantially reduces detector-model mismatches. However, metal artifacts are reduced to acceptable levels only when prior knowledge of the metal object in the patient, including its pose, shape, and attenuation map, are used to constrain AMs iterations. AM image reconstruction, in combination with object-constrained CT to estimate the pose of metal objects in the patient, is a promising approach for effectively mitigating metal artifacts and making quantitative estimation of tissue attenuation coefficients a clinical possibility.

Results of a Comparative Study of a Reconstruction Procedure for Producing Improved Estimates of Radioactivity Distributions in Time-of-Flight Emission Tomography

Several reconstruction algorithms for estimating distributions of radioactivity in an emission-tomography system having time-of-flight measurements have previously been identified. We present here a comparative study of various images reconstructed from data acquired with an emission-tomography system having time-of-flight data. Preliminary results indicate that a new recursive algorithm provides substantial improvements in image quality.

Image improvements in positron-emission tomography due to measuring differential time-of-flight and using maximum-likelihood estimation

Two distinctly different methods have been used to improve images produced in positron-emission tomography. The first method is to measure the differential time-of-flight of the photon pairs which are detected; the second is to use an iterative algorithm to compute maximum likelihood estimates of radioactivity distributions. The performances of algorithms which include neither, one or the other, or both methods of improvement have been quantified by performing a repetitive simulation experiment using the Hoffman brain phantom as the underlying distribution of radioactivity. Simulations show that all of the algorithms yield unbiased estimates of the desired image. The algorithm which computes maximum-likelihood estimates using time-of-flight information reconstructs images with the lowest variance. The algorithm which uses neither of these methods (filtered backprojection) reconstructs images with the highest variance. >

Deblurring subject to nonnegativity constraints when known functions are present with application to object-constrained computerized tomography

The reconstruction of tomographic images is often treated as a linear deblurring problem. When a high-density, man-made metal object is present somewhere in the image field, it is a deblurring problem in which the unknown function has a component that is known except for some location and orientation parameters. The authors first address general linear deblurring problems in which a known function having unknown parameters is present. They then show how the resulting iterative solution can be applied to tomographic imaging in the presence of man-made foreign objects, and they apply the result, in particular, to X-ray computed tomography imaging used in support of brachytherapy treatment of advanced cervical cancer.

The use of constraints to eliminate artifacts in maximum-likelihood image estimation for emission tomography

Images produced in emission tomography with unconstrained maximum-likelihood estimation techniques exhibit two artifacts as the likelihood hill is climbed and the images converge toward one with maximum likelihood. The first artifact is a speckled appearance on the image, a noise artifact. The second is an estimation error near edges of the underlying radioactivity concentration. However, if mathematical constraints are used that blur the estimated image and the image being estimated, these artifacts are eliminated. The authors review the nature of these constraints, demonstrate them, and measure the performance of the modified algorithm relative to the classical linear image-estimation algorithm. >

Image reconstruction for transmission tomography when projection data are incomplete.

Two iterative methods are developed for forming a maximum-likelihood estimate of the attenuation density in a patient or object for transmission tomography when projection data are incomplete. The methods converge monotonically to the same limit points. Results of testing the methods with both simulated and real data are given.