The effect of volume-of-interest misregistration on quantitative planar activity and dose estimation

ISIT-QA: In Silico Imaging Trial to Evaluate a Low-Count Quantitative SPECT Method Across Multiple Scanner-Collimator Configurations for 223Ra-Based Radiopharmaceutical Therapies

Personalized dose-based treatment planning requires accurate and reproducible noninvasive measurements to ensure safety and effectiveness. Dose estimation using SPECT is possible but challenging for alpha (α)-particle-emitting radiopharmaceutical therapy (α-RPT) because of complex γ-emission spectra, extremely low counts, and various image-degrading artifacts across a plethora of scanner-collimator configurations. Through the incorporation of physics-based considerations and skipping of the potentially lossy voxel-based reconstruction step, a recently developed projection-domain low-count quantitative SPECT (LC-QSPECT) method has the potential to provide reproducible, accurate, and precise activity concentration and dose measures across multiple scanners, as is typically the case in multicenter settings. To assess this potential, we conducted an in silico imaging trial to evaluate the LC-QSPECT method for a 223Ra-based α-RPT, with the trial recapitulating patient and imaging system variabilities. Methods: A virtual imaging trial titled In Silico Imaging Trial for Quantitation Accuracy (ISIT-QA) was designed with the objectives of evaluating the performance of the LC-QSPECT method across multiple scanner-collimator configurations and comparing performance with a conventional reconstruction-based quantification method. In this trial, we simulated 280 realistic virtual patients with bone-metastatic castration-resistant prostate cancer treated with 223Ra-based α-RPT. The trial was conducted with 9 simulated SPECT scanner-collimator configurations. The primary objective of this trial was to evaluate the reproducibility of dose estimates across multiple scanner-collimator configurations using LC-QSPECT by calculating the intraclass correlation coefficient. Additionally, we compared the reproducibility and evaluated the accuracy of both considered quantification methods across multiple scanner-collimator configurations. Finally, the repeatability of the methods was evaluated in a test-retest study. Results: In this trial, data from 268 223RaCl2 treated virtual prostate cancer patients, with a total of 2,903 lesions, were used to evaluate LC-QSPECT. LC-QSPECT provided dose estimates with good reproducibility across the 9 scanner-collimator configurations (intraclass correlation coefficient > 0.75) and high accuracy (ensemble average values of recovery coefficients ranged from 1.00 to 1.02). Compared with conventional reconstruction-based quantification, LC-QSPECT yielded significantly improved reproducibility across scanner-collimator configurations, accuracy, and test-retest repeatability ([Formula: see text] Conclusion: LC-QSPECT provides reproducible, accurate, and repeatable dose estimations in 223Ra-based α-RPT as evaluated in ISIT-QA. These findings provide a strong impetus for multicenter clinical evaluations of LC-QSPECT in dose quantification for α-RPTs.

The effects of mismatch between SPECT and CT images on quantitative activity estimation - A simulation study

BACKGROUND

Quantitative activity estimation is essential in nuclear medicine imaging. Mismatch between SPECT and CT images at the same imaging time point due to patient movement degrades accuracy in both diagnostic studies and target radionuclide therapy dosimetry. This work aims to study the mismatch effects between CT and SPECT data on attenuation correction (AC), volume-of-interest (VOI) delineation, and registration for activity estimation.

METHODS

Nine 4D XCAT phantoms were generated at 1, 24, and 144 h post In-111 Zevalin injection, varying in activity distributions, body sizes, and organ sizes. Realistic noisy SPECT projections were generated by an analytical projector and reconstructed with a quantitative OS-EM method. CT images were shifted, corresponding to SPECT images at each imaging time point, from -5 to 5 voxels and also according to a clinical reference. The effect of mismatched AC maps was evaluated using mismatched CT images for AC in SPECT reconstruction while VOIs were mapped out from matched CTs. The effect of mismatched VOI drawings was evaluated using mismatched CTs to map out target organs while using matched CTs for AC. The effect of mismatched CT images for registration was evaluated by registering sequential mismatched CTs to align corresponding SPECT images, with no AC and VOI mismatch. Bi-exponential curve fitting was performed to obtain time-integrated activity (TIA). Organ activity errors (%OAE) and TIA errors (%TIAE) were calculated.

