Iterative Reconstruction of SPECT Brain with Priors Based on MRI T1 and T2 Images
Purpose: Although brain Single Photon Emission Computed Tomography (SPECT) exam is a low cost, widely used functional and molecular brain imaging application, it also has poor spatial resolution (64 x 64 or 128 x 128, pixel sizes about 3 - 6 mm) and a noisy signal. As a result, the SPECT brain images may not be quantitatively accurate for radiotracer uptake, mainly gray matter (GM) and white matter (WM). Many studies have considered improving SPECT quantification by incorporating Magnetic Resonance Imaging (MRI) images into SPECT images. MRI has much higher spatial resolution (192x192 or 256 x 256, pixel sizes 1 to 1.5 mm), which is useful in correcting partial-volume degradation of SPECT quantification. MRI also provides broader image contrast options with many different types of MRI sequences, typically in T1 weighted (T1WI) and T2 weighted (T2WI) sequences. In most previous studies into the use of MRI or CT images to generate the anatomical priors for SPECT/ Positron Emission Tomography (PET) image reconstruction, only a single MRI sequence has been considered. Few studies have investigated the effects of different MRI sequence on the anatomical prior and the resulting SPECT/PET based on the different MRI sequences. In the present study, we evaluate the SPECT brain images at the midbrain level, with the anatomical priors based on the MRI T1WI gradient echo (GE) images and T2WI fast spin echo (FSE) images.
Materials and methods: Source brain images were downloaded from BrainWeb for SPECT image simulation. These included fuzzy gray matter and white matter models for digital radiotracer phantom creation, MRI T1WI and T2WI images for use in SPECT image-reconstruction anatomical priors. The images were selected at the midbrain level and converted to 32 bit to be used in SPECT-MAP. In SPECT-MAP, a ground truth radiotracer phantom was generated for Tc99m-ECD brain perfusion studies. Based on the phantom, SPECT projection data were simulated. These simulations modeled noise and spatial resolution. SPECT images were then reconstructed by maximum a posteriori (MAP). The prior probability distributions were generated from either gradient-echo T1 or fast-spin-echo T2 MRI images. The MAP objective function was optimized using an iterative coordinate descent (ICD) algorithm. SPECT images were also reconstructed by ordered subsets expectation maximization (OSEM). Reconstructed images were compared to the true phantom radiotracer distribution by visual inspection profiles, root mean square error (RMSE), and gray matter to white matter contrast to deviation ratio (CDR).
Results: After 17 iterations, the RMSE for method T1 MAP, T2 MAP and OSEM was 1266.4, 1752.6 and 3231.9. The CDR for method T1 MAP, T2 MAP and OSEM was 10.1, 7.8 and 2.8, which in the digital radiotracer phantom was 25.1. Relative to the T2-based prior, utilizing the T1-based prior for SPECT image reconstruction improved RMSE and CDR by 18% and 29% respectively. Relative to the best iterations for OSEM, the T1-based prior improved RMSE by 43% and CDR by a factor of 2.6. Visually, the SPECT image reconstructed with the T1-based prior was closest to the true phantom distribution, notably capturing certain structures that were not well reconstructed using the T2 image. Both MAP images were superior to OSEM visually and by RMSE and CDR.
Conclusion: The quality of SPECT images reconstructed utilizing MRI images depends substantially on the MRI sequence utilized. For this study, gradient-echo T1 MRI provided more accurate SPECT image reconstruction than fast-spin-echo T2 MRI. Both MRI sequences resulted in better RMSE and CDR than OSEM without use of MRI. The CSF signal distorted MRI boundaries relative to radiotracer boundaries, particularly for MRI T2 sequences. A T2 FLAIR-like images improved boundary alignment and SPECT reconstructed image as compared to T2 MRI images when they were used in anatomical priors for SPECT image reconstruction.
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