Voxel-based multimodel fitting method for modeling time activity curves in SPECT images

Enhancing voxel-based dosimetry accuracy with an unsupervised deep learning approach for hybrid medical image registration

BACKGROUND

Deformable registration is required to generate a time-integrated activity (TIA) map which is essential for voxel-based dosimetry. The conventional iterative registration algorithm using anatomical images (e.g., computed tomography (CT)) could result in registration errors in functional images (e.g., single photon emission computed tomography (SPECT) or positron emission tomography (PET)). Various deep learning-based registration tools have been proposed, but studies specifically focused on the registration of serial hybrid images were not found.

PURPOSE

In this study, we introduce CoRX-NET, a novel unsupervised deep learning network designed for deformable registration of hybrid medical images. The CoRX-NET structure is based on the Swin-transformer (ST), allowing for the representation of complex spatial connections in images. Its self-attention mechanism aids in the effective exchange and integration of information across diverse image regions. To augment the amalgamation of SPECT and CT features, cross-stitch layers have been integrated into the network.

METHODS

Two different 177 Lu DOTATATE SPECT/CT datasets were acquired at different medical centers. 22 sets from Seoul National University and 14 sets from Sunway Medical Centre are used for training/internal validation and external validation respectively. The CoRX-NET architecture builds upon the ST, enabling the modeling of intricate spatial relationships within images. To further enhance the fusion of SPECT and CT features, cross-stitch layers have been incorporated within the network. The network takes a pair of SPECT/CT images (e.g., fixed and moving images) and generates a deformed SPECT/CT image. The performance of the network was compared with Elastix and TransMorph using L1 loss and structural similarity index measure (SSIM) of CT, SSIM of normalized SPECT, and local normalized cross correlation (LNCC) of SPECT as metrics. The voxel-wise root mean square errors (RMSE) of TIA were compared among the different methods.

RESULTS

The ablation study revealed that cross-stitch layers improved SPECT/CT registration performance. The cross-stitch layers notably enhance SSIM (internal validation: 0.9614 vs. 0.9653, external validation: 0.9159 vs. 0.9189) and LNCC of normalized SPECT images (internal validation: 0.7512 vs. 0.7670, external validation: 0.8027 vs. 0.8027). CoRX-NET with the cross-stitch layer achieved superior performance metrics compared to Elastix and TransMorph, except for CT SSIM in the external dataset. When qualitatively analyzed for both internal and external validation cases, CoRX-NET consistently demonstrated superior SPECT registration results. In addition, CoRX-NET accomplished SPECT/CT image registration in less than 6 s, whereas Elastix required approximately 50 s using the same PC's CPU. When employing CoRX-NET, it was observed that the voxel-wise RMSE values for TIA were approximately 27% lower for the kidney and 33% lower for the tumor, compared to when Elastix was used.

CONCLUSION

This study represents a major advancement in achieving precise SPECT/CT registration using an unsupervised deep learning network. It outperforms conventional methods like Elastix and TransMorph, reducing uncertainties in TIA maps for more accurate dose assessments.

Fit of biokinetic data in molecular radiotherapy: a machine learning approach

BACKGROUND

In literature are reported different analytical methods (AM) to choose the proper fit model and to fit data of the time-activity curve (TAC). On the other hand, Machine Learning algorithms (ML) are increasingly used for both classification and regression tasks. The aim of this work was to investigate the possibility of employing ML both to classify the most appropriate fit model and to predict the area under the curve (τ).

METHODS

Two different ML systems have been developed for classifying the fit model and to predict the biokinetic parameters. The two systems were trained and tested with synthetic TACs simulating a whole-body Fraction Injected Activity for patients affected by metastatic Differentiated Thyroid Carcinoma, administered with [131I]I-NaI. Test performances, defined as classification accuracy (CA) and percentage difference between the actual and the estimated area under the curve (Δτ), were compared with those obtained using AM varying the number of points (N) of the TACs. A comparison between AM and ML were performed using data of 20 real patients.

RESULTS

As N varies, CA remains constant for ML (about 98%), while it improves for F-test (from 62 to 92%) and AICc (from 50 to 92%), as N increases. With AM, [Formula: see text] can reach down to - 67%, while using ML [Formula: see text] ranges within ± 25%. Using real TACs, there is a good agreement between τ obtained with ML system and AM.

