Lu-177 single-photon emission computed tomography (SPECT) is usually reconstructed from the 208 keV photopeak alone, with triple-energy-window (TEW) scatter correction. Both conventions limit precision: (i) counts from the 113 keV photopeak are discarded, and (ii) the TEW estimate carries the noise of two low-count windows. We propose joint Monte Carlo (MC) reconstruction of the two photopeaks, which removes both limitations by recovering the 113 keV counts and replacing the noisy TEW estimate with a system model that implicitly models scatter. The precision gained from the photopeak windows and from use of an MC system model was measured separately. Ordered-subset expectation maximization was applied to the 113 keV window, the 208 keV window or both jointly, using (i) an analytical projector with TEW scatter correction or (ii) an MC photon-transport projector as the system model. The six reconstruction types were compared on 52 repeated acquisitions of an image quality phantom and on 14 independent 500 MBq-equivalent realizations of a [^177Lu]Lu-DOTATATE patient acquisition. Precision was expressed as the speed-up factor by which acquisition time could be shortened at equal scan-to-scan coefficient of variation, relative to analytical 208 keV reconstruction. In the phantom, joint MC reconstruction yielded the largest mean speed-up factor ( 2.73 ± 0.23 ) and the highest contrast-to-noise ratio in every sphere. This factor was approximately the product of two gains: 1.87 (analytical) or 1.96 (MC) from adding the 113 keV window, and 1.38 to 1.47 from the MC system model. Reconstructed alone, the 113 keV window was worse (analytical, 0.63 ± 0.08 ) or no better (MC, 0.93 ± 0.11 ), despite 35% more raw counts. In the patient, joint MC reconstruction reached a mean speed-up factor of 3.25 ± 0.61 over seven regions, and MC reconstruction yielded 5.7 to 9.5
Accurate myocardial segmentation is essential for polar map-based quantitative analysis in SPECT myocardial perfusion imaging (MPI). Commercial software requires significant manual adjustment to ensure accurate segmentation, particularly for studies with severe myocardial defects or high extracardiac uptake. To address this limitation, we propose deep learning (DL)-based myocardial segmentation using non-contrast-enhanced CT images. Myocardial masks were generated by simply thresholding PET images with uniform myocardial uptake in 125 18F-FDG PET/CT studies. An optimal network was configured through the nn-UNet pipeline with CT images as input and the masks as labels. For evaluation, the network was tested on 50 99mTc-sestamibi SPECT/CT MPI studies with uniform myocardial uptake. Additional 50 SPECT MPI studies with severe myocardial defects or high extracardiac uptake were selected to demonstrate the impact of using DL-masks on polar map-based quantification. Three nuclear cardiologists visually compared myocardium contours with and without DL-masks in commercial software. DL-masks achieved a Dice Similarity Coefficient of 0.866 ± 0.035 in the PET/CT validation set and 0.846 ± 0.026 in the SPECT/CT test set. For additional studies, segmental differences in the polar map with and without DL-masks indicated meaningful quantitative differences ( 10
Purpose To date, some studies have employed deep learning techniques to directly generate dynamic positron emission tomography (PET) parametric images from static PET. Compared with traditional methods, this approach requires only a single PET/computed tomography (CT) scan. However, current methods tend to employ static PET images captured at fixed scanning times without considering whether static PET images acquired at different scanning times impact the quality of the dynamic PET parametric images generated via deep learning.Methods A single-frame image from dynamic -FAPI-04 total-body PET can actually be regarded as a static PET image acquired during that specific period of the PET/CT scan. We extracted 5 frames of dynamic -FAPI-04 total-body PET at equal intervals, specifically frames 50, 76, 80, 86, and 92. Each frame of the dynamic -FAPI-04 total-body PET images was subsequently input into the deep learning model to obtain dynamic -FAPI-04 total-body PET parametric images. By comparing and analyzing the quality of dynamic PET parametric images, we attempted to determine the optimal scan time for static PET.Results The experimental results revealed that the dynamic -FAPI-04 total-body PET parametric image generated from the 58th frame of the dynamic PET image via the deep learning model exhibited the poorest performance. This may be because the diffusion of the radioactive tracer was not stable at the time of PET/CT scanning in frame 58, but it was relatively stable from frames 70-92.Conclusion Static PET images acquired at different scanning times do indeed affect the quality of the dynamic -FAPI-04 total-body PET parametric images generated via deep learning. Specifically, when the radioactive tracer is unstable during the early stage of scanning, the quality of the dynamic -FAPI-04 total-body PET parametric images generated from static PET images via deep learning appears to be inferior.
