R.J. Jaszczak

Pinhole collimation for ultra-high-resolution, small-field-of-view SPECT

The objective of this investigation was to evaluate small-field-of-view, ultra-high-resolution pinhole collimation for a rotating-camera SPECT system that could be used to image small laboratory animals. Pinhole collimation offers distinct advantages over conventional parallel-hole collimation when used to image small objects. Since geometric sensitivity increases markedly for points close to the pinhole, small-diameter and high-magnification pinhole geometries may be useful for selected imaging tasks when used with large-field-of-view scintillation cameras. The use of large magnifications can minimize the loss of system resolution caused by the intrinsic resolution of the scintillation camera. A pinhole collimator has been designed and built that can be mounted on one of the scintillation cameras of a triple-head SPECT system. Three pinhole inserts with approximate aperture diameters of 0.6, 1.2 and 2.0 mm have been built and can be mounted individually on the collimator housing. When a ramp filter is used with a three-dimensional (3D) filtered backprojection (FBP) algorithm, the three apertures have in-plane SPECT spatial resolutions (FWHM) at 4 cm of 1.5, 1.9 and 2.8 mm, respectively. In-air point source sensitivities at 4 cm from the apertures are 0.9, 2.6 and 5.7 counts s(-1) microCi(-1) (24, 70 and 154 counts s(-1) MBq(-1)) for the 0.6, 1.2 and 2.0 mm apertures, respectively. In vitro image quality was evaluated with a micro-cold-rod phantom and a micro-Defrise phantom using both the 3D FBP algorithm and a 3D maximum likelihood-expectation maximization (ML-EM) algorithm. In vivo image quality was evaluated using two (315 and 325 g) rats. Ultra-high-resolution pinhole SPECT is an inexpensive and simple approach for imaging small animals that can be used with existing rotating-camera SPECT system.

Inverse Monte Carlo: A Unified Reconstruction Algorithm for SPECT

Inverse Monte Carlo (IMOC) is presented as a unified reconstruction algorithm for Emission Computed Tomography (ECT) providing simultaneous compensation for scatter, attenuation, and the variation of collimator resolution with depth. The technique of inverse Monte Carlo is used to find an inverse solution to the photon transport equation (an integral equation for photon flux from a specified source) for a parameterized source and specific boundary conditions. The system of linear equations so formed is solved to yield the source activity distribution for a set of acquired projections. For the studies presented here, the equations are solved using the EM (Maximum Likelihood) algorithm although other solution algorithms, such as Least Squares, could be employed. While the present results specifically consider the reconstruction of camera-based Single Photon Emission Computed Tomographic (SPECT) images, the technique is equally valid for Positron Emission Tomography (PET) if a Monte Carlo model of such a system is used. As a preliminary evaluation, experimentally acquired SPECT phantom studies for imaging Tc-99m (140 keV) are presented which demonstrate the quantitative compensation for scatter and attenuation for a two dimensional (single slice) reconstruction. The algorithm may be expanded in a straight forward manner to full three dimensional reconstruction including compensation for out of plane scatter.

Bayesian reconstruction and use of anatomical a priori information for emission tomography

A Bayesian method is presented for simultaneously segmenting and reconstructing emission computed tomography (ECT) images and for incorporating high-resolution, anatomical information into those reconstructions. The anatomical information is often available from other imaging modalities such as computed tomography (CT) or magnetic resonance imaging (MRI). The Bayesian procedure models the ECT radiopharmaceutical distribution as consisting of regions, such that radiopharmaceutical activity is similar throughout each region. It estimates the number of regions, the mean activity of each region, and the region classification and mean activity of each voxel. Anatomical information is incorporated by assigning higher prior probabilities to ECT segmentations in which each ECT region stays within a single anatomical region. This approach is effective because anatomical tissue type often strongly influences radiopharmaceutical uptake. The Bayesian procedure is evaluated using physically acquired single-photon emission computed tomography (SPECT) projection data and MRI for the three-dimensional (3-D) Hoffman brain phantom. A clinically realistic count level is used. A cold lesion within the brain phantom is created during the SPECT scan but not during the MRI to demonstrate that the estimation procedure can detect ECT structure that is not present anatomically.

