Browsing by Author "Bowsher, James Edwin"

Purpose: Single photon emission computed tomography (SPECT) is considered as a functional nuclear medicine imaging technique which is commonly used in the clinic. However, it suffers from low resolution and high noise because of the physical structure and photon scatter and attenuation. This research aims to develop a compact neural network reconstructing SPECT images from projection data, with better resolution and low noise. Methods and Materials: This research developed a MATLAB program to generate 2-D brain phantoms. We totally generated 20,000 2-D phantoms and corresponding projection data. Furthermore, those projection data were processed with Gaussian filter and Poisson noise to simulate the real clinical situation. And 16,000 of them were used to train the neural network, 2,000 for validation, and the final 2,000 for testing. To simulate the real clinical situation, there are five groups of projection data with decreasing acquisition views are used to train the network. Inspired by the SPECTnet, we used a two-step training strategy for network design. The full-size phantom images (128×128 pixels) were compressed into a vector (256×1) at first, then they were decompressed to full-size images again. This process was achieved by the AutoEncoder (AE) consisting of encoder and decoder. The compressed vector generated by the encoder works as targets in the second network, which map projection to compressed images. Then those compressed vectors corresponding to the projection were reconstructed to full-size images by the decoder. Results: A total of 10,000 testing dataset divided into 5 groups with 360 degrees, 180 degrees, 150 degrees, 120 degrees and 90 degrees acquisition, respectively, are generated by the developed neural network. Results were compared with those generated by conventional FBP methods. Compared with FBP algorithm, the neural network can provide reconstruction images with high resolution and low noise, even if under the limited-angles acquisitions. In addition, the new neural network had a better performance than SPECTnet. Conclusions: The network successfully reconstruct projection data to activity images. Especially for the groups whose view angles is less than 180 degrees, the reconstruction images by neural network have the same excellent quality as other images reconstructed by projection data over 360 degrees, even has a higher efficiency than the SPECTnet. Keywords: SPECT; SPECT image reconstruction; Deep learning; convolution neural network. Purpose: Single photon emission computed tomography (SPECT) is considered as a functional nuclear medicine imaging technique which is commonly used in the clinic. However, it suffers from low resolution and high noise because of the physical structure and photon scatter and attenuation. This research aims to develop a compact neural network reconstructing SPECT images from projection data, with better resolution and low noise. Methods and Materials: This research developed a MATLAB program to generate 2-D brain phantoms. We totally generated 20,000 2-D phantoms and corresponding projection data. Furthermore, those projection data were processed with Gaussian filter and Poisson noise to simulate the real clinical situation. And 16,000 of them were used to train the neural network, 2,000 for validation, and the final 2,000 for testing. To simulate the real clinical situation, there are five groups of projection data with decreasing acquisition views are used to train the network. Inspired by the SPECTnet, we used a two-step training strategy for network design. The full-size phantom images (128×128 pixels) were compressed into a vector (256×1) at first, then they were decompressed to full-size images again. This process was achieved by the AutoEncoder (AE) consisting of encoder and decoder. The compressed vector generated by the encoder works as targets in the second network, which map projection to compressed images. Then those compressed vectors corresponding to the projection were reconstructed to full-size images by the decoder. Results: A total of 10,000 testing dataset divided into 5 groups with 360 degrees, 180 degrees, 150 degrees, 120 degrees and 90 degrees acquisition, respectively, are generated by the developed neural network. Results were compared with those generated by conventional FBP methods. Compared with FBP algorithm, the neural network can provide reconstruction images with high resolution and low noise, even if under the limited-angles acquisitions. In addition, the new neural network had a better performance than SPECTnet. Conclusions: The network successfully reconstruct projection data to activity images. Especially for the groups whose view angles is less than 180 degrees, the reconstruction images by neural network have the same excellent quality as other images reconstructed by projection data over 360 degrees, even has a higher efficiency than the SPECTnet. Keywords: SPECT; SPECT image reconstruction; Deep learning; convolution neural network.

