Fully-3D PET image reconstruction using scanner-independent, adaptive projection data and highly rotation-symmetric voxel assemblies

For iterative, fully 3D positron emission tomography (PET) image reconstruction intrinsic symmetries can be used to significantly reduce the size of the system matrix. The precalculation and beneficial memory-resident storage of all nonzero system matrix elements is possible where sufficient compression exists. Thus, reconstruction times can be minimized independently of the used projector and more elaborate weighting schemes, e.g., volume-of-intersection (VOI), are applicable. A novel organization of scanner-independent, adaptive 3D projection data is presented which can be advantageously combined with highly rotation-symmetric voxel assemblies. In this way, significant system matrix compression is achieved. Applications taking into account all physical lines-of-response (LORs) with individual VOI projectors are presented for the Siemens ECAT HR+ whole-body scanner and the Siemens BrainPET, the PET component of a novel hybrid-MR/PET imaging system. Measured and simulated data were reconstructed using the new method with ordered-subset-expectation-maximization (OSEM). Results are compared to those obtained by the sinogram-based OSEM reconstruction provided by the manufacturer. The higher computational effort due to the more accurate image space sampling provides significantly improved images in terms of resolution and noise.     

Evaluating performance of reconstruction algorithms for 3-D [15O] water PET using subtraction analysis

Positron emission tomography (PET) [15O] activation studies have benefited significantly from three-dimensional (3-D) data acquisition. However, they have been slow to take advantage of new 3-D reconstruction techniques. Compared with the widely used 3-D reprojection reconstruction (3DRP), the advantage of signal and noise for iterative algorithms has been outweighed by concern about long and complicated reconstruction procedures and inconsistent performance. Most pseudo-3-D algorithms, such as rebinning methods, aim at increasing the speed of reconstruction but lack further resolution improvement or noise control. Although many evaluations have been conducted through simulations and phantom experiments, the spatially varying nature of signal and noise and the complexity of biological effects have complicated the interpretation of real data based on simulation or phantom results. We have taken a different approach and used the analysis of real data directly as a measure with which to compare three reconstruction algorithms: 3DRP, iterative filtered backprojection with median root prior (IFBP-MRP), and Fourier rebinning followed by two-dimensional (2-D) filtered backprojection (FORE-FBP) for [15O] PET. Two subjects, each with 32 scans acquired in four sessions during a finger opposition motor task, are analyzed using subtraction. A fixed volume-of-interest (VOI) measurement in regions related to the task demonstrates that at high resolution, IFBP-MRP has the best signal-to-noise performance followed by 3DRP and FORE-FBP; however, this advantage gradually diminishes as the resolution decreases. For a voxel measurement derived from the image of each reconstruction, all three algorithms are capable of detecting highly activated regions. Although there are some differences in the size, shape, and center location of the activated foci, our preliminary results suggest that IFBP-MRP does offer enhanced signal with some noise control compared with 3DRP for the analysis of high-resolution images. If images are to be analyzed at an intermediate to lower resolution, FORE-FBP provides a significant reduction of reconstruction time compared with 3DRP.     

2.5-D simultaneous multislice reconstruction by series expansion methods from Fourier-rebinned PET data

