CT-free attenuation and Monte-Carlo based scatter correction-guided quantitative <sup>90</sup>Y-SPECT imaging for improved dose calculation using deep learning.

Authors

Mansouri Z,Salimi Y,Wolf NB,Mainta I,Zaidi H

Affiliations (5)

  • Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, CH-1211, Switzerland.
  • Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, CH-1211, Switzerland. [email protected].
  • Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, Netherlands. [email protected].
  • Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark. [email protected].
  • University Research and Innovation Center, Óbuda University, Budapest, Hungary. [email protected].

Abstract

This work aimed to develop deep learning (DL) models for CT-free attenuation and Monte Carlo-based scatter correction (AC, SC) in quantitative <sup>90</sup>Y SPECT imaging for improved dose calculation. Data of 190 patients who underwent <sup>90</sup>Y selective internal radiation therapy (SIRT) with glass microspheres was studied. Voxel-level dosimetry was performed on uncorrected and corrected SPECT images using the local energy deposition method. Three deep learning models were trained individually for AC, SC, and joint ASC using a modified 3D shifted-window UNet Transformer (Swin UNETR) architecture. Corrected and unorrected dose maps served as reference and as inputs, respectively. The data was split into train set (~ 80%) and unseen test set (~ 20%). Training was conducted in a five-fold cross-validation scheme. The trained models were tested on the unseen test set. The model's performance was thoroughly evaluated by comparing organ- and voxel-level dosimetry results between the reference and DL-generated dose maps on the unseen test dataset. The voxel and organ-level evaluations also included Gamma analysis with three different distances to agreement (DTA (mm)) and dose difference (DD (%)) criteria to explore suitable criteria in SIRT dosimetry using SPECT. The average ± SD of the voxel-level quantitative metrics for AC task, are mean error (ME (Gy)): -0.026 ± 0.06, structural similarity index (SSIM (%)): 99.5 ± 0.25, and peak signal to noise ratio (PSNR (dB)): 47.28 ± 3.31. These values for SC task are - 0.014 ± 0.05, 99.88 ± 0.099, 55.9 ± 4, respectively. For ASC task, these values are as follows: -0.04 ± 0.06, 99.57 ± 0.33, 47.97 ± 3.6, respectively. The results of voxel level gamma evaluations with three different criteria, namely "DTA: 4.79, DD: 1%", "DTA:10 mm, DD: 5%", and "DTA: 15 mm, DD:10%" were around 98%. The mean absolute error (MAE (Gy)) for tumor and whole normal liver across tasks are as follows: 7.22 ± 5.9 and 1.09 ± 0.86 for AC, 8 ± 9.3 and 0.9 ± 0.8 for SC, and 11.8 ± 12.02 and 1.3 ± 0.98 for ASC, respectively. We developed multiple models for three different clinically scenarios, namely AC, SC, and ASC using the patient-specific Monte Carlo scatter corrected and CT-based attenuation corrected images. These task-specific models could be beneficial to perform the essential corrections where the CT images are either not available or not reliable due to misalignment, after training with a larger dataset.

Topics

Deep LearningMonte Carlo MethodTomography, Emission-Computed, Single-PhotonYttrium RadioisotopesScattering, RadiationImage Processing, Computer-AssistedRadiation DosageJournal Article

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