Symposium on molecular radiotherapy dosimetry: The first of a series?

The EFOMP Special Interest Group for Radionuclide Internal Dosimetry (SIG_FRID) organised its first scientific meeting, the Symposium on Molecular Radiotherapy Dosimetry, in Athens on November 9th-11th 2023. The Symposium was hosted by the Hellenic Association of Medical Physicists and the National and Kapodistrian University of Athens. This meeting gathered more than 180 scientists from 28 countries. Scientific, clinical and regulatory aspects were addressed by 8 invited experts. Two continuous professional development sessions were organised. A special round table gathering medical physics experts, physicians regulatory authority experts and patient representatives addressed the possibilities to increase clinical dosimetry dissemination. The event was supported by companies and a specific industry session allowed sponsors to present their products, innovations and future perspective in this field.

An automatic method to generate voxel-based absorbed doses from radioactivity distributions for nuclear medicine using generative adversarial networks: a feasibility study.

An approach to autogenerate voxel-based absorbed dose for nuclear medicine is proposed using generative adversarial networks. The method is based on image-to-image transformation and promises to achieve real-time visualization of the absorbed dose and optimization of therapeutic strategies. The activity-density superimposed image is input to generator (G) as a reference image to generate a pseudoabsorbed dose image (DI), which is then mixed with ground truth (GT) DI and recognized by discriminator (D). If the pseudoimage is recognized, the information is fed back, and G regenerates a pseudodose image until D drops to obtain a lifelike DI. As a feasibility study, we used the dose distribution of segmented human anatomy from different sources and activities as training and test datasets. The activity source was assumed to be 1, 2, 3, 4, or 7 subsource blocks. The 3-subsource model was used as the test dataset, and the others were used as the training dataset. The activity distribution in the subsource was assumed to be uniform or heterogeneous (i.e., Gaussian diffusion with sigma 0.0, 0.3, or 0.6). Differences were assessed by Gamma analysis. Results showed that the same or quasi-inhomogeneity model can well predict the dose distribution of different activity-inhomogeneity. Although the 1-source model was trained with very few datasets, it showed an optimal balance between accuracy and training efficiency. There were offsets in the mean absorbed dose between the predicted and GT, but they all showed a higher Gamma-pass-rate (> 93%) and ~ 10% std.

Deep Learning Renal Segmentation for Fully Automated Radiation Dose Estimation in Unsealed Source Therapy.

BACKGROUND: Convolutional neural networks (CNNs) have been shown to be powerful tools to assist with object detection and-like a human observer-may be trained based on a relatively small cohort of reference subjects. Rapid, accurate organ recognition in medical imaging permits a variety of new quantitative diagnostic techniques. In the case of therapy with targeted radionuclides, it may permit comprehensive radiation dose analysis in a manner that would often be prohibitively time-consuming using conventional methods. METHODS: An automated image segmentation tool was developed based on three-dimensional CNNs to detect right and left kidney contours on non-contrast CT images. Model was trained based on 89 manually contoured cases and tested on a cohort of patients receiving therapy with 177Lu-prostate-specific membrane antigen-617 for metastatic prostate cancer. Automatically generated contours were compared with those drawn by an expert and assessed for similarity based on dice score, mean distance-to-agreement, and total segmented volume. Further, the contours were applied to voxel dose maps computed from post-treatment quantitative SPECT imaging to estimate renal radiation dose from therapy. RESULTS: Neural network segmentation was able to identify right and left kidneys in all patients with a high degree of accuracy. The system was integrated into the hospital image database, returning contours for a selected study in approximately 90 s. Mean dice score was 0.91 and 0.86 for right and left kidneys, respectively. Poor performance was observed in three patients with cystic kidneys of which only few were included in the training data. No significant difference in mean radiation absorbed dose was observed between the manual and automated algorithms. CONCLUSION: Automated contouring using CNNs shows promise in providing quantitative assessment of functional SPECT and possibly PET images; in this case demonstrating comparable accuracy for radiation dose interpretation in unsealed source therapy relative to a human observer.