CONCLUSIONS

The employing of ML systems may be feasible, having both a better classification and a better estimation of biokinetic parameters.

On the Use of Artificial Intelligence for Dosimetry of Radiopharmaceutical Therapies

Routine clinical dosimetry along with radiopharmaceutical therapies is key for future treatment personalization. However, dosimetry is considered complex and time-consuming with various challenges amongst the required steps within the dosimetry workflow. The general workflow for image-based dosimetry consists of quantitative imaging, the segmentation of organs and tumors, fitting of the time-activity-curves, and the conversion to absorbed dose. This work reviews the potential and advantages of the use of artificial intelligence to improve speed and accuracy of every single step of the dosimetry workflow.

Lesion Dosimetry for [177Lu]Lu-PSMA-617 Radiopharmaceutical Therapy Combined with Stereotactic Body Radiotherapy in Patients with Oligometastatic Castration-Sensitive Prostate Cancer

A single-institution prospective pilot clinical trial was performed to demonstrate the feasibility of combining [177Lu]Lu-PSMA-617 radiopharmaceutical therapy (RPT) with stereotactic body radiotherapy (SBRT) for the treatment of oligometastatic castration-sensitive prostate cancer. Methods: Six patients with 9 prostate-specific membrane antigen (PSMA)-positive oligometastases received 2 cycles of [177Lu]Lu-PSMA-617 RPT followed by SBRT. After the first intravenous infusion of [177Lu]Lu-PSMA-617 (7.46 ± 0.15 GBq), patients underwent SPECT/CT at 3.2 ± 0.5, 23.9 ± 0.4, and 87.4 ± 12.0 h. Voxel-based dosimetry was performed with calibration factors (11.7 counts per second/MBq) and recovery coefficients derived from in-house phantom experiments. Lesions were segmented on baseline PSMA PET/CT (50% SUVmax). After a second cycle of [177Lu]Lu-PSMA-617 (44 ± 3 d; 7.50 ± 0.10 GBq) and an interim PSMA PET/CT scan, SBRT (27 Gy in 3 fractions) was delivered to all PSMA-avid oligometastatic sites, followed by post-PSMA PET/CT. RPT and SBRT voxelwise dose maps were scaled (α/β = 3 Gy; repair half-time, 1.5 h) to calculate the biologically effective dose (BED). Results: All patients completed the combination therapy without complications. No grade 3+ toxicities were noted. The median of the lesion SUVmax as measured on PSMA PET was 16.8 (interquartile range [IQR], 11.6) (baseline), 6.2 (IQR, 2.7) (interim), and 2.9 (IQR, 1.4) (post). PET-derived lesion volumes were 0.4-1.7 cm3 The median lesion-absorbed dose (AD) from the first cycle of [177Lu]Lu-PSMA-617 RPT (ADRPT) was 27.7 Gy (range, 8.3-58.2 Gy; corresponding to 3.7 Gy/GBq, range, 1.1-7.7 Gy/GBq), whereas the median lesion AD from SBRT was 28.1 Gy (range, 26.7-28.8 Gy). Spearman rank correlation, ρ, was 0.90 between the baseline lesion PET SUVmax and SPECT SUVmax (P = 0.005), 0.74 (P = 0.046) between the baseline PET SUVmax and the lesion ADRPT, and -0.81 (P = 0.022) between the lesion ADRPT and the percent change in PET SUVmax (baseline to interim). The median for the lesion BED from RPT and SBRT was 159 Gy (range, 124-219 Gy). ρ between the BED from RPT and SBRT and the percent change in PET SUVmax (baseline to post) was -0.88 (P = 0.007). Two cycles of [177Lu]Lu-PSMA-617 RPT contributed approximately 40% to the maximum BED from RPT and SBRT. Conclusion: Lesional dosimetry in patients with oligometastatic castration-sensitive prostate cancer undergoing [177Lu]Lu-PSMA-617 RPT followed by SBRT is feasible. Combined RPT and SBRT may provide an efficient method to maximize the delivery of meaningful doses to oligometastatic disease while addressing potential microscopic disease reservoirs and limiting the dose exposure to normal tissues.