AbstractPinhole imaging is a widely used method for high spatial resolution single gamma imaging with a small required field of view (FOV). Many factors affect pinhole imaging: (I) the geometric parameters of the pinhole imaging system, such as the pinhole diameter, focal length and opening angle; (II) the position, range, sampling interval, and sampling time of the pinhole trajectory; and (III) the image reconstruction algorithm. These differences result in different trade-offs between resolution, sensitivity, noise level, imaging FOV, and data-sampling integrity levels. In pinhole imaging, many different pinhole trajectories might be considered. The conventional approach to assessing different trajectories is to reconstruct images from the various trajectories and then assess which image is best. Such an approach is however time consuming, since (I) image reconstruction is time-consuming and (II) image analysis often requires ensembles of images, where the ensemble is time consuming to calculate, consumes considerable computer storage, and requires investigator time to organize and analyze. The object of this project is to develop a method to rapidly select the optimal SPECT pinhole trajectory from among several candidate trajectories. Equivalent Resolution Geometric Efficiency (ERGE) is proposed to represent the spatial resolution and geometric efficiency; a higher ERGE means a better trajectory. To verify this metric, two-dimensional and three-dimensional visualizations of the pinhole trajectory are implemented in software as a way to assess trajectories visually and qualitatively. Several different trajectories are employed, projection data are computer-simulated, including spatial resolution blurring and pseudo-random Poisson noise, and image reconstruction is performed using the OSEM algorithm. The reconstructed images are analyzed to characterize the performance of the different trajectories to assess whether the best trajectory can be determined by the sensitivity and resolution characteristics of the individual pinhole locations that make up the trajectory. Ultimately, the method proved to be effective. In this study, a relatively simple low-cost prospective method for selecting the optimal SPECT pinhole trajectory has been shown to be effective. Only very fast and simple calculations, utilizing Microsoft Excel for example, are required. The method does not require simulating or acquiring projection data and does not require image reconstruction. The ranking of ERGE matches well with the ranking of reconstructed images based on Root Mean Square Error (RMSE). In clinical and scientific research, many different pinhole trajectories might be considered for pinhole 3D SPECT imaging, but it is too time-consuming to assess each trajectory via reconstructed images. By demonstrating the validity of this method for assessing trajectories, it may facilitate the improved use of 3D pinhole SPECT imaging in clinical and scientific research. Keywords: Pinhole Trajectory, SPECT, Forward Projection, OSEM, Equivalent Resolution Geometric Efficiency (ERGE), Root Mean Square Error (RMSE).

Purpose: Early detection and prevention of differentiated thyroid cancer using thyroidectomy and ablation therapy can reduce disease persistence and recurrence. A preablation I-131 scan performed between the thyroidectomy and ablation therapy may improve patient management. Although I-123 would involve less radiation dose, I-131 is widely available, and its long half-life enables imaging 24-72 hours after injection, which is crucial for dosimetry and which enhances visibility of distant metastasis. Typically, 2-10 mCi I-131 is administered. To avoid stunning effects, 2 mCi is suggested. However, with 2 mCi, images are noisy. Compared with standard single pinhole SPECT systems, 7-pinhole systems may provide greater geometric efficiency when using smaller pinhole diameter and thereby reduce noise. Due to the size of 7-pinhole systems, collision constraints may, however, increase the pinhole radius of rotation (ROR), thereby reducing efficiency. Herein we assess the competing effects of more pinholes and larger ROR to determine whether 7 pinholes could meaningfully improve efficiency, at a comparable or better spatial resolution.

