3-D Monte Carlo-based Scatter Compensation in Quantitative I-131 SPECT Reconstruction

We have implemented highly accurate Monte Carlo based scatter modeling (MCS) with 3-D ordered subsets expectation maximization (OSEM) reconstruction. The scatter is included in the statistical model as an additive term and attenuation and detector response are included in the forward/backprojector. In the present implementation of MCS, a simple multiple window-based estimate is used for the initial iterations and in the later iterations the Monte Carlo estimate is used for several iterations before it is updated. For I-131, MCS was evaluated and compared with triple energy window (TEW) scatter compensation using simulation studies of a mathematical phantom and a clinically realistic voxel-phantom. Even after just two Monte Carlo runs, excellent agreement was found between the MCS estimate and the true scatter distribution. Accuracy and noise of the reconstructed images were superior with MCS compared to TEW. However, the improvement was not large, and in some cases may not justify the large computational requirements of MCS. Finally clinical application of MCS was demonstrated by applying the method to a radioimmunotherapy (RIT) patient study.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85865/1/Fessler201.pd

Quantitative I-131 SPECT Reconstruction using CT Side Information from Hybrid Imaging

A penalized-likelihood (PL) SPECT reconstruction method using a modified regularizer that accounts for anatomical boundary side information was implemented to achieve accurate estimates of both the total target activity and the activity distribution within targets. In both simulations and experimental I-131 phantom studies, reconstructions from 1) penalized likelihood employing CT-side information based regularization (PL-CT); 2) penalized likelihood with edge preserving regularization (no CT); 3) penalized likelihood with conventional spatially invariant quadratic regularization (no CT) were compared with 4) Ordered Subset Expectation Maximization (OSEM), which is the iterative algorithm conventionally used in clinics for quantitative SPECT. Evaluations included phantom studies with perfect and imperfect (misregistered) side information and studies with uniform and non-uniform activity distributions in the target. For targets with uniform activity, the PL-CT images and profiles were closest to the `truth', avoided the edge offshoots evident with OSEM and minimized the blurring across boundaries evident with regularization without CT information. Apart from visual comparison, reconstruction accuracy was evaluated using the bias and standard deviation (STD) of the total target activity estimate and the root mean square error (RMSE) of the activity distribution within the target. PL-CT reconstruction reduced both bias and RMSE compared with regularization without side information. When compared with unregularized OSEM, PL-CT reduced RMSE and STD while bias was comparable. For targets with non-uniform activity, these improvements with PL-CT were observed only when the change in activity was matched by a change in the anatomical image and the corresponding inner boundary was also used to control the regularization. In summary, the present work demonstrates the potential of using CT side information to obtain improved estimates of the activity distribution in targets wi- - thout sacrificing the accuracy of total target activity estimation.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85862/1/Fessler243.pd

Absolute quantitative total-body small-animal SPECT with focusing pinholes

Purpose: In pinhole SPECT, attenuation of the photon flux on trajectories between source and pinholes affects quantitative accuracy of reconstructed images. Previously we introduced iterative methods that compensate for image degrading effects of detector and pinhole blurring, pinhole sensitivity and scatter for multi-pinhole SPECT. The aim of this paper is (1) to investigate the accuracy of the Chang algorithm in rodents and (2) to present a practical Changbased method using body outline contours obtained with optical cameras. Methods: Here we develop and experimentally validate a practical method for attenuation correction based on a Chang first-order method. This approach has the advantage that it is employed after, and therefore independently from, iterative reconstruction. Therefore, no new system matrix has to be calculated for each specific animal. Experiments with phantoms and animals were performed with a highresolution focusing multi-pinhole SPECT system (USPECT-II, MILabs, The Netherlands). This SPECT system provides three additional optical camera images of the animal for each SPECT scan from which the animal contour can be estimated. Results: Phantom experiments demonstrated that an average quantification error of –18.7% was reduced to –1.7% when both window-based scatter correction and Chang correction based on the body outline from optical images were applied. Without scatter and attenuation correction, quantification errors in a sacrificed rat containing sources with known activity ranged from –23.6 to –9.3%. These errors were reduced to values between –6.3 and +4.3% (with an average magnitude of 2.1%) after applying scatter and Chang attenuation correction. Conclusion: We conclude that the modified Chang correction based on body contour combined with window-based scatter correction is a practical method for obtaining small-animal SPECT images with high quantitative accuracy.Radiation, Radionuclides and ReactorsApplied Science

K-Bayes Reconstruction for Perfusion MRI I: Concepts and Application

Despite the continued spread of magnetic resonance imaging (MRI) methods in scientific studies and clinical diagnosis, MRI applications are mostly restricted to high-resolution modalities, such as structural MRI. While perfusion MRI gives complementary information on blood flow in the brain, its reduced resolution limits its power for detecting specific disease effects on perfusion patterns. This reduced resolution is compounded by artifacts such as partial volume effects, Gibbs ringing, and aliasing, which are caused by necessarily limited k-space sampling and the subsequent use of discrete Fourier transform (DFT) reconstruction. In this study, a Bayesian modeling procedure (K-Bayes) is developed for the reconstruction of perfusion MRI. The K-Bayes approach (described in detail in Part II: Modeling and Technical Development) combines a process model for the MRI signal in k-space with a Markov random field prior distribution that incorporates high-resolution segmented structural MRI information. A simulation study was performed to determine qualitative and quantitative improvements in K-Bayes reconstructed images compared with those obtained via DFT. The improvements were validated using in vivo perfusion MRI data of the human brain. The K-Bayes reconstructed images were demonstrated to provide reduced bias, increased precision, greater effect sizes, and higher resolution than those obtained using DFT

