Resolution-recovery-embedded image reconstruction for a high-resolution animal SPECT system

Monte Carlo-based estimation of patient absorbed dose in 99mTc-DMSA, -MAG3, and -DTPA SPECT imaging using the University of Florida (UF) phantoms

It is generally accepted that children's organs are more sensitive to radiation than adults, due to their growth rate. Therefore, evaluating the absorbed dose in children to avoid irrecoverable damage is highly crucial. In this work, absorbed dose by different organs of children within the SPECT imaging for the 99mTc-MAG3, 99mTc-DTPA, and 99mTc-DMSA that are commonly employed for pediatric patients, were estimated through the use of Monte Carlo simulation and the University of Florida's (UF) voxel-wise phantoms at the ages of 4, 8, 11, and 14-years old. The results showed that the highest absorbed dose was by kidneys and when 99mTc-DMSA was used. Also, the highest and lowest absorbed dose in the organs occurred when 99mTc-DMSA and 99mTc-MAG3 were used, respectively. The simulation results were in good agreement with the ICRP 128 data.

SPECT-MPI iterative denoising during the reconstruction process using a two-phase learned convolutional neural network

PURPOSE

The problem of image denoising in single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) is a fundamental challenge. Although various image processing techniques have been presented, they may degrade the contrast of denoised images. The proposed idea in this study is to use a deep neural network as the denoising procedure during the iterative reconstruction process rather than the post-reconstruction phase. This method could decrease the background coefficient of variation (COV_bkg) of the final reconstructed image, which represents the amount of random noise, while improving the contrast-to-noise ratio (CNR).

METHODS

In this study, a generative adversarial network is used, where its generator is trained by a two-phase approach. In the first phase, the network is trained by a confined image region around the heart in transverse view. The second phase improves the network's generalization by tuning the network weights with the full image size as the input. The network was trained and tested by a dataset of 247 patients who underwent two immediate serially high- and low-noise SPECT-MPI.

RESULTS

Quantitative results show that compared to post-reconstruction low pass filtering and post-reconstruction deep denoising methods, our proposed method can decline the COV_bkg of the images by up to 10.28% and 12.52% and enhance the CNR by up to 54.54% and 45.82%, respectively.

CONCLUSION

The iterative deep denoising method outperforms 2D low-pass Gaussian filtering with an 8.4-mm FWHM and post-reconstruction deep denoising approaches.

Deep learning-based partial volume correction in standard and low-dose positron emission tomography-computed tomography imaging

BACKGROUND

Positron emission tomography (PET) imaging encounters the obstacle of partial volume effects, arising from its limited intrinsic resolution, giving rise to (I) considerable bias, particularly for structures comparable in size to the point spread function (PSF) of the system; and (II) blurred image edges and blending of textures along the borders. We set out to build a deep learning-based framework for predicting partial volume corrected full-dose (FD + PVC) images from either standard or low-dose (LD) PET images without requiring any anatomical data in order to provide a joint solution for partial volume correction and de-noise LD PET images.

METHODS

We trained a modified encoder-decoder U-Net network with standard of care or LD PET images as the input and FD + PVC images by six different PVC methods as the target. These six PVC approaches include geometric transfer matrix (GTM), multi-target correction (MTC), region-based voxel-wise correction (RBV), iterative Yang (IY), reblurred Van-Cittert (RVC), and Richardson-Lucy (RL). The proposed models were evaluated using standard criteria, such as peak signal-to-noise ratio (PSNR), root mean squared error (RMSE), structural similarity index (SSIM), relative bias, and absolute relative bias.

RESULTS

Different levels of error were observed for these partial volume correction methods, which were relatively smaller for GTM with a SSIM of 0.63 for LD and 0.29 for FD, IY with an SSIM of 0.63 for LD and 0.67 for FD, RBV with an SSIM of 0.57 for LD and 0.65 for FD, and RVC with an SSIM of 0.89 for LD and 0.94 for FD PVC approaches. However, large quantitative errors were observed for multi-target MTC with an RMSE of 2.71 for LD and 2.45 for FD and RL with an RMSE of 5 for LD and 3.27 for FD PVC approaches.