True three-dimensional (3-D) volume reconstruction from fully 3-D data in positron emission tomography (PET) has only a limited clinical use because of its large computational burden. Fourier rebinning (FORE) of the fully 3-D data into a set of 2-D sinogram data decomposes the 3-D reconstruction process into multiple 2-D reconstructions of decoupled 2-D image slices, thus substantially decreasing the computational burden even in the case when the 2-D reconstructions are performed by an iterative reconstruction algorithm. On the other hand, the approximations involved in the rebinning combined with the decoupling of the image slices cause a certain reduction of image quality, especially when the signal-to-noise ratio of the data is low. We propose a 2.5-D Simultaneous Multislice Reconstruction approach, based on the series expansion principle, where the volume is represented by the superposition of 3-D spherically symmetric bell-shaped basis functions. It takes advantage of the time reduction due to the use of the FORE (2-D) data, instead of the original fully 3-D data, but at the same time uses a 3-D iterative reconstruction approach with 3-D basis functions. The same general approach can be applied to any reconstruction algorithm belonging to the class of series expansion methods (iterative or noniterative) using 3-D basis functions that span multiple slices, and can be used for any multislice sinogram or list mode data whether obtained by a special rebinning scheme or acquired directly by a PET scanner in the 2-D mode using septa. Our studies confirm that the proposed 2.5-D approach provides a considerable improvement in reconstruction quality, as compared to the standard 2-D reconstruction approach, while the reconstruction time is of the same order as that of the 2-D approach and is clinically practical even on a general-purpose computer.     

Rotate-and-slant projector for fast LOR-based fully-3-D iterative PET reconstruction

One of the greatest challenges facing iterative fully-3-D positron emission tomography (PET) reconstruction is the issue of long reconstruction times due to the large number of measurements for 3-D mode as compared to 2-D mode. A rotate-and-slant projector has been developed that takes advantage of symmetries in the geometry to compute volumetric projections to multiple oblique sinograms in a computationally efficient manner. It is based upon the 2-D rotation-based projector using the three-pass method of shears, and it conserves the 2-D rotator computations for multiple projections to each oblique sinogram set. The projector is equally applicable to both conventional evenly-spaced projections and unevenly-spaced line-of-response (LOR) data. The LOR-based version models the location and orientation of the individual LORs (i.e., the arc-correction), providing an ordinary Poisson reconstruction framework. The projector was implemented in C with several optimizations for speed, exploiting the vertical symmetry of the oblique projection process, depth compression, and array indexing schemes which maximize serial memory access. The new projector was evaluated and compared to ray-driven and distance-driven projectors using both analytical and experimental phantoms, and fully-3-D iterative reconstructions with each projector were also compared to Fourier rebinning with 2-D iterative reconstruction. In terms of spatial resolution, contrast, and background noise measures, 3-D LOR-based iterative reconstruction with the rotate-and-slant projector performed as well as or better than the other methods. Total processing times, measured on a single cpu Linux workstation, were approximately 10x faster for the rotate-and-slant projector than for the other 3-D projectors studied. The new projector provided four iterations fully-3-D ordered-subsets reconstruction in as little as 15 s--approximately the same time as FORE + 2-D reconstruction. We conclude that the rotate-and-slant projector is a viable option for fully-3-D PET, offering quality statistical reconstruction in times only marginally slower than 2-D or rebinning methods.     

Iterative image reconstruction using inverse Fourier rebinning for fully 3-D PET

We describe a fast forward and back projector pair based on inverse Fourier rebinning for use in iterative image reconstruction for fully 3-D positron emission tomography (PET). The projector pair is used as part of a factored system matrix that takes into account detector-pair response by using shift-variant sinogram blur kernels, thereby combining the computational advantages of Fourier rebinning with iterative reconstruction using accurate system models. The forward projector consists of a 2-D projector, which maps 3-D images into 2-D direct sinograms, followed by exact inverse rebinning which maps the 2-D into fully 3-D sinograms. The back projector is implemented as the transpose of the forward projector and differs from the true exact rebinning operator in the sense that it does not require reprojection to compute missing lines of response (LORs). We compensate for two types of inaccuracies that arise in a cylindrical PET scanner when using inverse Fourier rebinning: 1) nonuniform radial sampling and 2) nonconstant oblique angles in the radial direction in a single oblique sinogram. We examine the effects of these corrections on sinogram accuracy and reconstructed image quality. We evaluate performance of the new projector pair for maximum a posteriori (MAP) reconstruction of simulated and in vivo data. The new projector results in only a small loss in resolution towards the edge of the field-of-view when compared to the fully 3-D geometric projector and requires an order of magnitude less computation.     