3-D Monte Carlo-Based Scatter Compensation in Quantitative I-131 SPECT Reconstruction

We have implemented highly accurate Monte Carlo based scatter modeling (MCS) with 3-D ordered subsets expectation maximization (OSEM) reconstruction for I-131 single photon emission computed tomography (SPECT). The scatter is included in the statistical model as an additive term and attenuation and detector response are included in the forward/backprojector. In the present implementation of MCS, a simple multiple window-based estimate is used for the initial iterations and in the later iterations the Monte Carlo estimate is used for several iterations before it is updated. For I-131, MCS was evaluated and compared with triple energy window (TEW) scatter compensation using simulation studies of a mathematical phantom and a clinically realistic voxel-phantom. Even after just two Monte Carlo updates, excellent agreement was found between the MCS estimate and the true scatter distribution. Accuracy and noise of the reconstructed images were superior with MCS compared to TEW. However, the improvement was not large, and in some cases may not justify the large computational requirements of MCS. Furthermore, it was shown that the TEW correction could be improved for most of the targets investigated here by applying a suitably chosen scaling factor to the scatter estimate. Finally clinical application of MCS was demonstrated by applying the method to an I-131 radioimmunotherapy (RIT) patient study.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85854/1/Fessler47.pd

PET-enabled Dual-Energy CT: Image Reconstruction and A Proof-of-Concept Computer Simulation Study

Standard dual-energy computed tomography (CT) uses two different X-ray energies to obtain energy-dependent tissue attenuation information to allow quantitative material decomposition. The combined use of dual-energy CT and positron emission tomography (PET) may provide a more comprehensive characterization of disease states in cancer and many other diseases. However, the integration of dual-energy CT with PET is not trivial, either requiring costly hardware upgrade or increasing radiation dose. This paper proposes a novel dual-energy CT imaging method that is enabled by the already-available PET data on PET/CT. Instead of using a second X-ray CT scan with a different energy, this method exploits time-of-flight PET image reconstruction via the maximum likelihood attenuation and activity (MLAA) algorithm to obtain a 511 keV gamma-ray attenuation image from PET emission data. The high-energy gamma-ray CT image is then combined with the low-energy X-ray CT of PET/CT to provide a pair of dual-energy CT images. A major challenge with the standard MLAA reconstruction is the high noise present in the reconstructed 511 keV attenuation map, which does not compromise the PET activity reconstruction too much but significantly affects the performance of the gamma-ray CT for material decomposition. To overcome the problem, we further propose a kernel MLAA algorithm to exploit the prior information from the available X-ray CT image. We conducted a computer simulation to test the concept and algorithm for the task of material decomposition. The simulation results indicate that this PET-enabled dual-energy CT method is promising for quantitative material decomposition. The proposed method can be readily implemented on time-of-flight PET/CT scanners to enable simultaneous PET and dual-energy CT imaging

Regularized reconstruction in quantitative SPECT using CT side information from hybrid imaging

A penalized-likelihood (PL) SPECT reconstruction method using a modified regularizer that accounts for anatomical boundary side information was implemented to achieve accurate estimates of both the total target activity and the activity distribution within targets. In both simulations and experimental I-131 phantom studies, reconstructions from (1) penalized likelihood employing CT-side information-based regularization (PL-CT), (2) penalized likelihood with edge preserving regularization (no CT) and (3) penalized likelihood with conventional spatially invariant quadratic regularization (no CT) were compared with (4) ordered subset expectation maximization (OSEM), which is the iterative algorithm conventionally used in clinics for quantitative SPECT. Evaluations included phantom studies with perfect and imperfect side information and studies with uniform and non-uniform activity distributions in the target. For targets with uniform activity, the PL-CT images and profiles were closest to the 'truth', avoided the edge offshoots evident with OSEM and minimized the blurring across boundaries evident with regularization without CT information. Apart from visual comparison, reconstruction accuracy was evaluated using the bias and standard deviation (STD) of the total target activity estimate and the root mean square error (RMSE) of the activity distribution within the target. PL-CT reconstruction reduced both bias and RMSE compared with regularization without side information. When compared with unregularized OSEM, PL-CT reduced RMSE and STD while bias was comparable. For targets with non-uniform activity, these improvements with PL-CT were observed only when the change in activity was matched by a change in the anatomical image and the corresponding inner boundary was also used to control the regularization. In summary, the present work demonstrates the potential of using CT side information to obtain improved estimates of the activity distribution in targets without sacrificing the accuracy of total target activity estimation. The method is best suited for data acquired on hybrid systems where SPECT-CT misregistration is minimized. To demonstrate clinical application, the PL reconstruction with CT-based regularization was applied to data from a patient who underwent SPECT/CT imaging for tumor dosimetry following I-131 radioimmunotherapy.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85409/1/pmb10_9_007.pd

Patch-based image reconstruction for PET using prior-image derived dictionaries

This collection contains figures and reconstructed images in .mat format associated with the manuscript tiled "Patch-based image reconstruction for PET using prior-image derived dictionaries" . The file, Data_Fig9-10.zip contains the reconstructed images associated with Fig 9 and 10 as a function of iteration for different methods. Data_Fig10-12.zip contains reconstructed images of the real data for different methods