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.

Simplified image-based dosimetry using planar images and patient-specific S-values.

Background: Single time point measurement approach and hybrid dosimetry were proposed to simplify the dosimetry process. It is anticipated that utilizing patient-specific S-value would enable more accurate dosimetry assessment based on imaging compared to using the conventional MIRD S-values.

Purpose: We performed planar image-based dosimetry scaled with a single SPECT image for the entire treatment cycle using patient-specific S-values (PSS dosimetry) of organs.

Voxel-Based Internal Dosimetry for Lu-Labeled Radiopharmaceutical Therapy Using Deep Residual Learning.

Purpose: In this study, we propose a deep learning (DL)-based voxel-based dosimetry method in which dose maps acquired using the multiple voxel S-value (VSV) approach were used for residual learning.

Methods: Twenty-two SPECT/CT datasets from seven patients who underwent Lu-DOTATATE treatment were used in this study. The dose maps generated from Monte Carlo (MC) simulations were used as the reference approach and target images for network training.

Comparison of voxel S-value methods for personalized voxel-based dosimetry of Lu-DOTATATE.

Purpose: Voxel-based dosimetry is potentially accurate than organ-based dosimetry because it considers the anatomical variations in each individual and the heterogeneous radioactivity distribution in each organ. Here, voxel-based dosimetry for Lu-DOTATATE therapy was performed using single and multiple voxel S-value (VSV) methods and compared with Monte Carlo simulations. To verify these methods, we adopted sequential Lu-DOTATATE single-photon emission computed tomography and X-ray computed tomography (SPECT/CT) dataset acquired from Sunway Medical Centre using the major vendor's SPECT/CT scanner (Siemens Symbia Intevo).