Methods: Radiotracer distributions and attenuation were computer simulated using modified XCAT phantom. Single-pinhole and 7-pinhole trajectories were developed to provide minimal RORs while avoiding collision with the patient. Reconstructed images were computer simulated, modeling attenuation, spatial resolution, and Poisson noise. Single-pinhole and 7-pinhole were compared for a range of lesion sizes and activity concentrations. Comparison metrics included lesion conspicuity, uniformity, and contrast; and image quality in terms of noise, contrast recovery and RMSE. Gamma camera sensitivity and spatial resolution were also assessed.

Results: In this study, seven-pinhole configurations were compared to a clinically typical single-pinhole system. In the low-count study, it was found that the seven-pinhole system with 4-5 mm pinhole diameter could outperform the benchmark single-pinhole system. In the high-count study, it was found that seven-pinhole system with 3 mm pinhole diameter could outperform the benchmark single-pinhole system. However, ROR increases are great enough to substantially decrease the benefit of seven pinholes, for the pinhole configuration considered here.

Conclusion: Seven pinhole maybe suitable for preablation scan because high sensitivity allows better detect the lesion with low activity concentration and smaller pinhole diameter allows better resolve the metastasis.

Brain Positron emission tomography (PET) has been widely employed for the clinic diagnosis of Alzheimer's disease (AD). Studies have shown that PET imaging is helpful in differentiating healthy elderly individuals, mild cognitive impairment (MCI) individuals, and AD individuals (Nordberg, Rinne, Kadir, & Långström, 2010). However, PET image quality and quantitative accuracy is degraded from partial volume effects (PVEs), which are due to the poor spatial resolution of PET. As a result, the compensation of PVEs in PET may be of great significance in the improvement of early diagnosis of AD. There are many different approaches available to address PVEs including region-based methods and voxel-based methods. In this study, a voxel-based PVE compensation technique using high-resolution anatomical images was investigated. The high-resolution anatomical images could be computed tomography (CT) or magnetic resonance imaging (MRI) images. Such methods have been proposed and investigated in many studies (Vunckx et al., 2012). However, relatively little research has been done on comparing the effects of different MRI images on voxel-based PVE correction methods. In this study, we compare the effect of 6 different MRI image protocols on PVE compensation in PET images. The MRI protocols compared in this study are T1-, T2-, proton-density (PD)-weighted and 3 different inversion recovery MRI protocols.

Results: OSEM and MAP/ICD images with isotropic prior are blurry and/or noisy. Compared with the OSEM and MAP/ICD images obtained by using an isotropic prior, the PET image reconstructed using anatomical information show better contrast and less noise. Visually, the PET image reconstructed with the ZeroCSF prior gave the PET image that visually appears to match best with the PET phantom. PET images reconstructed with T2, PD and ZeroWM image are similar to one another in image quality, but relative to the PET phantom and the ZeroCSF PET image, these images have poor contrast between CSF pockets and surrounding GM tissue, and they have less contrast between GM and WM. PET image reconstructed with T1 image had a better GM and CSF contrast, some of the CSF pockets in GM were reconstructed, but the WM region was very noisy. PET images reconstructed with ZeroGM image had noticeably worse performance on the GM reconstruction. Analysis suggest that these effects are caused by differences in tissue contrast with different MRI protocols

Keywords: PET, MRI, partial volume effect, image reconstruction, SPECT, Alzheimer's disease.

Onboard cone beam computed tomography (CBCT) has become a widespread means of three-dimensional target localization for radiation therapy; however, it is susceptible to metal artifacts and beam-hardening artifacts that can hinder visualization of low contrast anatomy. Dual-CBCT provides easy access to techniques that may reduces such artifacts. Additionally, dual-CBCT can decrease imaging time and provide simultaneous orthogonal projections which may also be useful for fast target localization. However, dual-CBCT will suffer from large increases in scattered radiation due to the addition of the second source.

An experimental bench top dual CBCT system was constructed so that each imaging chain in the dual CBCT system mimics the geometry of gantry-mounted CBCT systems commonly used in the radiation therapy room. The two systems share a common axis of rotation and are mounted orthogonally. Custom control software was developed to ensure reproducible exposure and rotation timings. This software allows the implementation of the acquisition sequences required for the cross scatter avoidance and correction strategies studied.