RESULTS

According to the clinical reference, %OAE was larger for organs near ribs for AC effect. For VOI effect, %OAE was larger for small and low uptake organs. For registration effect, %TIAE were larger when mismatch existed in more numbers of SPECT/CT images, while no substantial difference was observed when using mismatched CT at different imaging time points as registration reference. %TIAE was highest for VOI, followed by registration and AC, e.g., 20.62%±8.61%, 9.33%±4.66% and 1.13%±0.90% respectively for kidneys.

CONCLUSIONS

The mismatch between CT and SPECT images poses a significant impact on the accuracy of quantitative activity estimation, attributed particularly from VOI delineation errors. It is recommended to perform registration between emission and transmission images at the same time point to ensure diagnostic and dosimetric accuracy.

BIGDOSE: software for 3D personalized targeted radionuclide therapy dosimetry

Background

Advance 3D quantitative radionuclide imaging techniques boost the accuracy of targeted radionuclide therapy (TRT) dosimetry to voxel level. The goal of this work is to develop a comprehensive 3D dosimetric software, BIGDOSE, with new features of image registration and virtual CT for patient-specific dosimetry.

Methods

BIGDOSE includes a portable graphical user interface written in Python, integrating (I) input of sequential ECT/CT images; (II) segmentation; (III) non-rigid image registration; (IV) curve fitting and voxel-based integration; (V) dose conversion and (VI) 3D dose analysis. The accuracy of the software was evaluated using a simulation study with 9 XCAT phantoms. We simulated SPECT/CT acquisitions at 1, 12, 24, 72 and 144-hrs post In-111 Zevalin injection with inter-scans misalignments using an analytical projector for medium energy general purpose (MEGP) collimator, modeling attenuation, scatter and collimator-detector response. The SPECT data were reconstructed using quantitative OS-EM method. A CT organ-based registration was performed before the dose calculation. Organ absorbed doses for the corresponding Y-90 therapeutic agent were calculated on target organs and compared with those obtained from OLINDA/EXM, using dose measured from GATE as the gold standard. One patient with In-111 DTPAOC injection as well as two patients with Y-90 microsphere embolization were used to demonstrate the clinical effectiveness of our software.

Results

In the simulation, the organ dose errors of BIGDOSE were -9.59%±9.06%, -8.36±5.82%, -23.41%±6.67%, -6.05%±2.06% for liver, spleen, kidneys and lungs, while they were -25.72%±12.52%, -14.93%±10.91%, -28.63%±12.97% and -45.30%±5.84% for OLINDA/EXM. Cumulative dose volume histograms, dose maps and iso-dose contours provided 3D dose distribution information on the simulated and patient data.

Conclusions

BIGDOSE provides a one-stop platform for voxel-based dose estimation with enhanced functions. It is a promising tool to streamline the current clinical TRT dosimetric practice with high accuracy, incorporating 3D personalized imaging information for improved treatment outcome.

Technical Note: Virtual CT for reducing CT dose in targeted radionuclide therapy dosimetry

PURPOSE

Previously we have shown that using sequential CT images is superior to sequential SPECT for nonrigid registration in three-dimensional (3D) targeted radionuclide therapy (TRT) dosimetry. However, sequential CTs are often not available due to radiation concerns. In this paper, we propose a virtual CT (vCT) method for attenuation and scatter correction, image registration, and segmentation for improved dosimetric accuracy with single CT acquisition.

METHODS

We used a population of nine XCAT phantoms with different In-111 Zevalin biokinetics and anatomical variations for the simulations. An analytical projector was used to simulate sequential SPECT/CT acquisitions for a medium energy general purpose collimator at 1, 12, 24, 72, and 144 h postinjection, modeling attenuation, scatter, and geometric collimator-detector response. The corresponding sequential attenuation maps of the phantoms served as real CT (rCT) images. For vCT generation, we investigated three registration methods, that is, (a) SPECT to SPECT; (b) SPECT to CT, and (c) CT to SPECT, and the optimal time point for single CT acquisition. Difference images and average normalized mean square errors (NMSE) were calculated between different vCTs and their corresponding rCTs. Absorbed dose and dose-volume histograms (DVHs) for critical organs were computed for the rCT, optimized vCT, and conventional single CT (1CT) protocols, respectively, for dosimetric analyses.

