The recently developed total-body positron emission tomography (PET) scanner can capture total-body tracer kinetics, thus enabling systems imaging of multiple organs and interactions between organs, simultaneously. However, their prohibitive cost and sitting requirements may present a barrier for widespread adaptation. Most commercial PET scanners have a limited AFOV. To circumvent the limited AFOV, scanner manufacturers have implemented static step and shoot (SSS) protocols to “stitch” images acquired at multiple bed positions into a single total-body image. However, the resulting “total-body” images may not be quantitative depending on the kinetics of the tracers, thus biasing quantitative imaging comparisons. We propose a dynamic step and shoot (DSS) protocol with 2sec temporal sampling to pursue a continuous imaging protocol with different acquisition times in different bed positions for each pass through the torso. D-optimal criterion was used to optimize the acquisition protocol using a simulated annealing algorithm. The overall approach is illustrated in estimating parameters of a reversible two-compartment PET kinetic model for key organs in the torso. The intra- and inter-subject performance of the optimal DSS (ODSS) protocol was compared with the SSS protocol in terms of bias and variability and in comparison to the total-body (TB) protocol. The simulations suggest that the proposed ODSS protocol outperforms the conventional SSS protocol in both intra- and inter-subject parameter accuracy and precision tests and generates similar macro-parameter estimates compared to the TB scanner. Overall, we demonstrate that we can achieve an optimal temporal imaging schedule to support quantitative TB systems imaging.
Preclinical PET imaging is widely used to quantify in vivo biological and metabolic process at molecular level in small animal imaging. In preclinical PET, low-count acquisition has numerous benefits in terms of animal logistics, maintaining integrity in longitudinal multi-tracer studies, and increased throughput. Low-count acquisition can be realized by either decreasing the injected dose or by shortening the acquisition time. However, both these approaches lead to reduced photons, generating PET images with low signal-to-noise ratio (SNR) exhibiting poor image quality, lesion contrast, and quantitative accuracy. This study is aimed at developing a deep-learning (DL) based framework to generate high-count PET (HC-PET) from low-count PET (LC-PET) images using Residual U-Net (RU-Net) and Dilated U-Net (D-Net)-based architectures. Preclinical PET images at different photon count levels were simulated using a stochastic and physics-based method and fed into the framework. The integration of residual learning in the U-Net architecture enhanced feature propagation while the dilated kernels enlarged receptive field-of-view to incorporate multiscale context. Both DL methods exhibited significantly (p≤0.05) better performance in terms of Structural Similarity Index Metric (SSIM), Peak Signal-to-Noise Ratio (PSNR) and Normalized Root Mean Square Error (NRMSE) when compared to existing non-DL denoising techniques such as Non-Local Means (NLM) and BM3D filtering. In objective evaluation of quantification task, the DL-based approaches yielded significantly lower bias in determining the mean standardized uptake value (SUVmean) of liver and tumor lesion than the non-DL approaches. Of the DL frameworks, D-Net based generation of HC-PET had the least bias and coefficient of variation at all photon count levels. Our study suggests that DL can predict HC-PET images with improved visual quality and quantitative accuracy from LC-PET (preclinical) images.
Purpose: One major challenge facing simultaneous positron emission tomography (PET)/ magnetic resonance imaging (MRI) is PET attenuation correction (AC) measurement and evaluation of its accuracy. There is a crucial need for the evaluation of current and emergent PET AC methodologies in terms of absolute quantitative accuracy in the reconstructed PET images.
Approach: To address this need, we developed and evaluated a lesion insertion tool for PET/MRI that will facilitate this evaluation process. This tool was developed for the Biograph mMR and evaluated using phantom and patient data. Contrast recovery coefficients (CRC) from the NEMA IEC phantom of synthesized lesions were compared to measurements. In addition, SUV biases of lesions inserted in human brain and pelvis images were assessed from PET images reconstructed with MRI-based AC (MRAC) and CT-based AC (CTAC).