Fourier-based reconstruction for fully 3-D PET: optimization of interpolation parameters

Fourier-based approaches for three-dimensional (3-D) reconstruction are based on the relationship between the 3-D Fourier transform (FT) of the volume and the two-dimensional (2-D) FT of a parallel-ray projection of the volume. The critical step in the Fourier-based methods is the estimation of the samples of the 3-D transform of the image from the samples of the 2-D transforms of the projections on the planes through the origin of Fourier space, and vice versa for forward-projection (reprojection). The Fourier-based approaches have the potential for very fast reconstruction, but their straightforward implementation might lead to unsatisfactory results if careful attention is not paid to interpolation and weighting functions. In our previous work, we have investigated optimal interpolation parameters for the Fourier-based forward and back-projectors for iterative image reconstruction. The optimized interpolation kernels were shown to provide excellent quality comparable to the ideal sinc interpolator. This work presents an optimization of interpolation parameters of the 3-D direct Fourier method with Fourier reprojection (3D-FRP) for fully 3-D positron emission tomography (PET) data with incomplete oblique projections. The reprojection step is needed for the estimation (from an initial image) of the missing portions of the oblique data. In the 3D-FRP implementation, we use the gridding interpolation strategy, combined with proper weighting approaches in the transform and image domains. We have found that while the 3-D reprojection step requires similar optimal interpolation parameters as found in our previous studies on Fourier-based iterative approaches, the optimal interpolation parameters for the main 3D-FRP reconstruction stage are quite different. Our experimental results confirm that for the optimal interpolation parameters a very good image accuracy can be achieved even without any extra spectral oversampling, which is a common practice to decrease errors caused by interpolation in Fourier reconstruction.     

Iterative image reconstruction using inverse fourier rebinning for fully 3-D PET

We describe a fast forward and back projector pair based on inverse Fourier rebinning for use in iterative image reconstruction for fully three-dimensional (3-D) positron emission tomography (PET). The projector pair is used as part of a factored system matrix that takes into account detector-pair response by using shift-variant sinogram blur kernels, thereby combining the computational advantages of Fourier rebinning with iterative reconstruction using accurate system models. The forward projector consists of a two-dimensional (2-D) projector, which maps 3-D images into 2-D direct sinograms, followed by exact inverse rebinning which maps the 2-D into fully 3-D sinograms. The back projector is implemented as the transpose of the forward projector and differs from the true exact rebinning operator in the sense that it does not require reprojection to compute missing line of responses (LORs). We compensate for two types of inaccuracies that arise in a cylindrical PET scanner when using inverse Fourier rebinning: 1) nonuniform radial sampling and 2) nonconstant oblique angles in the radial direction in a single oblique sinogram. We examine the effects of these corrections on sinogram accuracy and reconstructed image quality. We evaluate performance of the new projector pair for maximum a posteriori (MAP) reconstruction of simulated and in vivo data. The new projector results in only a small loss in resolution towards the edge of the field-of-view when compared to the fully 3-D geometric projector and requires an order of magnitude less computation.     

Direct reconstruction of kinetic parameter images from dynamic PET data

Our goal in this paper is the estimation of kinetic model parameters for each voxel corresponding to a dense three-dimensional (3-D) positron emission tomography (PET) image. Typically, the activity images are first reconstructed from PET sinogram frames at each measurement time, and then the kinetic parameters are estimated by fitting a model to the reconstructed time-activity response of each voxel. However, this "indirect" approach to kinetic parameter estimation tends to reduce signal-to-noise ratio (SNR) because of the requirement that the sinogram data be divided into individual time frames. In 1985, Carson and Lange proposed, but did not implement, a method based on the expectation-maximization (EM) algorithm for direct parametric reconstruction. The approach is "direct" because it estimates the optimal kinetic parameters directly from the sinogram data, without an intermediate reconstruction step. However, direct voxel-wise parametric reconstruction remained a challenge due to the unsolved complexities of inversion and spatial regularization. In this paper, we demonstrate and evaluate a new and efficient method for direct voxel-wise reconstruction of kinetic parameter images using all frames of the PET data. The direct parametric image reconstruction is formulated in a Bayesian framework, and uses the parametric iterative coordinate descent (PICD) algorithm to solve the resulting optimization problem. The PICD algorithm is computationally efficient and is implemented with spatial regularization in the domain of the physiologically relevant parameters. Our experimental simulations of a rat head imaged in a working small animal scanner indicate that direct parametric reconstruction can substantially reduce root-mean-squared error (RMSE) in the estimation of kinetic parameters, as compared to indirect methods, without appreciably increasing computation.     