Utilizing the experimental dual CBCT system cross scatter was characterized from 70-145 kVp in projections and reconstructed images using this system and three cylindrical phantoms (15cm, 20cm, and 30cm) with a common Catphan core. A novel strategy for avoiding cross-scatter in dual-CBCT was developed that utilized interleaved data acquisition on each imaging chain. Contrast and contrast-to-noise-ratio were measured in reconstructions to evaluate the effectiveness of this strategy to avoid the effects of cross scatter.

A novel correction strategy for cross scatter was developed wherein the cross scatter was regularly sampled during the course of data acquisition and these samples were used as the basis for low- and high- frequency corrections for the cross-scatter in projections. The cross scatter sampling interval was determined for an anthropomorphic phantom at three different sites relevant to radiation therapy by estimating the angular Nyquist frequency. The low frequency portion of the cross scatter distribution is interpolated between samples to provide an estimate of the cross scatter distribution at every projection angle and was then subtracted from the projections.

The high-frequency portion of the correction was applied after the low-frequency correction was applied. The novel high-frequency correction utilizes the fact that a direct estimate of the high-frequency components was obtained in the cross scatter samples. The high-frequency components of the measured cross scatter were subtracted from the projections in the Fourier domain, a process referred to as spectral subtraction. Each projection is corrected using the cross scatter sample taken at the closest projection angle. In order to apply this correction in the Fourier domain the high-frequency component of the cross scatter must be approximately stationary. To improve the stationarity of the high-frequency cross scatter component a novel two-dimensional, overlapping window was developed. The spectral subtraction was then applied in each window and the results added to form the final image.

The effectiveness of the correction techniques were evaluated by measuring the contrast and contrast-to-noise-ratio in an image quality phantom. Additionally, the effect of the high-frequency correction on resolution was measured using a line pair phantom.

Cross scatter in dual CBCT was shown for large phantoms to be much higher than forward scatter which has long been known to be one of the largest degrading factors of image quality in CBCT. This results in large losses of contrast and CNR in reconstructed images. The interleaving strategy for avoiding cross scatter during projection acquisition showed similar performance to cross scatter free acquisitions, however, does not acquire projections at the maximum possible rate. For those applications in which maximizing the acquisition rate of projections is important, the low- and high-frequency corrections effectively mitigated the effects of cross scatter in the dual CBCT system.

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.

On-board image guidance, such as cone-beam CT (CBCT) and kV/MV 2D imaging, is essential in many radiation therapy procedures, such as intensity modulated radiotherapy (IMRT) and stereotactic body radiation therapy (SBRT). These imaging techniques provide predominantly anatomical information for treatment planning and target localization. Recently, studies have shown that treatment planning based on functional and molecular information about the tumor and surrounding tissue could potentially improve the effectiveness of radiation therapy. However, current on-board imaging systems are limited in their functional and molecular imaging capability. Single Photon Emission Computed Tomography (SPECT) is a candidate to achieve on-board functional and molecular imaging. Traditional SPECT systems typically take 20 minutes or more for a scan, which is too long for on-board imaging. A robotic multi-pinhole SPECT system was proposed in this dissertation to provide shorter imaging time by using a robotic arm to maneuver the multi-pinhole SPECT system around the patient in position for radiation therapy.

A 49-pinhole collimated SPECT detector and its shielding were designed and simulated in this work using the computer-aided design (CAD) software. The trajectories of robotic arm about the patient, treatment table and gantry in the radiation therapy room and several detector assemblies such as parallel holes, single pinhole and 49 pinholes collimated detector were investigated. The rail mounted system was designed to enable a full range of detector positions and orientations to various crucial treatment sites including head and torso, while avoiding collision with linear accelerator (LINAC), patient table and patient.

