Use of Integrated SPECT/CT Imaging for Tumor Dosimetry in I-131 Radioimmunotherapy: A Pilot Patient Study

Abstract Integrated systems combining functional (single-photon emission computed tomography; SPECT) imaging with anatomic (computed tomography; CT) imaging have the potential to greatly improve the accuracy of dose estimation in radionuclide therapy. In this article, we present the methodology for highly patient-specific tumor dosimetry by utilizing such a system and apply it to a pilot study of 4 follicular lymphoma patients treated with I-131 tositumomab. SPECT quantification included three-dimensional ordered-subset expectation-maximization reconstruction and CT-defined tumor outlines at each time point. SPECT/CT images from multiple time points were coupled to a Monte Carlo algorithm to calculate a mean tumor dose that incorporated measured changes in tumor volume. The tumor shrinkage, defined as the difference between volumes drawn on the first and last CT scan (a typical time period of 15 days) was in the range 5%-49%. The therapy-delivered mean tumor-absorbed dose was in the range 146-334cGy. For comparison, the therapy dose was also calculated by assuming a static volume from the initial CT and was found to underestimate this dose by up to 47%. The agreement between tracer-predicted and therapy-delivered tumor-absorbed dose was in the range 7%-21%. In summary, malignant lymphomas can have dramatic tumor regression within days of treatment, and advanced imaging methods allow for a highly patient-specific tumor-dosimetry calculation that accounts for this regression.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/78152/1/cbr.2008.0568.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

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

Recovery of total I-131 activity within focal volumes using SPECT and 3D OSEM

We experimentally investigated the SPECT recovery of I-131 activity in multiple spheres located simultaneously at different locations within a cylindrical phantom that had an elliptical cross section. The sphere volumes ranged from 209 cc down to 4.2 cc. A Prism 3000 camera and two types of parallel-hexagonal-hole collimation were employed: high energy (HE) and ultra high energy (UHE). Using appropriately-different 3D models of the point source response function for the two types of collimation, approximately the same recovery of activity could be achieved with either collimation by 3D OSEM reconstruction. The recovery coefficient was greater with no background activity in the phantom by 0.10, on average, compared to that with background. In the HE collimation case, the activity recovery was considerably better for all volumes using 3D OSEM reconstruction than it had been in the past using 1D SAGE reconstruction. Recovery-coefficient-based correction in a simulated patient case involving spherical tumours moderately improved the activity estimates (average error reduced from 14% to 9% for UHE collimation, and from 15% to 11% for HE collimation). For a test case with HE collimation, increasing the projection-image sampling density while decreasing the image voxel size increased the recovery coefficient by 0.075 on average, and, if used in a full set of calibration measurements of recovery coefficient versus volume, might lead to further improvement in accuracy for the patient case.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/58098/2/pmb7_3_017.pd

Training End-to-End Unrolled Iterative Neural Networks for SPECT Image Reconstruction

Training end-to-end unrolled iterative neural networks for SPECT image reconstruction requires a memory-efficient forward-backward projector for efficient backpropagation. This paper describes an open-source, high performance Julia implementation of a SPECT forward-backward projector that supports memory-efficient backpropagation with an exact adjoint. Our Julia projector uses only ~5% of the memory of an existing Matlab-based projector. We compare unrolling a CNN-regularized expectation-maximization (EM) algorithm with end-to-end training using our Julia projector with other training methods such as gradient truncation (ignoring gradients involving the projector) and sequential training, using XCAT phantoms and virtual patient (VP) phantoms generated from SIMIND Monte Carlo (MC) simulations. Simulation results with two different radionuclides (90Y and 177Lu) show that: 1) For 177Lu XCAT phantoms and 90Y VP phantoms, training unrolled EM algorithm in end-to-end fashion with our Julia projector yields the best reconstruction quality compared to other training methods and OSEM, both qualitatively and quantitatively. For VP phantoms with 177Lu radionuclide, the reconstructed images using end-to-end training are in higher quality than using sequential training and OSEM, but are comparable with using gradient truncation. We also find there exists a trade-off between computational cost and reconstruction accuracy for different training methods. End-to-end training has the highest accuracy because the correct gradient is used in backpropagation; sequential training yields worse reconstruction accuracy, but is significantly faster and uses much less memory.Comment: submitted to IEEE TRPM

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

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

Update on HE vs UHE Collimation for Focal Total-activity Quantification in I-131 SPECT Using 3D OSEM

We calibrated a scintillation camera for the counts-to-activity conversion factor, CF, by measuring a phantom consisting of a sphere containing a known 131-I activity placed within an elliptical cylinder. Within a 3D OSEM reconstruction algorithm, we employed a depth-dependent detector-response model based on smooth fits to the point-source-response function. Using the ultra-high-energy (UHE) collimator and 100 iterations, the recovery coefficient, RC, appeared to be 1 for any sphere volume down to 20 cm3. The CF changed only a small amount as the background-over-target activity concentration ratio, b, increased for both UHE and high-energy (HE) collimation. Tests of activity quantification were carried out with an anthropomorphic phantom simulating a 100 cm3 spherical tumor centrally located inferior to the lungs. With 3D OSEM reconstruction, using the global-average CF and no RC-based correction, mean bias in the simulated-tumor activity estimate over 20 realizations was -7.4% with UHE collimation, and -9.4% with HE collimation. For comparison, with 1D SAGE reconstruction, using the CF corresponding to the experimental estimate of b and RC-based correction, the mean bias was worse, -10.7% for UHE collimation, but better, -4.3 %, for HE collimation.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85907/1/Fessler190.pd

VIDA: A Voxel-Based Dosimetry Method for Targeted Radionuclide Therapy Using Geant4

We have developed the Voxel-Based Internal Dosimetry Application (VIDA) to provide patient-specific dosimetry in targeted radionuclide therapy performing Monte Carlo simulations of radiation transport with the Geant4 toolkit. The code generates voxel-level dose rate maps using anatomical and physiological data taken from individual patients. Voxel level dose rate curves are then fit and integrated to yield a spatial map of radiation absorbed dose. In this article, we present validation studies using established dosimetry results, including self-dose factors (DFs) from the OLINDA/EXM program for uniform activity in unit density spheres and organ self- and cross-organ DFs in the Radiation Dose Assessment Resource (RADAR) reference adult phantom. The comparison with reference data demonstrated agreement within 5% for self-DFs to spheres and reference phantom source organs for four common radionuclides used in targeted therapy (131I, 90Y, 111In, 177Lu). Agreement within 9% was achieved for cross-organ DFs. We also present dose estimates to normal tissues and tumors from studies of two non-Hodgkin Lymphoma patients treated by 131I radioimmunotherapy, with comparison to results generated independently with another dosimetry code. A relative difference of 12% or less was found between methods for mean absorbed tumor doses accounting for tumor regression.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140327/1/cbr.2014.1713.pd