RESULTS

For vCT generation, SPECT to SPECT registration with a single CT acquired at the first time point shows the smallest difference and NMSE. For organ absorbed doses, the results of vCT were similar to those of rCT and were superior to 1CT, that is, -0.24 ± 1.56% vs -0.49 ± 1.76% vs -6.37 ± 5.63% for the liver, -1.05 ± 2.89% vs -0.69 ± 2.74% vs -4.87 ± 4.35% for kidneys, respectively. The results of DVHs also showed improvement for all organs using vCTs as compared to the conventional 1CT protocol.

CONCLUSION

The optimized vCT method can effectively increase the TRT dosimetric results if there is only a single CT available in the sequential imaging protocol, reducing the substantial increase in radiation burden from repeated CT scans.

Evaluation of sequential SPECT and CT for targeted radionuclide therapy dosimetry

PURPOSE

In targeted radionuclide therapy (TRT), a prior knowledge of the absorbed dose biodistribution is essential for pre-therapy treatment planning. Previously, we showed that non-rigid organ-by-organ registration in sequential quantitative SPECT images improved dose estimation. This study aims to investigate if sequential CT can further improve TRT dosimetric accuracy.

METHODS

We simulated SPECT/CT acquisitions at 1, 12, 24, 72 and 144 h In-111 Zevalin post-injection using an analytical MEGP projector, modeling attenuation, scatter and collimator-detector response. We later recruited a patient injected with 222 MBq In-111 DTPAOC imaged at 3 SPECT/CT sessions for clinical evaluations. Four registration schemes were evaluated: whole-body-based registration performed on sequential (1) SPECT (WB-SPECT) or (2) CT (WB-CT) images; organ-based registration applied on organs individually segmented from sequential (3) SPECT (O-SPECT) or (4) CT (O-CT) images. Voxel-by-voxel integration was performed followed by Y-90 voxel-S-kernel convolution. Organ-absorbed doses, iso-dose curves, dose-volume histograms (DVHs) were generated for targeted organs for analysis.

RESULTS

In simulation study, organ-absorbed dose errors were (- 8.66 ± 2.83)%, (- 2.51 ± 3.69)%, (- 9.23 ± 3.28)%, (- 7.17 ± 2.53)% for liver, (- 14.81 ± 4.91)%, (- 3.60 ± 4.37)%, (- 18.13 ± 4.44)%, (- 11.34 ± 4.22)% for spleen, for O-SPECT, O-CT, WB-SPECT and WB-CT registrations, respectively. For all organs, O-CT showed superior results. Results of iso-dose contour, DVHs were in accordance with the organ-absorbed doses. In clinical studies, the results were also consistent which showed O-CT method deviated the most from the result with no registration.

CONCLUSIONS

We conclude that if both sequential SPECT/CT scans are available, CT organ-based registration method can more effectively improve the 3D dose estimation. Sequential low-dose CT scans might be considered to be included in the standard TRT protocol.

Development and evaluation of convergent and accelerated penalized SPECT image reconstruction methods for improved dose-volume histogram estimation in radiopharmaceutical therapy

PURPOSE

Three-dimensional (3D) dosimetry has the potential to provide better prediction of response of normal tissues and tumors and is based on 3D estimates of the activity distribution in the patient obtained from emission tomography. Dose-volume histograms (DVHs) are an important summary measure of 3D dosimetry and a widely used tool for treatment planning in radiation therapy. Accurate estimates of the radioactivity distribution in space and time are desirable for accurate 3D dosimetry. The purpose of this work was to develop and demonstrate the potential of penalized SPECT image reconstruction methods to improve DVHs estimates obtained from 3D dosimetry methods.