Generation and visualization of four-dimensional MR angiography data using an undersampled 3-D projection trajectory

Time-resolved contrast-enhanced magnetic resonance (MR) angiography (CE-MRA) has gained in popularity relative to X-ray Digital Subtraction Angiography because it provides three-dimensional (3-D) spatial resolution and it is less invasive. We have previously presented methods that improve temporal resolution in CE-MRA while providing high spatial resolution by employing an undersampled 3-D projection (3D PR) trajectory. The increased coverage and isotropic resolution of the 3D PR acquisition simplify visualization of the vasculature from any perspective. We present a new algorithm to develop a set of time-resolved 3-D image volumes by preferentially weighting the 3D PR data according to its acquisition time. An iterative algorithm computes a series of density compensation functions for a regridding reconstruction, one for each time frame, that exploit the variable sampling density in 3D PR. The iterative weighting procedure simplifies the calculation of appropriate density compensation for arbitrary sampling patterns, which improve sampling efficiency and, thus, signal-to-noise ratio and contrast-to-noise ratio, since it is does not require a closed-form calculation based on geometry. Current medical workstations can display these large four-dimensional studies, however, interactive cine animation of the data is only possible at significantly degraded resolution. Therefore, we also present a method for interactive visualization using powerful graphics cards and distributed processing. Results from volunteer and patient studies demonstrate the advantages of dynamic imaging with high spatial resolution.     

Efficient 3-D TOF PET Reconstruction Using View-Grouped Histo-Images: DIRECT—Direct Image Reconstruction for TOF

For modern time-of-flight (TOF) positron emission tomography (PET) systems, in which the number of possible lines of response and TOF bins is much larger than the number of acquired events, the most appropriate reconstruction approaches are considered to be list-mode methods. However, their shortcomings are relatively high computational costs for reconstruction and for sensitivity matrix calculation. Efficient treatment of TOF data within the proposed DIRECT approach is obtained by 1) angular (azimuthal and co-polar) grouping of TOF events to a set of views as given by the angular sampling requirements for the TOF resolution, and 2) deposition (weighted-histogramming) of these grouped events, and correction data, into a set of ldquohisto-images,rdquo one histo-image per view. The histo-images have the same geometry (voxel grid, size and orientation) as the reconstructed image. The concept is similar to the approach involving binning of the TOF data into angularly subsampled histo-projections - projections expanded in the TOF directions. However, unlike binning into histo-projections, the deposition of TOF events directly into the image voxels eliminates the need for tracing and/or interpolation operations during the reconstruction. Together with the performance of reconstruction operations directly in image space, this leads to a very efficient implementation of TOF reconstruction algorithms. Furthermore, the resolution properties are not compromised either, since events are placed into the image elements of the desired size from the beginning. Concepts and efficiency of the proposed data partitioning scheme are demonstrated in this work by using the DIRECT approach in conjunction with the row-action maximum-likelihood (RAMLA) algorithm.     