CONCLUSIONS

We found that the proposed framework could effectively perform joint de-noising and partial volume correction for PET images with LD and FD input PET data (LD vs. FD). When no magnetic resonance imaging (MRI) images are available, the developed deep learning models could be used for partial volume correction on LD or standard PET-computed tomography (PET-CT) scans as an image quality enhancement technique.

Employing Multiple Low-Dose PET Images (at Different Dose Levels) as Prior Knowledge to Predict Standard-Dose PET Images

The existing deep learning-based denoising methods predicting standard-dose PET images (S-PET) from the low-dose versions (L-PET) solely rely on a single-dose level of PET images as the input of deep learning network. In this work, we exploited the prior knowledge in the form of multiple low-dose levels of PET images to estimate the S-PET images. To this end, a high-resolution ResNet architecture was utilized to predict S-PET images from 6 to 4% L-PET images. For the 6% L-PET imaging, two models were developed; the first and second models were trained using a single input of 6% L-PET and three inputs of 6%, 4%, and 2% L-PET as input to predict S-PET images, respectively. Similarly, for 4% L-PET imaging, a model was trained using a single input of 4% low-dose data, and a three-channel model was developed getting 4%, 3%, and 2% L-PET images. The performance of the four models was evaluated using structural similarity index (SSI), peak signal-to-noise ratio (PSNR), and root mean square error (RMSE) within the entire head regions and malignant lesions. The 4% multi-input model led to improved SSI and PSNR and a significant decrease in RMSE by 22.22% and 25.42% within the entire head region and malignant lesions, respectively. Furthermore, the 4% multi-input network remarkably decreased the lesions' SUVmean bias and SUVmax bias by 64.58% and 37.12% comparing to single-input network. In addition, the 6% multi-input network decreased the RMSE within the entire head region, within the lesions, lesions' SUVmean bias, and SUVmax bias by 37.5%, 39.58%, 86.99%, and 45.60%, respectively. This study demonstrated the significant benefits of using prior knowledge in the form of multiple L-PET images to predict S-PET images.

Preclinical SPECT and SPECT-CT in Oncology

Molecular imaging enables both spatial and temporal understanding of the complex biologic systems underlying carcinogenesis and malignant spread. Single-photon emission tomography (SPECT) is a versatile nuclear imaging-based technique with ideal properties to study these processes in vivo in small animal models, as well as to identify potential drug candidates and characterize their antitumor action and potential adverse effects. Small animal SPECT and SPECT-CT (single-photon emission tomography combined with computer tomography) systems continue to evolve, as do the numerous SPECT radiopharmaceutical agents, allowing unprecedented sensitivity and quantitative molecular imaging capabilities. Several of these advances, their specific applications in oncology as well as new areas of exploration are highlighted in this chapter.

Capabilities of the Monte Carlo Simulation Codes for Modeling of a Small Animal SPECT Camera

Purpose

This study aims to compare Monte Carlo-based codes' characteristics in the determination of the basic parameters of a high-resolution single photon emission computed tomography (HiReSPECT) scanner.

Methods

The geometry of this dual-head gamma camera equipped with a pixelated CsI(Na) scintillator and lead hexagonal hole collimator were accurately described in the GEANT4 Application for the Tomographic Emission (GATE), Monte Carlo N-particle extended (MCNP-X), and simulation of imaging nuclear detectors (SIMIND) codes. We implemented simulation procedures similar to the experimental test for calculation of the energy spectra, spatial resolution, and sensitivity of HiReSPECT by using ^99mTc sources.