An alignment method was developed in this work to calibrate the on-board robotic SPECT to the LINAC coordinate frame and to the coordinate frames of other on-board imaging systems such as CBCT. This alignment method utilizes line sources and one pinhole projection of these line sources. The model consists of multiple alignment parameters which maps line sources in 3-dimensional (3D) space to their 2-dimensional (2D) projections on the SPECT detector. Computer-simulation studies and experimental evaluations were performed as a function of number of line sources, Radon transform accuracy, finite line-source width, intrinsic camera resolution, Poisson noise and acquisition geometry. In computer-simulation studies, when there was no error in determining angles (α) and offsets (ρ) of the measured projections, the six alignment parameters (3 translational and 3 rotational) were estimated perfectly using three line sources. When angles (α) and offsets (ρ) were provided by Radon transform, the estimation accuracy was reduced. The estimation error was associated with rounding errors of Radon transform, finite line-source width, Poisson noise, number of line sources, intrinsic camera resolution and detector acquisition geometry. The estimation accuracy was significantly improved by using 4 line sources rather than 3 and also by using thinner line-source projections (obtained by better intrinsic detector resolution). With 5 line sources, median errors were 0.2 mm for the detector translations, 0.7 mm for the detector radius of rotation, and less than 0.5° for detector rotation, tilt and twist. In experimental evaluations, average errors relative to a different, independent registration technique were about 1.8 mm for detector translations, 1.1 mm for the detector radius of rotation (ROR), 0.5° and 0.4° for detector rotation and tilt, respectively, and 1.2° for detector twist.

Simulation studies were performed to investigate the improvement of imaging sensitivity and accuracy of hot sphere localization for breast imaging of patients in prone position. A 3D XCAT phantom was simulated in the prone position with nine hot spheres of 10 mm diameter added in the left breast. A no-treatment-table case and two commercial prone breast boards, 7 and 24 cm thick, were simulated. Different pinhole focal lengths were assessed for root-mean-square-error (RMSE). The pinhole focal lengths resulting in the lowest RMSE values were 12 cm, 18 cm and 21 cm for no table, thin board, and thick board, respectively. In both no table and thin board cases, all 9 hot spheres were easily visualized above background with 4-minute scans utilizing the 49-pinhole SPECT system while seven of nine hot spheres were visible with the thick board. In comparison with parallel-hole system, our 49-pinhole system shows reduction in noise and bias under these simulation cases. These results correspond to smaller radii of rotation for no-table case and thinner prone board. Similarly, localization accuracy with the 49-pinhole system was significantly better than with the parallel-hole system for both the thin and thick prone boards. Median localization errors for the 49-pinhole system with the thin board were less than 3 mm for 5 of 9 hot spheres, and less than 6 mm for the other 4 hot spheres. Median localization errors of 49-pinhole system with the thick board were less than 4 mm for 5 of 9 hot spheres, and less than 8 mm for the other 4 hot spheres.

Besides prone breast imaging, respiratory-gated region-of-interest (ROI) imaging of lung tumor was also investigated. A simulation study was conducted on the potential of multi-pinhole, region-of-interest (ROI) SPECT to alleviate noise effects associated with respiratory-gated SPECT imaging of the thorax. Two 4D XCAT digital phantoms were constructed, with either a 10 mm or 20 mm diameter tumor added in the right lung. The maximum diaphragm motion was 2 cm (for 10 mm tumor) or 4 cm (for 20 mm tumor) in superior-inferior direction and 1.2 cm in anterior-posterior direction. Projections were simulated with a 4-minute acquisition time (40 seconds per each of 6 gates) using either the ROI SPECT system (49-pinhole) or reference single and dual conventional broad cross-section, parallel-hole collimated SPECT. The SPECT images were reconstructed using OSEM with up to 6 iterations. Images were evaluated as a function of gate by profiles, noise versus bias curves, and a numerical observer performing a forced-choice localization task. Even for the 20 mm tumor, the 49-pinhole imaging ROI was found sufficient to encompass fully usual clinical ranges of diaphragm motion. Averaged over the 6 gates, noise at iteration 6 of 49-pinhole ROI imaging (10.9 µCi/ml) was approximately comparable to noise at iteration 2 of the two dual and single parallel-hole, broad cross-section systems (12.4 µCi/ml and 13.8 µCi/ml, respectively). Corresponding biases were much lower for the 49-pinhole ROI system (3.8 µCi/ml), versus 6.2 µCi/ml and 6.5 µCi/ml for the dual and single parallel-hole systems, respectively. Median localization errors averaged over 6 gates, for the 10 mm and 20 mm tumors respectively, were 1.6 mm and 0.5 mm using the ROI imaging system and 6.6 mm and 2.3 mm using the dual parallel-hole, broad cross-section system. The results demonstrate substantially improved imaging via ROI methods. One important application may be gated imaging of patients in position for radiation therapy.