Comparison of 3-D reconstruction with 3D-OSEM and with FORE+OSEM for PET

The combination of Fourier rebinning (FORE) and the ordered subsets expectation-maximization (OSEM), a fast statistical algorithm, appears as a promising alternative to the fully three-dimensional (3-D) iterative approach for clinical positron emission tomography (PET) data. In this paper, we evaluated the properties of FORE+OSEM and compared it with fully 3-D OSEM using both simulations and data acquired by commercial scanners. The aim is to determine to what extent the speed advantage of FORE+OSEM is paid for by a possible degradation of image quality in the case of noisy clinical PET data. A forward- and back-projection pair based on a line integral model was used in two-dimensional OSEM and 3-D OSEM (3D-OSEM) instead of a system matrix. Different variants of both approaches have been studied with simulations in terms of contrast-noise tradeoff. Two variants--FORE+OSEM with attenuation weighting (AW) [FORE+OSEM(AW)] and 3D-OSEM with attenuation-normalization weighting (ANSP) and a shifted-Poisson (SP) model [3D-OSEM(ANSP)]--were compared with measured phantom data and patient data. Based on the results from both simulations and measured data, we conclude that: 1) both attenuation (-normalization) weighting and the SP model improve the image quality but slow down the convergence and 2) despite its approximate nature, FORE+OSEM does not show apparent image degradation compared with 3D-OSEM for data with a noise level typical of a whole-body FDG scan.     

心脏体素化三维模型感兴趣血管交互式显示方法研究

通过应用线性八叉树将心脏三维模型离散成体素以快速提取并显示局部任意感兴趣血管,把三维模型离散成体素后,利用多边形交互区域选择感兴趣体积内部体素。根据26-邻接体素的拓扑关系和体素互信息相似度比较结果,最终确定最佳深度和最佳互信息阈值分别为14和-1.375,以此来寻找感兴趣体积中同一组织的体素集合。最终实现了三维医学影像中任意感兴趣血管的精确显示,该技术可被视为用一个提取工具将任意局部三维模型进行提取并进行四维动态显示的过程。实验结果表明:与C-V三维分割算法精度90.1%相比,本分割算法平均精确度达到96.02%;运行时间从13.8s缩短为10.7s;四维播放帧数最大40FPS,基本满足了血管三维分割的临床需求。该算法不仅可以快速地分析局部病灶的生理学特点和病理特征,而且让医生更加直观、便利地观察病人心脏任意局部血管的实际运动状况,以便做出临床决策。     

Compact encoding of 3-D voxel surfaces based on pattern code representation

We propose a lossless compression algorithm for three-dimensional (3-D) binary voxel surfaces, based on the pattern code representation (PCR). In PCR, a voxel surface is represented by a series of pattern codes. The pattern of a voxel v is defined as the 3 /spl times/ 3 /spl times/ 3 array of voxels, centered on v. Therefore, the pattern code for v informs of the local shape of the voxel surface around v. The proposed algorithm can achieve the coding gain, since the patterns of adjacent voxels are highly correlated to each other. The performance of the proposed algorithm is evaluated using various voxel surfaces, which are scan-converted from triangular mesh models. It is shown that the proposed algorithm requires only 0.5/spl sim/1 bits per black voxel (bpbv) to store or transmit the voxel surfaces.     

Gaussian process models of dynamic PET for functional volume definition in radiation oncology

In routine oncologic positron emission tomography (PET), dynamic information is discarded by time-averaging the signal to produce static images of the "standardised uptake value" (SUV). Defining functional volumes of interest (VOIs) in terms of SUV is flawed, as values are affected by confounding factors and the chosen time window, and SUV images are not sensitive to functional heterogeneity of pathological tissues. Also, SUV iso-contours are highly affected by the choice of threshold and no threshold, or other SUV-based segmentation method, is universally accepted for a given VOI type. Gaussian Process (GP) time series models describe macro-scale dynamic behavior arising from countless interacting micro-scale processes, as is the case for PET signals from heterogeneous tissue. We use GPs to model time-activity curves (TACs) from dynamic PET and to define functional volumes for PET oncology. Probabilistic methods of tissue discrimination are presented along with novel contouring methods for functional VOI segmentation. We demonstrate the value of GP models for voxel classification and VOI contouring of diseased and metastatic tissues with functional heterogeneity in prostate PET. Classification experiments reveal superior sensitivity and specificity over SUV calculation and a TAC-based method proposed in recent literature. Contouring experiments reveal differences in shape between gold-standard and GP VOIs and correlation with kinetic models shows that the novel VOIs contain extra clinically relevant information compared to SUVs alone. We conclude that the proposed models offer a principled data analysis technique that improves on SUVs for oncologic VOI definition. Continuing research will generalize GP models for different oncology tracers and imaging protocols with the ultimate goal of clinical use including treatment planning.     

