The Effects of Mismatch between SPECT and CT Images on Quantitative Activity Estimation – A Simulation Study

Abstract

Background: Quantitative activity estimation is essential in targeted radionuclide therapy dosimetry. Misregistration between SPECT and CT images at the same imaging time point due to patient movement degrades accuracy. This work aims to study the mismatch effects between CT and SPECT data on attenuation correction (AC), volume-of-interest (VOI) delineation and registration for activity estimation.Methods: Nine 4D XCAT phantoms were generated at 1, 24, and 144 hrs post In-111 Zevalin injection, varying in activity distributions, body and organ sizes. Realistic noisy SPECT projections were generated by an analytical projection and reconstructed with quantitative OS-EM method. CT images were shifted from -5 to 5 voxels as well as according to clinical reference corresponding to SPECT images at each time point. For AC effect, mismatched CT images were used for AC in SPECT reconstruction while VOIs were mapped out from matched CTs. For VOI effect, target organs were mapped out using mismatched CTs with matched CTs for AC. For registration effect, non-rigid registrations were performed on sequential mismatched CTs to align corresponding SPECT images, with no AC and VOI mismatch. Bi-exponential curve fitting was performed to obtain time-integrated activity (TIA). Organ activity errors (%OAE) and TIA errors (%TIAE) were calculated.Results: According to clinical reference, %OAE was larger for organs near ribs for AC effect, e.g., -2.58%±0.81% for liver. For VOI effect, %OAE was larger for small and low uptake organs, e.g., -11.94%±10.34% for spleen. %OAE was proportional to mismatch magnitude, e.g., 4.77%±1.41%, 12.01%±3.97% and 42.81%±6.38% for 1-, 2-, and 5-voxel mismatch for lungs. For registration effect, %TIAE were larger when mismatch existed in more numbers of SPECT/CT images, while no substantial difference was observed when using mismatched CT at different time points for registration reference. %TIAE was highest for VOI, followed by registration and AC, e.g., 37.61%±5.08%, 14.25%±7.07% and 1.13%±0.90% respectively for kidneys.Conclusions: The mismatch between CT and SPECT images poses a significant impact on accuracy of quantitative activity estimation in dosimetry, attributed particularly from VOI delineation errors. It is recommended to perform registration between emission and transmission images at the same time point to ensure dosimetric accuracy.

CBCT‐Based synthetic CT image generation using conditional denoising diffusion probabilistic model

“…For the two conditionally trained models with weak adaptive condition, Adap‐Cond‐1 method preserved most anatomy while few distorted structures can be observed, while in contrast Adap‐Cond‐2 scheme cannot even preserve the body contour in some cases because the guidance is too weak at the early stages of sample generation. In the Cycle GAN results, the loss of low‐contrast details, falsely generated features, and distorted structures are observed, which are consistent with previous work 34 …”

“…It has been shown in previously reported works that diffusion models outperform other generative models like GAN in the image generation, including the medical imaging tasks 26,38 . Hallucinations are reported to appear in the generative models like U‐net and GAN, 34,46 which means that the model generates features that are not present in the measured data. The presence of such fake structures can possibly lead to misdiagnosis, false contouring, and inaccurate dose calculation.…”

“…One of the first applications of the diffusion model in medical imaging was to solve inverse problems like sparse‐view CT reconstruction and metal artifact removal 30 . Denoising diffusion probabilistic model (DDPM), 31 one of the most famous diffusion models, has been used for under‐sampled MRI reconstruction, 32,33 conversion between MRI and CT images, 34 low‐dose CT denoising, 35 and 4D CT generation 36 . Compared to GAN and variational autoencoder (VAE) models which are difficult to interpret and train, diffusion models are analytically principled, easy to train, and produce state‐of‐the‐art (SoAT) image quality 37,38 …”

Synthetic CT generation from MRI using 3D transformer‐based denoising diffusion model

“…This improves training stability and results in more authentic output images with higher quality and greater semantic diversity without the need of overwhelmingly hyper‐parameter fine‐turning. Several diffusion‐based generative models 25–28 have been proposed for medical image synthesis, including generating sCT from MRI, and demonstrate state‐of‐the‐art image quality superior to CNN‐based and GAN‐based methods. However, a common drawback plaguing most of these diffusion models is their low efficiency; the substantial amount of time they necessitate to synthesize 2D images (e.g., about 1000 times slower than GAN‐based methods) even significantly escalates when tasked with generating 3D volumes.…”

High-resolution MRI synthesis using a data-driven framework with denoising diffusion probabilistic modeling

“…DDPM, representing a groundbreaking image synthesis model, generates high-quality images without common issues such as blurriness, mode collapse, or the lack of explicit likelihood estimation (Saharia et al 2023, Wu et al 2023. However, Lyu and Wang (2022) highlighted the inherent randomness associated with using DDPM for MRI image generation. Image uncertainty can propagate into radiation treatment planning, leading to an escalation of errors in dose calculation.…”