A Pipeline for Automated Voxel Dosimetry: Application in Patients with Multi-SPECT/CT Imaging After (177)Lu-Peptide Receptor Radionuclide Therapy.
Patient-specific dosimetry in radiopharmaceutical therapy (RPT) is impeded by the lack of tools that are accurate and practical for the clinic. Our aims were to construct and test an integrated voxel-level pipeline that automates key components (organ segmentation, registration, dose-rate estimation, and curve fitting) of the RPT dosimetry process and then to use it to report patient-specific dosimetry in 177Lu-DOTATATE therapy. Methods: An integrated workflow that automates the entire dosimetry process, except tumor segmentation, was constructed. First, convolutional neural networks (CNNs) are used to automatically segment organs on the CT portion of one post-therapy SPECT/CT scan. Second, local contour intensity-based SPECT--SPECT alignment results in volume-of-interest propagation to other time points. Third, dose rate is estimated by explicit Monte Carlo (MC) radiation transport using the fast, Dose Planning Method code. Fourth, the optimal function for dose-rate fitting is automatically selected for each voxel. When reporting mean dose, we apply partial-volume correction, and uncertainty is estimated by an empiric approach of perturbing segmentations. Results: The workflow was used with 4-time-point 177Lu SPECT/CT imaging data from 20 patients with 77 neuroendocrine tumors, segmented by a radiologist. CNN-defined kidneys resulted in high Dice values (0.91-0.94) and only small differences (2%-5%) in mean dose when compared with manual segmentation. Contour intensity-based registration led to visually enhanced alignment, and the voxel-level fitting had high R 2 values. Across patients, dosimetry results were highly variable; for example, the average of the mean absorbed dose (Gy/GBq) was 3.2 (range, 0.2-10.4) for lesions, 0.49 (range, 0.24-1.02) for left kidney, 0.54 (range, 0.31-1.07) for right kidney, and 0.51 (range, 0.27-1.04) for healthy liver. Patient results further demonstrated the high variability in the number of cycles needed to deliver hypothetical threshold absorbed doses of 23 Gy to kidney and 100 Gy to tumor. The uncertainty in mean dose, attributable to variability in segmentation, averaged 6% (range, 3%-17%) for organs and 10% (range, 3%-37%) for lesions. For a typical patient, the time for the entire process was about 25 min (∼2 min manual time) on a desktop computer, including time for CNN organ segmentation, coregistration, MC dosimetry, and voxel curve fitting. Conclusion: A pipeline integrating novel tools that are fast and automated provides the capacity for clinical translation of dosimetry-guided RPT.
Dewaraja YK
，Mirando DM
，Peterson AB
，Niedbala J
，Millet JD
，Mikell JK
，Frey KA
，Wong KK
，Wilderman SJ
，Nelson AS
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Validation of Monte Carlo (131) I radiopharmaceutical dosimetry workflow using a 3D-printed anthropomorphic head and neck phantom.
Approximately 50% of head and neck cancer (HNC) patients will experience loco-regional disease recurrence following initial courses of therapy. Retreatment with external beam radiotherapy (EBRT) is technically challenging and may be associated with a significant risk of irreversible damage to normal tissues. Radiopharmaceutical therapy (RPT) is a potential method to treat recurrent HNC in conjunction with EBRT. Phantoms are used to calibrate and add quantification to nuclear medicine images, and anthropomorphic phantoms can account for both the geometrical and material composition of the head and neck. In this study, we present the creation of an anthropomorphic, head and neck, nuclear medicine phantom, and its characterization for the validation of a Monte Carlo, SPECT image-based, 131 I RPT dosimetry workflow.
3D-printing techniques were used to create the anthropomorphic phantom from a patient CT dataset. Three 131 I SPECT/CT imaging studies were performed using a homogeneous, Jaszczak, and an anthropomorphic phantom to quantify the SPECT images using a GE Optima NM/CT 640 with a high energy general purpose collimator. The impact of collimator detector response (CDR) modeling and volume-based partial volume corrections (PVCs) upon the absorbed dose was calculated using an image-based, Geant4 Monte Carlo RPT dosimetry workflow and compared against a ground truth scenario. Finally, uncertainties were quantified in accordance with recent EANM guidelines.
The 3D-printed anthropomorphic phantom was an accurate re-creation of patient anatomy including bone. The extrapolated Jaszczak recovery coefficients were greater than that of the 3D-printed insert (∼22.8 ml) for both the CDR and non-CDR cases (with CDR: 0.536 vs. 0.493, non-CDR: 0.445 vs. 0.426, respectively). Utilizing Jaszczak phantom PVCs, the absorbed dose was underpredicted by 0.7% and 4.9% without and with CDR, respectively. Utilizing anthropomorphic phantom recovery coefficient overpredicted the absorbed dose by 3% both with and without CDR. All dosimetry scenarios that incorporated PVC were within the calculated uncertainty of the activity. The uncertainties in the cumulative activity ranged from 23.6% to 106.4% for Jaszczak spheres ranging in volume from 0.5 to 16 ml.
The accuracy of Monte Carlo-based dosimetry for 131 I RPT in HNC was validated with an anthropomorphic phantom. In this study, it was found that Jaszczak-based PVCs were sufficient. Future applications of the phantom could involve 3D printing and characterizing patient-specific volumes for more personalized RPT dosimetry estimates.
Adam DP
，Grudzinski JJ
，Bormett I
，Cox BL
，Marsh IR
，Bradshaw TJ
，Harari PM
，Bednarz BP
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A framework for prediction of personalized pediatric nuclear medical dosimetry based on machine learning and Monte Carlo techniques.
Objective:A methodology is introduced for the development of an internal dosimetry prediction toolkit for nuclear medical pediatric applications. The proposed study exploits Artificial Intelligence techniques using Monte Carlo simulations as ground truth for accurate prediction of absorbed doses per organ prior to the imaging acquisition considering only personalized anatomical characteristics of any new pediatric patient.Approach:GATE Monte Carlo simulations were performed using a population of computational pediatric models to calculate the specific absorbed dose rates (SADRs) in several organs. A simulated dosimetry database was developed for 28 pediatric phantoms (age range 2-17 years old, both genders) and 5 different radiopharmaceuticals. Machine Learning regression models were trained on the produced simulated dataset, with leave one out cross validation for the prediction model evaluation. Hyperparameter optimization and ensemble learning techniques for a variation of input features were applied for achieving the best predictive power, leading to the development of a SADR prediction toolkit for any new pediatric patient for the studied organs and radiopharmaceuticals.Main results. SADR values for 30 organs of interest were calculated via Monte Carlo simulations for 28 pediatric phantoms for the cases of five radiopharmaceuticals. The relative percentage uncertainty in the extracted dose values per organ was lower than 2.7%. An internal dosimetry prediction toolkit which can accurately predict SADRs in 30 organs for five different radiopharmaceuticals, with mean absolute percentage error on the level of 8% was developed, with specific focus on pediatric patients, by using Machine Learning regression algorithms, Single or Multiple organ training and Artificial Intelligence ensemble techniques. Significance: A large simulated dosimetry database was developed and utilized for the training of Machine Learning models. The developed predictive models provide very fast results (<2 s) with an accuracy >90% with respect to the ground truth of Monte Carlo, considering personalized anatomical characteristics and the biodistribution of each radiopharmaceutical. The proposed method is applicable to other medical dosimetry applications in different patients' populations.
Eleftheriadis V
，Savvidis G
，Paneta V
，Chatzipapas K
，Kagadis GC
，Papadimitroulas P
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ResearchGate
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