Enhancing the multivariate signal of [15O] water PETstudies with a new nonlinear neuroanatomical registration algorithm [MRIapplication]

This paper addresses the problem of neuro-anatomical registration across individuals for functional [15O] water PET activation studies. A new algorithm for three-dimensional (3-D) nonlinear structural registration (warping) of MR scans is presented. The method performs a hierarchically scaled search for a displacement field, maximizing one of several voxel similarity measures derived from the two-dimensional (2-D) histogram of matched image intensities, subject to a regularizer that ensures smoothness of the displacement field. The effect of the nonlinear structural registration is studied when it is computed on anatomical MR scans and applied to coregistered [15O] water PET scans from the same subjects: in this experiment, a study of visually guided saccadic eye movements. The performance of the nonlinear warp is evaluated using multivariate functional signal and noise measures. These measures prove to be useful for comparing different intersubject registration approaches, e.g., affine versus nonlinear. A comparison of 12-parameter affine registration versus non-linear registration demonstrates that the proposed nonlinear method increases the number of voxels retained in the cross-subject mask. We demonstrate that improved structural registration may result in an improved multivariate functional signal-to-noise ratio (SNR). Furthermore, registration of PET scans using the 12-parameter affine transformations that align the coregistered MR images does not improve registration, compared to 12-parameter affine alignment of the PET images directly.     

A Factor-Image Framework to Quantification of Brain Receptor Dynamic PET Studies

The positron emission tomography (PET) imaging technique enables the measurement of receptor distribution or neurotransmitter release in the living brain and the changes of the distribution with time and thus allows quantification of binding sites as well as the affinity of a radioligand. However, quantification of receptor binding studies obtained with PET is complicated by tissue heterogeneity in the sampling image elements (i.e., voxels, pixels). This effect is caused by a limited spatial resolution of the PET scanner. Spatial heterogeneity is often essential in understanding the underlying receptor binding process. Tracer kinetic modeling also often requires an intrusive collection of arterial blood samples. In this paper, we propose a likelihood-based framework in the voxel domain for quantitative imaging with or without the blood sampling of the input function. Radioligand kinetic parameters are estimated together with the input function. The parameters are initialized by a subspace-based algorithm and further refined by an iterative likelihood-based estimation procedure. The performance of the proposed scheme is examined by simulations. The results show that the proposed scheme provides reliable estimation of factor time-activity curves (TACs) and the underlying parametric images. A good match is noted between the result of the proposed approach and that of the Logan plot. Real brain PET data are also examined, and good performance is observed in determining the TACs and the underlying factor images.     

Fully three-dimensional reconstruction for a PET camera with retractable septa

A fully 3-D reconstruction algorithm has been developed to reconstruct data from a 16 ring PET camera (a Siemens/CTI 953B) with automatically retractable septa. The tomograph is able to acquire coincidences between any pair of detector rings and septa retraction increases the total system count rate by a factor of 7.8 (including scatter) and 4.7 (scatter subtracted) for a uniform, 20 cm diameter cylinder. The reconstruction algorithm is based on 3-D filtered backprojection, expressed in a form suitable for the multi-angle sinogram data. Sinograms which are not measured due to the truncated cylindrical geometry of the tomograph, but which are required for a spatially invariant response function, are obtained by forward projection. After filtering, the complete set of sinograms is backprojected into a 3-D volume of 128x128x31 voxels using a voxel-driven procedure. The algorithm has been validated with simulation, and tested with both phantom and clinical data from the 953B.     

