Investigation and optimization of PET-guided SPECT reconstructions for improved radionuclide therapy dosimetry...

Area of Science:

• Nuclear medicine
• Medical imaging
• Radiotherapy

Background:

• Accurate dosimetry is crucial for radionuclide therapy (RNT).
• Single Photon Emission Computed Tomography (SPECT) is used for RNT dosimetry, but image quality can be a limitation.
• SPECTRE (Single Photon Emission Computed Theranostic REconstruction) is a novel approach aiming to improve SPECT image reconstruction.

Purpose of the Study:

• To investigate and optimize the SPECTRE reconstruction approach using the hybrid kernelised expectation maximization (HKEM) algorithm.
• To demonstrate the feasibility of performing algorithm exploration and optimization in 2D for faster development.
• To improve SPECT-based RNT dosimetry estimates.

Main Methods:

• Simulated SPECT data using a NEMA IEC body phantom with 177Lu and a 68Ga PET-prior.
• Investigated HKEM algorithm parameter space for SPECTRE.
• Compared SPECTRE reconstructions with OSEM reconstructions with resolution modelling (OSEM_RM) using metrics like bias, COV, recovery, SNR, and RMSE.
• Evaluated 2D vs. 3D reconstructions and segmentation accuracy using a 42% fixed threshold.

Main Results:

• SPECTRE parameters were optimized, leading to improved image quality and quantitative accuracy.
• SPECTRE reconstructions showed an average reduction in background COV % by a factor of 2.7 (2D) and 3.3 (3D) compared to OSEM_RM.
• Segmentation accuracy improved significantly with SPECTRE (26% volume difference) compared to OSEM_RM (158% volume difference).

Conclusions:

• The SPECTRE reconstruction approach shows significant potential for enhancing SPECT image quality and improving RNT dosimetry estimates, particularly with conventional segmentation.
• Algorithm optimization in 2D provided fast reconstruction times and valuable insights for SPECT data reconstruction using PET side information.
• This study offers the first in-depth exploration of the SPECTRE approach, providing key insights for its application.