Future trends for patient-specific dosimetry methodology in molecular radiotherapy

Molecular radiotherapy is rapidly expanding, and new radiotherapeutics are emerging. The majority of treatments is still performed using empirical fixed activities and not tailored for individual patients. Molecular radiotherapy dosimetry is often seen as a promising candidate that would allow personalisation of treatments as outcome should ultimately depend on the absorbed doses delivered and not the activities administered. The field of molecular radiotherapy dosimetry has made considerable progress towards the feasibility of routine clinical dosimetry with reasonably accurate absorbed-dose estimates for a range of molecular radiotherapy dosimetry applications. A range of challenges remain with respect to the accurate quantification, assessment of time-integrated activity and absorbed dose estimation. In this review, we summarise a range of technological and methodological advancements, mainly focussed on beta-emitting molecular radiotherapeutics, that aim to improve molecular radiotherapy dosimetry to achieve accurate, reproducible, and streamlined dosimetry. We describe how these new technologies can potentially improve the often time-consuming considered process of dosimetry and provide suggestions as to what further developments might be required.

Accuracy and uncertainty analysis of reduced time point imaging effect on time-integrated activity for 177Lu-DOTATATE PRRT in patients and clinically realistic simulations

BACKGROUND

Dosimetry promises many advantages for radiopharmaceutical therapies but repeat post-therapy imaging for dosimetry can burden both patients and clinics. Recent applications of reduced time point imaging for time-integrated activity (TIA) determination for internal dosimetry following ¹⁷⁷Lu-DOTATATE peptide receptor radionuclide therapy have shown promising results that allow for the simplification of patient-specific dosimetry. However, factors such as scheduling can lead to sub-optimal imaging time points, but the resulting impact on dosimetry accuracy is still under investigation. We use four-time point ¹⁷⁷Lu SPECT/CT data for a cohort of patients treated at our clinic to perform a comprehensive analysis of the error and variability in time-integrated activity when reduced time point methods with various combinations of sampling points are employed.

METHODS

The study includes 28 patients with gastroenteropancreatic neuroendocrine tumors who underwent post-therapy SPECT/CT imaging at approximately 4, 24, 96, and 168 h post-therapy (p.t.) following the first cycle of ¹⁷⁷Lu-DOTATATE. The healthy liver, left/right kidney, spleen and up to 5 index tumors were delineated for each patient. Time-activity curves were fit with either monoexponential or biexponential functions for each structure, based on the Akaike information criterion. This fitting was performed using all 4 time points as a reference and various combinations of 2 and 3 time points to determine optimal imaging schedules and associated errors. 2 commonly used methods of single time point (STP) TIA estimation are also evaluated. A simulation study was also performed with data generated by sampling curve fit parameters from log-normal distributions derived from the clinical data and adding realistic measurement noise to sampled activities. For both clinical and simulation studies, error and variability in TIA estimates were estimated with various sampling schedules.

RESULTS

The optimal post-therapy imaging time period for STP estimates of TIA was found to be 3-5 days (71-126 h) p.t. for tumor and organs, with one exception of 6-8 days (144-194 h) p.t. for spleen with one STP approach. At the optimal time point, STP estimates give mean percent errors (MPE) within ± 5% and SD < 9% across all structures with largest magnitude error for kidney TIA (MPE = - 4.1%) and highest variability also for kidney TIA (SD = 8.4%). The optimal sampling schedule for 2TP estimates of TIA is 1-2 days (21-52 h) p.t. followed by 3-5 days (71-126 h) p.t. for kidney, tumor, and spleen. Using the optimal sampling schedule, the largest magnitude MPE for 2TP estimates is 1.2% for spleen and highest variability is in tumor with SD = 5.8%. The optimal sampling schedule for 3TP estimates of TIA is 1-2 days (21-52 h) p.t. followed by 3-5 days (71-126 h) p.t. and 6-8 days (144-194 h) p.t. for all structures. Using the optimal sampling schedule, the largest magnitude MPE for 3TP estimates is 2.5% for spleen and highest variability is in tumor with SD = 2.1%. Simulated patient results corroborate these findings with similar optimal sampling schedules and errors. Many sub-optimal reduced time point sampling schedules also exhibit low error and variability.