A robotic SPECT imaging system was constructed utilizing a gamma camera detector (Digirad 2020tc) and a robot (KUKA KR150-L110 robot). An imaging study was performed with a phantom (PET CT PhantomTM), which includes 5 spheres of 10, 13, 17, 22 and 28 mm in diameter. The phantom was placed on a flat-top couch. SPECT projections were acquired with a parallel-hole collimator and a single-pinhole collimator both without background in the phantom, and with background at 1/10th the sphere activity concentration. The imaging trajectories of parallel-hole and pinhole collimated detectors spanned 180 degrees and 228 degrees respectively. The pinhole detector viewed a 14.7 cm-diameter common volume which encompassed the 28 mm and 22 mm spheres. The common volume for parallel-hole was a 20.8-cm-diameter cylinder which encompassed all five spheres in the phantom. The maneuverability of the robotic system was tested by navigating the detector to trace the flat-top table while avoiding collision with the table and maintaining the closest possible proximity to the common volume. For image reconstruction, detector trajectories were described by radius-of-rotation and detector rotation angle θ. These reconstruction parameters were obtained from the robot base and tool coordinates. The robotic SPECT system was able to maneuver the parallel-hole and pinhole collimated SPECT detectors in close proximity to the phantom, minimizing impact of the flat-top couch on detector to center-of-rotation (COR) distance. In no background case, all five spheres were visible in the reconstructed parallel-hole and pinhole images. In with background case, three spheres of 17, 22 and 28 mm diameter were readily observed with the parallel-hole imaging, and the targeted spheres (22 and 28 mm diameter) were readily observed in the pinhole ROI imaging.

In conclusion, the proposed on-board robotic SPECT can be aligned to LINAC/CBCT with a single pinhole projection of the line-source phantom. Alignment parameters can be estimated using one pinhole projection of line sources. This alignment method may be important for multi-pinhole SPECT, where relative pinhole alignment may vary during rotation. For single pinhole and multi-pinhole SPECT imaging onboard radiation therapy machines, the method could provide alignment of SPECT coordinates with those of CBCT and the LINAC. In simulation studies of prone breast imaging and respiratory-gated lung imaging, the 49-pinhole detector showed better tumor contrast recovery and localization in a 4-minute scan compared to parallel-hole detector. On-board SPECT could be achieved by a robot maneuvering a SPECT detector about patients in position for radiation therapy on a flat-top couch. The robot inherent coordinate frames could be an effective means to estimate detector pose for use in SPECT image reconstruction.

On-board imaging is useful for guiding radiation to patients in the treatment position; however, current treatment-room imaging modalities are not sensitive to physiology - features that may differentiate tumor from nearby tissue or identify biological targets, e.g., hypoxia, high tumor burden, or increased proliferation. Single photon emission computed tomography (SPECT) is sensitive to physiology. We propose on-board SPECT for biological target localization.