Parameter estimation of finite mixtures using the EM algorithm and information criteria with application to medical image processing

A method for parameter estimation in image classification or segmentation is studied within the statistical frame of finite mixture distributions. The method models an image as a finite mixture. Each mixture component corresponds to an image class. Each image class is characterized by parameters, such as the intensity mean, the standard deviation, and the number of image pixels in that class. The method uses a maximum likelihood (ML) approach to estimate the parameters of each class and employs information criteria of Akaike (AIC) and/or Schwarz and Rissanen (MDL) to determine the number of classes in the image. In computing the ML solution of the mixture, the method adopts the expectation maximization (EM) algorithm. The initial estimation and convergence of the ML-EM algorithm were studied. The accuracy in determining the number of image classes using AIC and MDL is compared. The MDL criterion performed better than the AIC criterion. A modified MDL showed further improvement. >

Energy and spatial distribution of multiple order Compton scatter in SPECT: a Monte Carlo investigation

Energy and spatial projection distributions were simulated for gamma camera imaging of multiple order Compton scattered photons. SPECT imaging of a line source of radioactivity located in a water filled cylindrical phantom was modelled using Monte Carlo techniques. Photon trajectories were followed from emission to detection including the effects of all physical interactions and the resulting energy spectra and spatial projections were sorted as a function of the number of times the photon underwent Compton scattering before detection. Analysis of energy spectra demonstrates that Compton events up to second order overlap with the non-scattered events and distributions are peaked at lower energies as the scattering order increases. Analysis of spatial projections shows that, with increasing order, Compton events produce tails on the line spread function which progress from roughly exponential to nearly flat distributions. The use of Monte Carlo modelling thus allows a detailed investigation of the spatial and energy distribution of Compton scatter which could not be performed using present experimental techniques.

Scatter Compensation Techniques for SPECT

Compton scattered photons included within the photopeak pulse-height window decrease the image contrasts of lesions. Furthermore, in the absence of an effective compensation procedure, the accuracy of the SPECT measurement is reduced. In the following sections the effect of scatter on SPECT is reviewed, and the influence of scattered photons on the effective linear attenuation coefficient used with multiplicative attenuation compensation methods is described. Criteria for evaluating scatter compensation procedures are proposed, and approaches to reducing the effect of scatter on SPECT imaging are described.

The role of three dimensional functional lung imaging in radiation treatment planning: The functional dose-volume histogram

PURPOSE During thoracic irradiation (XRT), treatment fields are usually designed to minimize the volume of nontumor-containing lung included. Generally, functional heterogeneities within the lung are not considered. The three dimensional (3D) functional information provided by single photon emission computed tomography (SPECT) lung perfusion scans might be useful in designing beams that minimize incidental irradiation of functioning lung tissue. We herein review the pretreatment SPECT scans in 86 patients (56 with lung cancer) to determine which are likely to benefit from this technology. METHODS AND MATERIALS Prior to thoracic XRT, SPECT lung perfusion scans were obtained following the intravenous injection of approximately 4 mCi of 99mcTc-labeled macro-aggregated albumin. The presence of areas of decreased perfusion, their location relative to the tumor, and the potential clinical usefulness of their recognition, were scored. Patients were grouped and compared (two-tailed chi-square) based on clinical factors. Conventional dose-volume histograms (DVHs) (DVFHs) are calculated based on the dose distribution throughout the computed tomography (CT)-defined lung and SPECT-defined perfused lung, respectively. RESULTS Among 56 lung cancer patients, decreases in perfusion were observed at the tumor, adjacent to the tumor, and separate from the tumor in 94%, 74%, and 42% of patients, respectively. Perfusion defects adjacent to the tumor were often large with centrally placed tumors. Hypoperfusion in regions separate from the tumor were statistically most common in patients with relatively poor pulmonary function and chronic obstructive pulmonary disease (COPD). Considering all SPECT defects adjacent to and separate from the tumor, corresponding CT abnormalities were seen in only approximately 50% and 20% of patients, respectively, and were generally not as impressive. Following XRT, hypoperfusion at and separate from the tumor persisted, while defects adjacent to the tumor improved in several patients. In four patients who achieved a complete response scored by CT with chemotherapy prior to XRT, persistent hypoperfusion was present at and adjacent to the tumor site in three. Among 30 patients with cancers not arising in the lung (14 breast, 12 lymphoma, 4 others), perfusion defects were seen in only 4 (2 adjacent and 2 apart). Recognition of decreases in perfusion mainly impacted on treatment planning for a few patients with poor pulmonary function and limited target volumes. DVFHs have been useful in beam selection for patients with marked perfusion heterogeneities. CONCLUSIONS Lung perfusion scans provide functional information not provided by CT scans that can be useful in designing radiation treatment beams that minimize incidental irradiation of the function regions of the lung. This approach appears to be most helpful in patients with gross intrathoracic lung cancer, especially those with small targets and relatively poor pulmonary function. One limitation of this approach is that some of the defects adjacent to the tumor site reperfuse following treatment, indicating that these scans identify perfusion rather than potential perfusion. Three dimensional functional data can be used to generate DVFHs that may be more predictive of the physiological consequences of the radiation than conventional DVHs. Additional work is currently underway to test this hypothesis.