CONCLUSIONS

We show that reduced time point methods can be used to achieve acceptable average TIA errors over a wide range of imaging time points and sampling schedules while maintaining low uncertainty. This information can improve the feasibility of dosimetry for ¹⁷⁷Lu-DOTATATE and elucidate the uncertainty associated with non-ideal conditions.

177Lu-DOTATATE Theranostics: Predicting Renal Dosimetry From Pretherapy 68Ga-DOTATATE PET and Clinical Biomarkers

PURPOSE

Pretreatment predictions of absorbed doses can be especially valuable for patient selection and dosimetry-guided individualization of radiopharmaceutical therapy. Our goal was to build regression models using pretherapy 68Ga-DOTATATE PET uptake data and other baseline clinical factors/biomarkers to predict renal absorbed dose delivered by 177Lu-DOTATATE peptide receptor radionuclide therapy (177Lu-PRRT) for neuroendocrine tumors. We explore the combination of biomarkers and 68Ga PET uptake metrics, hypothesizing that they will improve predictive power over univariable regression.

PATIENTS AND METHODS

Pretherapy 68Ga-DOTATATE PET/CTs were analyzed for 25 patients (50 kidneys) who also underwent quantitative 177Lu SPECT/CT imaging at approximately 4, 24, 96, and 168 hours after cycle 1 of 177Lu-PRRT. Kidneys were contoured on the CT of the PET/CT and SPECT/CT using validated deep learning-based tools. Dosimetry was performed by coupling the multi-time point SPECT/CT images with an in-house Monte Carlo code. Pretherapy renal PET SUV metrics, activity concentration per injected activity (Bq/mL/MBq), and other baseline clinical factors/biomarkers were investigated as predictors of the 177Lu SPECT/CT-derived mean absorbed dose per injected activity to the kidneys using univariable and bivariable models. Leave-one-out cross-validation (LOOCV) was used to estimate model performance using root mean squared error and absolute percent error in predicted renal absorbed dose including mean absolute percent error (MAPE) and associated standard deviation (SD).

RESULTS

The median therapy-delivered renal dose was 0.5 Gy/GBq (range, 0.2-1.0 Gy/GBq). In LOOCV of univariable models, PET uptake (Bq/mL/MBq) performs best with MAPE of 18.0% (SD = 13.3%), and estimated glomerular filtration rate (eGFR) gives an MAPE of 28.5% (SD = 19.2%). Bivariable regression with both PET uptake and eGFR gives LOOCV MAPE of 17.3% (SD = 11.8%), indicating minimal improvement over univariable models.

CONCLUSIONS

Pretherapy 68Ga-DOTATATE PET renal uptake can be used to predict post-177Lu-PRRT SPECT-derived mean absorbed dose to the kidneys with accuracy within 18%, on average. Compared with PET uptake alone, including eGFR in the same model to account for patient-specific kinetics did not improve predictive power. Following further validation of these preliminary findings in an independent cohort, predictions using renal PET uptake can be used in the clinic for patient selection and individualization of treatment before initiating the first cycle of PRRT.

Accuracy and uncertainty analysis of reduced time point imaging effect on time-integrated activity for 177Lu-DOTATATE PRRT in clinical patients and realistic simulations