Development of a vertebral endplate 3-D reconstruction technique

The increase of low back problems has stimulated the development of different analysis and evaluation techniques. Among these methods, the direct linear transformation (DLT) technique is commonly used to reconstruct the spine in three dimensions by means of its known image coordinates on radiographs. Despite its efficiency and precision, general reconstruction of some standard anatomical landmarks does not give all the necessary data for a detailed analysis of the intrinsic geometrical characteristics of lumbar vertebrae. Thus, in order to obtain such geometrical information a three-dimensional (3-D) reconstruction vertebral endplate contour technique has been developed. This technique involves: (1) iterative optimization and reconstruction processes of the vertebral endplate centroid; and (2) 3-D reconstruction of vertebral endplate contour. Validation based on mathematical simulations demonstrated that two or three iterations are necessary to correct (within 2 mm) the endplate centroid position for simulated error higher than 10 mm. Other validations based on 3-D reconstructions of a chamfered tube and a dry vertebra contours of known dimensions have given mean errors of 2 mm. Application on a healthy subject demonstrated the potential of this 3-D reconstruction technique. Finally, 3-D data obtained on vertebral endplates would allow the development of new clinical measurements that could be used to evaluate the lumbar spine geometrical behavior and orthoses biomechanical effects     

A methodology for specifying PET VOIs using multimodalitytechniques

Volume-of-interest (VOI) extraction for radionuclide and anatomical measurements requires correct identification and delineation of the anatomical feature being studied. The authors have developed a toolset for specifying three dimensional (3-D) VOIs on a multislice positron emission tomography (PET) dataset. The software is particularly suited for specifying cerebral cortex VOIs which represent a particular gyrus or deep brain structure. A registered 3-D magnetic resonance image (MRI) dataset is used to provide high-resolution anatomical information, both as oblique two-dimensional (2-D) sections and as volume renderings of a segmented cortical surface. VOIs are specified indirectly in two dimensions by drawing a stack of 2-D regions on the MRI data. The regions are tiled together to form closed triangular mesh surface models, which are subsequently transformed into the observation space of the PET scanner. Quantification by this method allows calculation of radionuclide activity in the VOIs, as well as their statistical uncertainties and correlations. The methodology for this type of analysis and validation results are presented     

Effect of Voxel Size and Computation Method on Tc-99m MAA SPECT/CT-Based Dose Estimation for Y-90 Microsphere Therapy

The use of selective internal radiation therapy for treatment of hepatocellular carcinoma and liver metastases using Y-90 labeled microspheres has become an effective and widely used treatment regimen. However, dosimetric evaluations of this treatment are still primitive as uniform distribution models based only on injected activity are often used. This investigation attempts to quantify the effectiveness of several sophisticated patient-specific techniques which utilize the source distribution of Tc-99m MAA simulation studies to perform voxelized dosimetric computations. Among these techniques are complete Monte-Carlo radiation transport computation in patient-specific CT-based voxel phantoms, local energy deposition in patient specific phantoms and kernel transport techniques in water. Each technique was evaluated using three different phantom voxel dimensions and SPECT reconstruction matrix sizes. Dose evaluation results using all methods were compared to the exact solution, obtained using fully 3-D Monte-Carlo simulations with source distribution based not on SPECT data, but on the injected activity and exact boundaries of the anthropomorphic phantom used in the study. The results of this study show that at large voxel sizes and using SPECT reconstructions with a small matrix size (64 $,times,$64), Monte-Carlo and local deposition methods are nearly equivalent. However, using a large SPECT reconstruction matrix (256$,times,$ 256) the local deposition method is significantly more accurate than full 3-D Monte-Carlo transport, and with a negligible computational burden.