Cone beam collimation for single photon emission computed tomography: analysis, simulation, and image reconstruction using filtered backprojection

This paper presents an analysis of two cone beam configurations (having focal lengths of 40 and 60 cm) for the acquisition of single photon emission computed tomography (SPECT) projection data. A three-dimensional filtered backprojection algorithm is used to reconstruct SPECT images of cone beam projection data obtained using Monte Carlo simulations. The mathematical analysis resulted in on-axis point source sensitivities (calculated for a distance of 15 cm from the collimator surface) for cone beam configurations that were 1.4-3 times the sensitivities of parallel-hole and fan beam geometries having similar geometric resolutions. Cone beam collimation offers the potential for improved sensitivity for SPECT devices using large-field-of-view scintillation cameras.

Whole-body single-photon emission computed tomography using dual, large-field-of-view scintillation cameras

A whole-body single-photon emission computed tomography system (SPECT) consisting of two large-field-of-view scintillation cameras mounted on a rotatable gantry, a minicomputer and a display station has been designed, constructed and evaluated. In its usual mode of operation, eleven contiguous transverse sections, each 12.5 or 25 mm thick, are reconstructed from projection data acquired during a single, continuous 360 degree rotation lasting from 2 to 22 min. A generalised filtered and weighted backprojection algorithm is used to reconstruct data obtained with conventional parallel-hole collimators in the case of body scanning, or with specially designed fan beam collimators in the case of centrally positioned organs. A simple, yet effective, correction is used to compensate for the effects of gamma ray attenuation within the patient. In addition to providing transverse section images, the system is capable of simultaneous acquisition of opposed conventional scintigrams, the reconstruction of longitudinal section images, and the acquisition of gated cardiac transverse sections. Resolutions in the reconstructed images are typically 15 mm for body scans and 11 mm for brain scans, with only slight variations in sensitivity and resolution within the image. Phantoms and clinical data demonstrate that the SPECT system generates high quality section images while maintaining most of the flexibility of normal scintillation cameras, with the added advantage of dual heads.

Fully Bayesian estimation of Gibbs hyperparameters for emission computed tomography data

In recent years, many investigators have proposed Gibbs prior models to regularize images reconstructed from emission computed tomography data. Unfortunately, hyperparameters used to specify Gibbs priors can greatly influence the degree of regularity imposed by such priors and, as a result, numerous procedures have been proposed to estimate hyperparameter values, from observed image data. Many of these, procedures attempt to maximize the joint posterior distribution on the image scene. To implement these methods, approximations to the joint posterior densities are required, because the dependence of the Gibbs partition function on the hyperparameter values is unknown. Here, the authors use recent results in Markov chain Monte Carlo (MCMC) sampling to estimate the relative values of Gibbs partition functions and using these values, sample from joint posterior distributions on image scenes. This allows for a fully Bayesian procedure which does not fix the hyperparameters at some estimated or specified value, but enables uncertainty about these values to be propagated through to the estimated intensities. The authors utilize realizations from the posterior distribution for determining credible regions for the intensity of the emission source. The authors consider two different Markov random field (MRF) models-the power model and a line-site model. As applications they estimate the posterior distribution of source intensities from computer simulated data as well as data collected from a physical single photon emission computed tomography (SPECT) phantom.