Background. Dosimetry promises many advantages for radiopharmaceutical therapies but repeat post-therapy imaging for dosimetry can burden both patients and clinics. Recent applications of reduced time point imaging for time-integrated activity (TIA) determination for internal dosimetry following ¹⁷⁷ Lu-DOTATATE peptide receptor radionuclide therapy have shown promising results that allow for the simplification of patient-specific dosimetry. However, factors such as scheduling can lead to undesirable imaging time points, but the resulting impact on dosimetry accuracy is unknown. We use four-time point ¹⁷⁷ Lu SPECT/CT data for a cohort of patients treated at our clinic to perform a comprehensive analysis of the error and variability in time-integrated activity when reduced time point methods with various combination of sampling points are employed. Methods. The study includes 28 patients with gastroenteropancreatic neuroendocrine tumors who underwent post-therapy SPECT/CT imaging at approximately 4, 24, 96, and 168 hours post-therapy (p.t.) following the first cycle of ¹⁷⁷ Lu-DOTATATE. The healthy liver, left/right kidney, spleen and up to 5 index tumors were delineated for each patient. Time-activity curves were fit with either monoexponential or biexponential functions for each structure, based on the Akaike information criterion. This fitting was performed using all 4 time points as a reference and various combinations of 2 and 3 time points to determine optimal imaging schedules and associated errors. 2 commonly used methods of single time point (STP) TIA estimation are also evaluated. A simulation study was also performed with data generated by sampling curve fit parameters from log-normal distributions derived from the clinical data and adding realistic measurement noise to sampled activities. For both clinical and simulation studies, error and variability in TIA estimates were estimated with various sampling schedules. Results . The optimal post-therapy imaging time period for STP estimates of TIA was found to be 3-5 days (71-126 h) p.t. for tumor and organs, with one exception of 6-8 days (144-194 h) p.t. for spleen with one STP approach. At the optimal time point, STP estimates give mean percent errors (MPE) within +/-5% and SD < 9% across all structures with largest magnitude error for kidney TIA (MPE=-4.1%) and highest variability also for kidney TIA (SD=8.4%). The optimal sampling schedule for 2TP estimates of TIA is 1-2 days (21-52 h) p.t. followed by 3-5 days (71-126 h) p.t. for kidney, tumor, and spleen. Using the optimal sampling schedule, the largest magnitude MPE for 2TP estimates is 1.2% for spleen and highest variability is in tumor with SD=5.8%. The optimal sampling schedule for 3TP estimates of TIA is 1-2 days (21-52 h) p.t. followed by 3-5 days (71-126 h) p.t. and 6-8 days (144-194 h) p.t. for all structures. Using the optimal sampling schedule, the largest magnitude MPE for 3TP estimates is 2.5% for spleen and highest variability is in tumor with SD=2.1%. Simulated patient results corroborate these findings with similar optimal sampling schedules and errors. Many sub-optimal reduced time point sampling schedules also exhibit low error and variability. Conclusions. We show that reduced time point methods can be used to achieve acceptable average TIA errors over a wide range of imaging time points and sampling schedules while maintaining low uncertainty. This information can improve the feasibility of dosimetry for ¹⁷⁷ Lu-DOTATATE and elucidate the uncertainty associated with non-ideal conditions.

Evolution of Portable Sensors for In-Vivo Dose and Time-Activity Curve Monitoring as Tools for Personalized Dosimetry in Molecular Radiotherapy

Treatment personalization in Molecular Radiotherapy (MRT) relies on pre- and post-treatment SPECT/ PET-based images and measurements to obtain a patient-specific absorbed dose-rate distribution map and its evolution over time. Unfortunately, the number of time points that are available per patient to investigate individual pharmacokinetics is often reduced by limited patient compliance or SPECT or PET/CT scanner availability for dosimetry in busy departments. The adoption of portable sensors for in-vivo dose monitoring during the entire treatment could improve the assessment of individual biokinetics in MRT and, thus, the treatment personalization. The evolution of portable devices, non-SPECT/PET-based options, already used for monitoring radionuclide activity transit and accumulation during therapy with radionuclides (i.e., MRT or brachytherapy), is presented to identify valuable ones, which combined with conventional nuclear medicine imaging systems could be effective in MRT. External probes, integration dosimeters and active detecting systems were included in the study. The devices and their technology, the range of applications, the features and limitations are discussed. Our overview of the available technologies encourages research and development of portable devices and dedicated algorithms for MRT patient-specific biokinetics study. This would represent a crucial advancement towards personalized treatment in MRT.