Introduction Administration activities of Lu-177-PSMA were chosen cautiously in the treatment of advanced prostate cancer. The primary aim was to investigate the association between increased injected activities, and the calculated absorbed doses (AD). Methods In this single center, retrospective analysis, patients with PSMA-avid metastatic castration-resistant prostate cancer treated with [Lu-177]Lu-PSMA-I&T were included. Patients underwent multiple timepoint SPECT/CT imaging at 24, 48, and 72 h post-injection. Patients included were injected with 7.4, or 9.1 GBq. Segmentations were completed manually for the kidneys (CT-based), while a 3.0 SUV threshold-based approach was used for lesions. Dosimetry calculations were then performed using a multiple-timepoint protocol. Manual verification of the individual segmentations coregistered between timepoints was completed. All time activity data was fit using a mono-exponential function. Time-integrated activity was then convolved with voxel-s-value using a predefined Lu-177 kernel resulting the AD in Gy. No further partial volume corrections were made. Results 76 patients treated were included: 22 and 54 were injected with 9.1 +/- 0.3 and 7.4 +/- 0.3 GBq respectively. The AD/GBq to the kidney was comparable, 0.29 +/- 0.08 vs. 0.33 +/- 0.13 Gy/GBq [p=0.193] (9.1 vs. 7.4 GBq). The tumor AD/GBq was comparable between 9.1 and 7.4 GBq cohorts: total tumor burden (0.95 +/- 0.42 vs. 0.92 +/- 0.52 Gy/GBq) [p=0.302]; bone lesions 0.78 +/- 0.42 vs. 0.74 +/- 0.46 Gy/GBq [p=<0.001]; hottest lesion in each patient 1.37 +/- 0.61 vs. 1.30 +/- 0.74 [p=0.421]; coldest lesion in each patient 0.40 +/- 0.16 vs. 0.50 +/- 0.32 Gy/GBq [p=0.930]. Conclusion The AD/GBq across the disease burden, individual lesions, and kidney remained comparable even with increased injected activity. No evidence of the tumor sink effect was found. No evidence of a clinically relevant tumor sink effect in patients with <2000 mL tumor volume was found but patients with substantially high TTV (> 2 L) lacks.
Background Radionuclide therapy is an increasingly used treatment modality for patients with disseminated cancer diseases. Tumor-binding radiopharmaceuticals labelled with the alpha-particle emitter astatine-211 (211At) are of interest to enable high radiation absorbed doses to small tumors. The alpha particles emitted by 211At will deposit all their energy in a short-range including neighboring cells, causing localized irradiation. Due to being part of group 17 the halogen family with iodine, free 211At accumulates in the thyroid gland, which is the main normal organ at risk. It is therefore of great importance to estimate the radiation absorbed dose accurately to the thyroid gland for free 211At. The aims of the work were to determine microdosimetric quantities for uniform and non-uniform 211At distributions within different models of the thyroid gland using the Monte Carlo (MC) codes AlfaMC and GEometry ANd Tracking (Geant4), and to compare the results with previously published data using Monte Carlo N-Particle eXtended (MCNPX). AlfaMC is customized for calculating microdosimetric quantities for alpha-particles.Background Radionuclide therapy is an increasingly used treatment modality for patients with disseminated cancer diseases. Tumor-binding radiopharmaceuticals labelled with the alpha-particle emitter astatine-211 (211At) are of interest to enable high radiation absorbed doses to small tumors. The alpha particles emitted by 211At will deposit all their energy in a short-range including neighboring cells, causing localized irradiation. Due to being part of group 17 the halogen family with iodine, free 211At accumulates in the thyroid gland, which is the main normal organ at risk. It is therefore of great importance to estimate the radiation absorbed dose accurately to the thyroid gland for free 211At. The aims of the work were to determine microdosimetric quantities for uniform and non-uniform 211At distributions within different models of the thyroid gland using the Monte Carlo (MC) codes AlfaMC and GEometry ANd Tracking (Geant4), and to compare the results with previously published data using Monte Carlo N-Particle eXtended (MCNPX). AlfaMC is customized for calculating microdosimetric quantities for alpha-particles.Results Both a single and a multiple thyroid follicle model designed for man, rat and mouse were used from previously published studies. Monte Carlo simulations and absorbed dose-calculations were performed for different distributions of 211At within the thyroid models. The mean specific energy, single-hit mean specific energy and single-hit specific energy distribution were calculated for follicle cell nuclei in models for each species.Conclusion The overall agreement between AlfaMC and MCNPX results, and between AlfaMC and Geant4 results, was found to be within 5% and 2%, respectively. The intercomparison suggested an effective way for validation of microdosimetry results; of AlfaMC validation, a code designed with a focus on reducing the calculation time.