Results

The energy resolutions simulated by SIMIND, MCNP-X, and GATE were 17.53, 19.24, and 18.26%, respectively, while it was calculated at 19.15% in experimental test. The average spatial resolutions of the HiReSPECT camera at 2.5 cm from the collimator surface simulated by SIMIND, MCNP-X, and GATE were 3.18, 2.9, and 2.62 mm, respectively, while this parameter was reported at 2.82 mm in the experiment test. The sensitivities simulated by SIMIND, MCNP-X, and GATE were 1.44, 1.27, and 1.38 cps/μCi, respectively, on the collimator surface.

Conclusions

Comparison between simulation and experimental results showed that among these MC codes, GATE enabled to accurately model realistic SPECT system and electromagnetic physical processes, but it required more time and hardware facilities to run simulations. SIMIND was the most flexible and user-friendly code to simulate a SPECT camera, but it had limitations in defining the non-conventional imaging device. The most important characteristics like time and speed of simulation, preciseness of results, and user-friendliness should be considered during simulations.

Spinning slithole collimation for high-sensitivity small animal SPECT: Design and assessment using GATE simulation

PURPOSE

While traditional collimations are widely used in preclinical SPECT imaging, they usually suffer from possessing a low system sensitivity leading to noisy images. In this study, we are aiming at introducing a novel collimator, the slithole, offering a superior resolution-sensitivity tradeoff for small animal SPECT.

METHODS

The collimator was designed for a molecular SPECT scanner, the HiReSPECT. The slithole is a knife-edge narrow long aperture extended across long-axis of the camera's head. To meet the data completeness requirement, the collimator-detector assembly spins at each regular SPECT angle. The collimator was modeled within GATE Monte Carlo simulator and the data acquisition was performed for NEMA Image Quality (IQ) phantom. In addition, a dedicated 3D iterative reconstruction algorithm based upon plane-integral projections was also developed.

RESULTS

The mean sensitivity of the slithole is 285cps/MBq while the current parallel-hole collimator holds a sensitivity of 36cps/MBq at a 30mm distance. The slithole collimation gives rise to a tomographic resolution of 1.8mm compared to a spatial resolution of∼1.7mm for the parallel-hole one (even after resolution modeling). A 1.75 reduction factor in the noise level was observed when the current parallel-hole collimator is replaced by the slithole. Furthermore, quantitative analysis proves that 3 full-iterations of our dedicated image reconstruction lead to optimal image quality. For the largest rod in the NEMA IQ phantom, a recovery coefficient of∼0.83 was obtained.

CONCLUSION

The slithole collimator outperforms the current parallel-hole collimation by exhibiting a better resolution-sensitivity compromise for preclinical SPECT studies.

Implementation of absolute quantification in small-animal SPECT imaging: Phantom and animal studies

PURPOSE

Presence of photon attenuation severely challenges quantitative accuracy in single-photon emission computed tomography (SPECT) imaging. Subsequently, various attenuation correction methods have been developed to compensate for this degradation. The present study aims to implement an attenuation correction method and then to evaluate quantification accuracy of attenuation correction in small-animal SPECT imaging.

METHODS

Images were reconstructed using an iterative reconstruction method based on the maximum-likelihood expectation maximization (MLEM) algorithm including resolution recovery. This was implemented in our designed dedicated small-animal SPECT (HiReSPECT) system. For accurate quantification, the voxel values were converted to activity concentration via a calculated calibration factor. An attenuation correction algorithm was developed based on the first-order Chang's method. Both phantom study and experimental measurements with four rats were used in order to validate the proposed method.

RESULTS

The phantom experiments showed that the error of -15.5% in the estimation of activity concentration in a uniform region was reduced to +5.1% when attenuation correction was applied. For in vivo studies, the average quantitative error of -22.8 ± 6.3% (ranging from -31.2% to -14.8%) in the uncorrected images was reduced to +3.5 ± 6.7% (ranging from -6.7 to +9.8%) after applying attenuation correction.

CONCLUSION

The results indicate that the proposed attenuation correction algorithm based on the first-order Chang's method, as implemented in our dedicated small-animal SPECT system, significantly improves accuracy of the quantitative analysis as well as the absolute quantification.