Quantitative SPECT/CT for dosimetry of peptide receptor radionuclide therapy

Neuroendocrine tumors (NETs) are uncommon malignancies of increasing incidence and prevalence. As these slow growing tumors usually overexpress somatostatin receptors (SSTRs), the use of ⁶⁸Ga-DOTA-peptides (gallium-68 chelated with dodecane tetra-acetic acid to somatostatin), which bind to the SSTRs, allows for PET based imaging and selection of patients for peptide receptor radionuclide therapy (PRRT). PRRT with radiolabeled somatostatin analogues such as ¹⁷⁷Lu-DOTATATE (lutetium-177-[DOTA,Tyr³]-octreotate), is mainly used for the treatment of metastatic or inoperable NETs. However, PRRT is generally administered at a fixed injected activity in order not to exceed dose limits in critical organs, which is suboptimal given the variability in radiopharmaceutical uptake among patients. Advances in SPECT (single photon emission computed tomography) imaging enable the absolute quantitative measure of the true radiopharmaceutical distribution providing for PRRT dosimetry in each patient. Personalized PRRT based on patient-specific dosimetry could improve therapeutic efficacy by optimizing effective tumor absorbed dose while limiting treatment related radiotoxicity.

DblurDoseNet: A deep residual learning network for voxel radionuclide dosimetry compensating for single-photon emission computerized tomography imaging resolution

PURPOSE

Current methods for patient-specific voxel-level dosimetry in radionuclide therapy suffer from a trade-off between accuracy and computational efficiency. Monte Carlo (MC) radiation transport algorithms are considered the gold standard for voxel-level dosimetry but can be computationally expensive, whereas faster dose voxel kernel (DVK) convolution can be suboptimal in the presence of tissue heterogeneities. Furthermore, the accuracies of both these methods are limited by the spatial resolution of the reconstructed emission image. To overcome these limitations, this paper considers a single deep convolutional neural network (CNN) with residual learning (named DblurDoseNet) that learns to produce dose-rate maps while compensating for the limited resolution of SPECT images.

METHODS

We trained our CNN using MC-generated dose-rate maps that directly corresponded to the true activity maps in virtual patient phantoms. Residual learning was applied such that our CNN learned only the difference between the true dose-rate map and DVK dose-rate map with density scaling. Our CNN consists of a 3D depth feature extractor followed by a 2D U-Net, where the input was 11 slices (3.3 cm) of a given Lu-177 SPECT/CT image and density map, and the output was the dose-rate map corresponding to the center slice. The CNN was trained with nine virtual patient phantoms and tested on five different phantoms plus 42 SPECT/CT scans of patients who underwent Lu-177 DOTATATE therapy.

RESULTS

When testing on virtual patient phantoms, the lesion/organ mean dose-rate error and the normalized root mean square error (NRMSE) relative to the ground truth of the CNN method was consistently lower than DVK and MC, when applied to SPECT images. Compared to DVK/MC, the average improvement for the CNN in mean dose-rate error was 55%/53% and 66%/56%; and in NRMSE was 18%/17% and 10%/11% for lesion and kidney regions, respectively. Line profiles and dose-volume histograms demonstrated compensation for SPECT resolution effects in the CNN-generated dose-rate maps. The ensemble noise standard deviation, determined from multiple Poisson realizations, was improved by 21%/27% compared to DVK/MC. In patients, potential improvements from CNN dose-rate maps compared to DVK/MC were illustrated qualitatively, due to the absence of ground truth. The trained residual CNN took about 30 s on a single GPU (Tesla V100) to generate a 512 × 512 × 130 dose-rate map for a patient.

CONCLUSION

The proposed residual CNN, trained using phantoms generated from patient images, has potential for real-time patient-specific dosimetry in clinical treatment planning due to its demonstrated improvement in accuracy, resolution, noise, and speed over the DVK/MC approaches.

Role of Artificial Intelligence in Theranostics:: Toward Routine Personalized Radiopharmaceutical Therapies

We highlight emerging uses of artificial intelligence (AI) in the field of theranostics, focusing on its significant potential to enable routine and reliable personalization of radiopharmaceutical therapies (RPTs). Personalized RPTs require patient-specific dosimetry calculations accompanying therapy. Additionally we discuss the potential to exploit biological information from diagnostic and therapeutic molecular images to derive biomarkers for absorbed dose and outcome prediction; toward personalization of therapies. We try to motivate the nuclear medicine community to expand and align efforts into making routine and reliable personalization of RPTs a reality.