Localization performance was studied in computer-simulated and scanner-acquired parallel-hole SPECT images. Numerical observers were forced to localize hot targets in limited search volumes that account for uncertainties common to radiation therapy delivery. Localization performance was studied for spherical targets of various diameters, activity ratios, and anatomical locations. Also investigated were the effects of detector response function compensation (DRC) and observer normalization on target localization. Localization performance was optimized as a function of iteration number and degree of post-reconstruction smoothing. Localization error patterns were analyzed for directional dependencies and were related to the detector trajectory. Localization performance and the effect of the detector trajectory were investigated in a hardware study using a whole-body phantom.

Typically targets of 6:1 activity were localized as accurately using 4-minute scans as those of 3:1 activity using 20-minute scans. This trend is consistent with the relationship between contrast and noise in the contrast-to-noise ratio (CNR) and implies that higher contrast targets are better candidates for on-board SPECT because of time constraints in the treatment room. Using 4-minute scans, mean localization errors were within 2 mm for superficial targets of 6:1 activity that were proximal to the detector trajectory and of at least 14 mm in diameter. Localization was significantly better (p < 0.05, Wilcoxon signed-rank test) with than without observer normalization and DRC at 5 of 6 superficial tumor sites. Observer normalization improved localization substantially for a target proximal to the much hotter heart. Localization error patterns were shown to be anisotropic and dependent on target position relative to the detector trajectory. Detector views of close approach and of minimal attenuation were predictive of directions with the smallest (magnitude) localization bias and precision. The detector trajectory had a substantial effect on localization performance. In scanner-acquired SPECT images, mean localization errors of a 22-mm-diameter superficial target were 0.8, 1.5, and 6.9 mm respectively using proximal 180°, 360°, and distal 180° detector trajectories, thus demonstrating the benefits of using a proximal 180° detector trajectory.

In conclusion, the potential performance characteristics of on-board SPECT were investigated using computer-simulation and real-detector studies. Mean localization errors < 2 mm were obtained for proximal, superficial targets with diameters >14 mm and of 6:1 activity relative to background using scan times of approximately 5 minutes. The observed direction-dependent localization errors are related to the detector trajectory and have important implications for radiation therapy. This works shows that parallel-hole SPECT could be useful for localizing certain biological targets.

AbstractIntroduction: SPECT and PET are long established methods for functional imaging of bone lesions, including lesions in bone marrow and bone metastasis. These imaging modalities are however limited by poor spatial resolution which degrades quantitative accuracy and precise localization. This limitation in quantitative accuracy corresponds to the partial volume effect (PVE), in which a portion of the radiotracer activity truly in one structure appears, in the image, to be in nearby image voxels. To some extent PVEs can be corrected by iterative image reconstruction algorithms, such as ordered-subsets expectation maximization (OSEM), that model spatial resolution. This approach is however limited by noise, which is amplified as spatial resolution is recovered and PVEs are reduced. SPECT and PET imaging often involves CT as well. CT provides very high-resolution anatomical information which can be used to correct PVEs in SPECT and PET. One approach to PVE correction is using Markov Random Fields (MRFs) that incorporate anatomical information. However, there has been relatively little investigation on MRF-based PVE correction for SPECT/PET bone imaging using CT information. In this work, two types of CT anatomical information are considered: (i) partial anatomical information (pAI) which distinguishes, for example, compact bone from bone marrow but does not otherwise distinguish the tumor from surrounding tissue and (ii) full anatomical information (fAI), which fully distinguishes tumor from surrounding tissue. Image reconstructions involving pAI and fAI are referred to as RpAI and RfAI, respectively. RfAI is expected to provide the best correction of tumor PVEs, but RpAI may be more often available from CT images. The objective of the work is to assess the effectiveness of RpAI as compared to RfAI and OSEM.