Results: For cross-comparison PET/MRI scanners AC evaluation, we demonstrated that the developed lesion insertion tool can be harmonized with the GE-SIGNA lesion insertion tool. About <3 % CRC curves difference between simulation and measurement was achieved. An average of 1.6% between harmonized simulated CRC curves obtained with mMR and SIGNA lesion insertion tools was achieved. A range of −5 % to 12% MRAC to CTAC SUV bias was respectively achieved in the vicinity and inside bone tissues in patient images in two anatomical regions, the brain, and pelvis.
Conclusions: A lesion insertion tool was developed for the Biograph mMR PET/MRI scanner and harmonized with the SIGNA PET/MRI lesion insertion tool. These tools will allow for an accurate evaluation of different PET/MRI AC approaches and permit exploration of subtle attenuation correction differences across systems.
Objective evaluation of new and improved methods for PET imaging requires access to images with ground truth, as can be obtained through simulation studies. However, for these studies to be clinically relevant, it is important that the simulated images are clinically realistic. In this study, we develop a stochastic and physics-based method to generate realistic oncological two-dimensional (2-D) PET images, where the ground-truth tumor properties are known. The developed method extends upon a previously proposed approach. The approach captures the observed variabilities in tumor properties from actual patient population. Further, we extend that approach to model intra-tumor heterogeneity using a lumpy object model. To quantitatively evaluate the clinical realism of the simulated images, we conducted a human-observer study. This was a two-alternative forced-choice (2AFC) study with trained readers (five PET physicians and one PET physicist). Our results showed that the readers had an average of ∼ 50% accuracy in the 2AFC study. Further, the developed simulation method was able to generate wide varieties of clinically observed tumor types. These results provide evidence for the application of this method to 2-D PET imaging applications, and motivate development of this method to generate 3-D PET images.
Attenuation compensation (AC) is a pre-requisite for reliable quantification and beneficial for visual interpretation tasks in single-photon emission computed tomography (SPECT). Typical AC methods require the availability of an attenuation map, which is obtained using a transmission scan, such as a CT scan. This has several disadvantages such as increased radiation dose, higher costs, and possible misalignment between SPECT and CT scans. Also, often a CT scan is unavailable. In this context, we and others are showing that scattered photons in SPECT contain information to estimate the attenuation distribution. To exploit this observation, we propose a physics and learning-based method that uses the SPECT emission data in the photopeak and scatter windows to perform transmission-less AC in SPECT. The proposed method uses data acquired in the scatter window to reconstruct an initial estimate of the attenuation map using a physicsbased approach. A convolutional neural network is then trained to segment this initial estimate into different regions. Predefined attenuation coefficients are assigned to these regions, yielding the reconstructed attenuation map, which is then used to reconstruct the activity distribution using an ordered subsets expectation maximization (OSEM)-based reconstruction approach. We objectively evaluated the performance of this method using highly realistic simulation studies conducted on the clinically relevant task of detecting perfusion defects in myocardial perfusion SPECT. Our results showed no statistically significant differences between the performance achieved using the proposed method and that with the true attenuation maps. Visually, the images reconstructed using the proposed method looked similar to those with the true attenuation map. Overall, these results provide evidence of the capability of the proposed method to perform transmissionless AC and motivate further evaluation.
Quantitative and noninvasive measurement of protease activities has remained an imaging challenge in deep tissues such as the lungs. Here, we designed a dual-radiolabeled probe for reporting the activities of proteases such as matrix metalloproteinases (MMPs) with multispectral single photon emission computed tomography (SPECT) imaging. A gold nanoparticle (NP) was radiolabeled with 125I and 111In and functionalized with an MMP9-cleavable peptide to form a multispectral SPECT imaging contrast agent. In another design, incorporation of 199Au radionuclide into the metal crystal structure of gold NPs provided a superior and stable reference signal in lungs, and 111In was linked to the NP surface via a protease-cleavable substrate, which can serve as an enzyme activity reporter. This work reveals strategies to correlate protease activities with diverse pathologies in a tissue-depth independent manner.
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