Individual dosimetry system for targeted alpha therapy based on PHITS coupled with microdosimetric kinetic model

BACKGROUND

An individual dosimetry system is essential for the evaluation of precise doses in nuclear medicine. The purpose of this study was to develop a system for calculating not only absorbed doses but also EQDX(α/β) from the PET-CT images of patients for targeted alpha therapy (TAT), considering the dose dependence of the relative biological effectiveness, the dose-rate effect, and the dose heterogeneity.

METHODS

A general-purpose Monte Carlo particle transport code PHITS was employed as the dose calculation engine in the system, while the microdosimetric kinetic model was used for converting the absorbed dose to EQDX(α/β). PHITS input files for describing the geometry and source distribution of a patient are automatically created from PET-CT images, using newly developed modules of the radiotherapy package based on PHITS (RT-PHITS). We examined the performance of the system by calculating several organ doses using the PET-CT images of four healthy volunteers after injecting 18F-NKO-035.

RESULTS

The deposition energy map obtained from our system seems to be a blurred image of the corresponding PET data because annihilation γ-rays deposit their energies rather far from the source location. The calculated organ doses agree with the corresponding data obtained from OLINDA 2.0 within 20%, indicating the reliability of our developed system. Test calculations by replacing the labeled radionuclide from 18F to 211At suggest that large dose heterogeneity in a target volume is expected in TAT, resulting in a significant decrease of EQDX(α/β) for higher-activity injection.

CONCLUSIONS

As an extension of RT-PHITS, an individual dosimetry system for nuclear medicine was developed based on PHITS coupled with the microdosimetric kinetic model. It enables us to predict the therapeutic and side effects of TAT based on the clinical data largely available from conventional external radiotherapy.

Comparison of commercial dosimetric software platforms in patients treated with 177 Lu-DOTATATE for peptide receptor radionuclide therapy

Purpose

The aim of this study was to quantitatively compare five commercial dosimetric software platforms based on the analysis of clinical datasets of patients who benefited from peptide receptor radionuclide therapy (PRRT) with ¹⁷⁷Lu‐DOTATATE (LUTATHERA^®).

Methods

The dosimetric analysis was performed on two patients during two cycles of PRRT with ¹⁷⁷Lu. Single photon emission computed tomography/computed tomography images were acquired at 4, 24, 72, and 192 h post injection. Reconstructed images were generated using Dosimetry Toolkit^® (DTK) from Xeleris™ and HybridRecon‐Oncology version_1.3_Dicom (HROD) from HERMES. Reconstructed images using DTK were analyzed using the same software to calculate time‐integrated activity coefficients (TIAC), and mean absorbed doses were estimated using OLINDA/EXM V1.0 with mass correction. Reconstructed images from HROD were uploaded into PLANET® OncoDose from DOSIsoft, STRATOS from Phillips, Hybrid Dosimetry Module™ from HERMES, and SurePlan™ MRT from MIM. Organ masses, TIACs, and mean absorbed doses were calculated from each application using their recommendations.

Results

The majority of organ mass estimates varied by <9.5% between all platforms. The highest variability for TIAC results between platforms was seen for the kidneys (28.2%) for the two patients and the two treatment cycles. Relative standard deviations in mean absorbed doses were slightly higher compared with those observed for TIAC, but remained of the same order of magnitude between all platforms.

Conclusions

When applying a similar processing approach, results obtained were of the same order of magnitude regardless of the platforms used. However, the comparison of the performances of currently available platforms is still difficult as they do not all address the same parts of the dosimetric analysis workflow. In addition, the way in which data are handled in each part of the chain from data acquisition to absorbed doses may be different, which complicates the comparison exercise. Therefore, the dissemination of commercial solutions for absorbed dose calculation calls for the development of tools and standards allowing for the comparison of the performances between dosimetric software platforms.

Technical Note: Rapid multiexponential curve fitting algorithm for voxel-based targeted radionuclide dosimetry

BACKGROUND

Dosimetry in nuclear medicine often relies on estimating pharmacokinetics based on sparse temporal data. As analysis methods move toward image-based three-dimensional computation, it becomes important to interpolate and extrapolate these data without requiring manual intervention; that is, in a manner that is highly efficient and reproducible. Iterative least-squares solvers are poorly suited to this task because of the computational overhead and potential to optimize to local minima without applying tight constraints at the outset.