Methods: Radiotracer (SPECT/PET) and attenuation coefficient (CT) phantoms were generated using XCAT software. Tumor lesions with high activity were added to the bone marrow in the radiotracer phantom. Two CT phantoms, pAI and fAI, were generated, with the fAI CT phantom including reduced CT number in the tumor-lesion locations. Projection data were simulated, and images were reconstructed using the computer code SPECT-MAP, with modeled spatial resolutions of 12mm (SPECT-like data) and 6mm (PET-like data). The RpAI and RfAI image reconstructions were performed using the iterative coordinate descent (ICD) algorithm and the Bowsher prior. The reconstructions were performed with projection data at 4 noise levels: 5M-, 50M-, and 100M-counts and noise-free. Reconstructed images were evaluated by visual inspection and by root-mean-square (RMS) error across the entire image and in 2 small ROIs (ROI-1 and ROI-2) surrounding the tumor lesions.

Results: The estimated rmsemin calculated from ROI-1 and ROI-2 reconstructed images of noisy (5M counts) projection data with res-12mm using OSEM, RpAI and RfAI were 0.92E-5 & 0.82E-5 (both at iteration 5, subset 9), 0.76E-5 & 0.65E-5 (both at OPS of 1.0E+4), and 0.44E-5 & 0.44E-5 (both at OPS of 1.0E+4), respectively; while for res-6mm, the rmsemin were 0.85E-5 & 0.81E-5 (both at iteration 10, subset 9), 0.57E-5 & 0.53E-5 (both at OPS of 1.0E+4), 0.37E-5 & 0.37E-5 (both at OPS of 1.0E+4), respectively. At both spatial resolutions, the RpAI reconstructions, using partial anatomical information only, provided reduced RMS errors compared to OSEM. Conclusions: At spatial resolutions characteristic of SPECT and PET, the partial anatomical information available from normal bone structures such as marrow and compact bone can improve estimation of hot-spot lesions, as measured by visual inspection and RMS error.

Paranasal sinus cancers account for 3% of all head and neck cancers. Of these cases, only 15% cases are ethmoid sinus cancers (Harari et al., 2014). Cancer in these regions may spread rapidly to the adjacent areas, especially as the early disease is frequently asymptomatic (Bleehen NM, 1972). Ethmoid cancers are locally advanced at presentation and are difficult to cure, with 5-year disease-free survival rates of 30–50%. Because of the anatomic constraints and patterns of local spread, the inclusion of these tumors with those arising in the head and neck region and nasal fossa may be misleading (Waldron et al., 1998). Different imaging modalities can be used to diagnose ethmoid sinus cancer, such as Positron Emission Tomography (PET), Single-Photon Emission Computed Tomography (SPECT), Computed Tomography (CT), and Magnetic Resonance Imaging (MRI). PET and SPECT can provide important functional and molecular information, but they have considerable partial volume effects (PVEs), in which radiotracer activity in small structures appears blurred into nearby tissue, thereby degrading quantitative accuracy. These PVEs can be reduced if full anatomical information (fAI) is available about the spatial extent of the structure, e.g. of a tumor. A less investigated – but perhaps clinically more frequent – scenario is if only partial anatomical information (pAI) is available. Consider for example a tumor in the ethmoid bone. Even if the tumor is not visible on CT, the ethmoid bone is visible, and the visible spatial extent of the ethmoid bone places some limit on the spatial extent of the tumor. In this study, the impacts of CT partial anatomical information in the ethmoid and vomer bones are investigated and are compared to full anatomical information and to no anatomical information. The studies are performed for PET-like imaging (6-mm spatial resolution) and SPECT-like imaging (12-mm spatial resolution). Results: Compared with no anatomical information (as in OSEM reconstructed images), PET/SPECT images reconstructed with anatomical information (pAI) have reduced PVEs, as gauged by visual inspection and RMS errors, albeit not to the same extent as images reconstructed with full anatomical information (fAI). Keywords: Ethmoid sinus cancers, CT anatomy information, PET, SPECT, partial volume effect, image reconstruction