METHODS

The authors developed penalized image reconstruction methods, using maximum a posteriori (MAP) formalism, which intrinsically incorporate regularization in order to control noise and, unlike linear filters, are designed to retain sharp edges. Two priors were studied: one is a 3D hyperbolic prior, termed single-time MAP (STMAP), and the second is a 4D hyperbolic prior, termed cross-time MAP (CTMAP), using both the spatial and temporal information to control noise. The CTMAP method assumed perfect registration between the estimated activity distributions and projection datasets from the different time points. Accelerated and convergent algorithms were derived and implemented. A modified NURBS-based cardiac-torso phantom with a multicompartment kidney model and organ activities and parameters derived from clinical studies were used in a Monte Carlo simulation study to evaluate the methods. Cumulative dose-rate volume histograms (CDRVHs) and cumulative DVHs (CDVHs) obtained from the phantom and from SPECT images reconstructed with both the penalized algorithms and OS-EM were calculated and compared both qualitatively and quantitatively. The STMAP method was applied to patient data and CDRVHs obtained with STMAP and OS-EM were compared qualitatively.

RESULTS

The results showed that the penalized algorithms substantially improved the CDRVH and CDVH estimates for large organs such as the liver compared to optimally postfiltered OS-EM. For example, the mean squared errors (MSEs) of the CDRVHs for the liver at 5 h postinjection obtained with CTMAP and STMAP were about 15% and 17%, respectively, of the MSEs obtained with optimally filtered OS-EM. For the CDVH estimates, the MSEs obtained with CTMAP and STMAP were about 16% and 19%, respectively, of the MSEs from OS-EM. For the kidneys and renal cortices, larger residual errors were observed for all algorithms, likely due to partial volume effects. The STMAP method showed promising qualitative results when applied to patient data.

CONCLUSIONS

Penalized image reconstruction methods were developed and evaluated through a simulation study. The study showed that the MAP algorithms substantially improved CDVH estimates for large organs such as the liver compared to optimally postfiltered OS-EM reconstructions. For small organs with fine structural detail such as the kidneys, a large residual error was observed for both MAP algorithms and OS-EM. While CTMAP provided marginally better MSEs than STMAP, given the extra effort needed to handle misregistration of images at different time points in the algorithm and the potential impact of residual misregistration, 3D regularization methods, such as that used in STMAP, appear to be a more practical choice.

EQPlanar: a maximum-likelihood method for accurate organ activity estimation from whole body planar projections

Optimizing targeted radionuclide therapy requires patient-specific estimation of organ doses. The organ doses are estimated from quantitative nuclear medicine imaging studies, many of which involve planar whole body scans. We have previously developed the quantitative planar (QPlanar) processing method and demonstrated its ability to provide more accurate activity estimates than conventional geometric-mean-based planar (CPlanar) processing methods using physical phantom and simulation studies. The QPlanar method uses the maximum likelihood-expectation maximization algorithm, 3D organ volume of interests (VOIs), and rigorous models of physical image degrading factors to estimate organ activities. However, the QPlanar method requires alignment between the 3D organ VOIs and the 2D planar projections and assumes uniform activity distribution in each VOI. This makes application to patients challenging. As a result, in this paper we propose an extended QPlanar (EQPlanar) method that provides independent-organ rigid registration and includes multiple background regions. We have validated this method using both Monte Carlo simulation and patient data. In the simulation study, we evaluated the precision and accuracy of the method in comparison to the original QPlanar method. For the patient studies, we compared organ activity estimates at 24 h after injection with those from conventional geometric mean-based planar quantification using a 24 h post-injection quantitative SPECT reconstruction as the gold standard. We also compared the goodness of fit of the measured and estimated projections obtained from the EQPlanar method to those from the original method at four other time points where gold standard data were not available. In the simulation study, more accurate activity estimates were provided by the EQPlanar method for all the organs at all the time points compared with the QPlanar method. Based on the patient data, we concluded that the EQPlanar method provided a substantial increase in accuracy of organ activity estimates from 24 h planar images compared to the CPlanar using 24 h SPECT as the golden standard. For other time points, where no golden standard is available, better agreement between estimated and measured projections was observed by using the EQPlanar method compared to the QPlanar method. This phenomenon is consistent with the improvement in goodness of fit seen in both simulation data and 24 h patient data. Therefore, this indicates the improved reliability of organ activity estimates obtained though the EQPlanar method.