METHODOLOGY

This work describes a fully analytical method for solving three-phase exponential time-activity curves based on three measured time points in a manner that may be readily employed by image-based dosimetry tools. The methodology uses a series of conditional statements and a piecewise approach for solving exponential slope directly through measured values in most instances. The proposed algorithm is tested against a purpose-designed iterative fitting technique and linear piecewise method followed by single exponential in a cohort of ten patients receiving ¹⁷⁷ Lu-DOTA-Octreotate therapy.

RESULTS

Tri-exponential time-integrated values are shown to be comparable to previously published methods with an average difference between organs when computed at the voxel level of 9.8 ± 14.2% and -3.6 ± 10.4% compared to iterative and interpolated methods, respectively. Of the three methods, the proposed tri-exponential algorithm was most consistent when regional time-integrated activity was evaluated at both voxel- and whole-organ levels. For whole-body SPECT imaging, it is possible to compute 3D time-integrated activity maps in <5 min processing time. Furthermore, the technique is able to predictably and reproducibly handle artefactual measurements due to noise or spatial misalignment over multiple image times.

CONCLUSIONS

An efficient, analytical algorithm for solving multiphase exponential pharmacokinetics is reported. The method may be readily incorporated into voxel-dose routines by combining with widely available image registration and radiation transport tools.

Particle filter de-noising of voxel-specific time-activity-curves in personalized 177Lu therapy

BACKGROUND

Currently, there is a high interest in ¹⁷⁷Lu targeted radionuclide therapies, which could be attributed to favorable results obtained from ¹⁷⁷Lu compounds targeting neuro-endocrine and prostate tumors. SPECT based dosimetry could be used for deriving dose values for individual voxels, as is the standard in external-beam radiation-therapy (EBRT). For this a time-activity-curve (TAC) at voxel resolution and also a voxel-wise modeling of radiation energy deposition are necessary. But a voxel-wise determination of TACs is problematic, since several confounding factors exist, such as e.g. poor count-statistics or registration inaccuracies, which add noise to the observed activity states. A particle filter (PF) is a class of methods which applies regularization based on a model of the temporal evolution of activity states. The aim of this study is to introduce the application of PFs for de-noising of per-voxel time-activity curves.

METHODS

We applied a PF for de-noising the TACs of 26 patients, who underwent ¹⁷⁷Lu-DOTATOC or -PSMA therapy. The TACs were obtained from fully-quantitative, serial SPECT(/CT) data, acquired at 4h, 24h, 48h, 72h p.i. The model used in the PF was a mono-exponential decay and its free parameters were determined based on objective criteria. The time-integrated activities (TIA) resulting from the PF (PFF) were compared to the results of a mono-exponential fit (SF) of individual voxels in several volumes of interest (kidneys, spleen, tumors). Additionally, an organ-averaged TIA was derived from whole-organ VOIs and subsequent curve-fitting. This whole-organ TIA was also compared to the whole-organ TIAs obtained from summation of the voxel-wise TIAs from PFF and SF.

RESULTS

The number of particles was set to 1000. Optimal values for noise of observations and noise of the model were 0.25 and 0.5, respectively. The deviation of whole-organ TIAs from conventional organ-based dosimetry and the summation of the voxel-wise TIAs was substantial for SF (kidneys -22.3%, spleen -49.6%, tumor -60.0%), as well as for PFF (kidneys -37.1%, spleen -57.9%, tumor -70.9%). The distribution of voxel-wise half-lives resulting from the PFF method was considerably closer to the organ-averaged value, and the number of implausibly long half-lives (>physical HL) was reduced.

CONCLUSION

The PFF leads to voxel-wise half-lives, which are more plausible than those resulting from SF. However, one has to admit that voxel-wise fitting generally leads to considerable deviations from the organ-averaged TIA as obtained by conventional whole-organ evaluation. Unfortunately, we did not have ground-truth TIA of our patient data and proper ground-truth could even be impossible to obtain. Nevertheless, there are strong indicators that particle filtering can be used for reducing voxel-wise TAC noise.