List-mode likelihood: EM algorithm and image quality estimation demonstrated on 2-D PET

Exploration of Coincidence Detection of Cascade Photons to Enhance Preclinical Multi-Radionuclide SPECT Imaging

We proposed a technique of coincidence detection of cascade photons (CDCP) to enhance preclinical SPECT imaging of therapeutic radionuclides emitting cascade photons, such as Lu-177, Ac-225, Ra-223, and In-111. We have carried out experimental studies to evaluate the proposed CDCP-SPECT imaging of low-activity radionuclides using a prototype coincidence detection system constructed with large-volume cadmium zinc telluride (CZT) imaging spectrometers and a pinhole collimator. With In-111 in experimental studies, the CDCP technique allows us to improve the signal-to-contamination in the projection (Projection-SCR) by ~53 times and reduce ~98% of the normalized contamination. Compared to traditional scatter correction, which achieves a Projection-SCR of 1.00, our CDCP method boosts it to 15.91, showing enhanced efficacy in reducing down-scattered contamination, especially at lower activities. The reconstructed images of a line source demonstrated the dramatic enhancement of the image quality with CDCP-SPECT compared to conventional and triple-energy-window-corrected SPECT data acquisition. We also introduced artificial energy blurring and Monte Carlo simulation to quantify the impact of detector performance, especially its energy resolution and timing resolution, on the enhancement through the CDCP technique. We have further demonstrated the benefits of the CDCP technique with simulation studies, which shows the potential of improving the signal-to-contamination ratio by 300 times with Ac-225, which emits cascade photons with a decay constant of ~0.1 ns. These results have demonstrated the potential of CDCP-enhanced SPECT for imaging a super-low level of therapeutic radionuclides in small animals.

Effect of depth of interaction resolution on the spatial resolution of SIAT aPET

Objective.Spatial resolution is a crucial parameter for a positron emission tomography (PET) scanner. The spatial resolution of a high-resolution small animal PET scanner is significantly influenced by the effect of depth of interaction (DOI) uncertainty. The aim of this work is to investigate the impact of DOI resolution on the spatial resolution of a small animal PET scanner called SIAT aPET and determine the required DOI resolution to achieve nearly uniform spatial resolution within the field of view (FOV).Approach. The SIAT aPET detectors utilize 1.0 × 1.0 × 20 mm3crystals, with an average DOI resolution of ∼2 mm. A default number of 16 DOI bins are used during data acquisition. First, a Na-22 point source was scanned in the center of the axial FOV with different radial offsets. Then, a Derenzo phantom was scanned at radial offsets of 0 and 15 mm in the center axial FOV. The measured DOI information was rebinned to 1, 2, 4 and 8 DOI bins to mimic different DOI resolutions of the detectors during image reconstruction.Main results. Significant artifacts were observed in images obtained from both the point source and Derenzo phantom when using only one DOI bin. When accurate measurement of DOI is not achieved, degradation in spatial resolution is more pronounced in the radial direction compared to tangential and axial directions for large radial offsets. The radial spatial resolutions at a 30 mm radial offset are 5.05, 2.62, 1.24, 0.86 and 0.78 mm when using 1, 2, 4, 8, or 16 DOI bins, respectively. The axial spatial resolution improved from ∼1.3 to 0.7 mm as the number of DOI bins increased from 1 to 16 at radial offsets from 0 to 25 mm. Two DOI bins are required to obtain images without significant artifacts. The required DOI resolution is about three times the crystal width of SIAT aPET to achieve a uniform submillimeter spatial resolution within the central 60 mm FOV and resolve the 1 mm rods of the Derenzo phantom at both positions.

A list-mode multi-energy window low-count SPECT reconstruction method for isotopes with multiple emission peaks

BACKGROUND

Single-photon emission computed tomography (SPECT) provides a mechanism to perform absorbed-dose quantification tasks for [Formula: see text]-particle radiopharmaceutical therapies ([Formula: see text]-RPTs). However, quantitative SPECT for [Formula: see text]-RPT is challenging due to the low number of detected counts, the complex emission spectrum, and other image-degrading artifacts. Towards addressing these challenges, we propose a low-count quantitative SPECT reconstruction method for isotopes with multiple emission peaks.

METHODS

Given the low-count setting, it is important that the reconstruction method extracts the maximal possible information from each detected photon. Processing data over multiple energy windows and in list-mode (LM) format provide mechanisms to achieve that objective. Towards this goal, we propose a list-mode multi energy window (LM-MEW) ordered-subsets expectation-maximization-based SPECT reconstruction method that uses data from multiple energy windows in LM format and include the energy attribute of each detected photon. For computational efficiency, we developed a multi-GPU-based implementation of this method. The method was evaluated using 2-D SPECT simulation studies in a single-scatter setting conducted in the context of imaging [[Formula: see text]Ra]RaCl[Formula: see text], an FDA-approved RPT for metastatic prostate cancer.

RESULTS

The proposed method yielded improved performance on the task of estimating activity uptake within known regions of interest in comparison to approaches that use a single energy window or use binned data. The improved performance was observed in terms of both accuracy and precision and for different sizes of the region of interest.

CONCLUSIONS

Results of our studies show that the use of multiple energy windows and processing data in LM format with the proposed LM-MEW method led to improved quantification performance in low-count SPECT of isotopes with multiple emission peaks. These results motivate further development and validation of the LM-MEW method for such imaging applications, including for [Formula: see text]-RPT SPECT.

Experimental Evaluation of a 3-D CZT Imaging Spectrometer for Potential Use in Compton-Enhanced PET Imaging

We constructed a prototype positron emission tomography (PET) system and experimentally evaluated large-volume 3-D cadmium zinc telluride (CZT) detectors for potential use in Compton-enhanced PET imaging. The CZT spectrometer offers sub-0.5-mm spatial resolution, an ultrahigh energy resolution (~1% @ 511 keV), and the capability of detecting multiple gamma-ray interactions that simultaneously occurred. The system consists of four CZT detector panels with a detection area of around 4.4 cm × 4.4 cm. The distance between the front surfaces of the two opposite CZT detector panels is ~80 mm. This system allows us to detect coincident annihilation photons and Compton interactions inside the detectors and then, exploit Compton kinematics to predict the first Compton interaction site and reject chance coincidences. We have developed a numerical integration technique to model the near-field Compton response that incorporates Doppler broadening, detector's finite resolutions, and the distance between the first and second interactions. This method was used to effectively reject random and scattered coincidence events. In the preliminary imaging studies, we have used point sources, line sources, a custom-designed resolution phantom, and a commercial image quality (IQ) phantom to demonstrate an imaging resolution of approximately 0.75 mm in PET images, and Compton-based enhancement.

Total-body PET: a new paradigm for molecular imaging

Total body (TB) positron emission tomography (PET) instruments have dramatically changed the paradigm of PET clinical and research studies due to their very high sensitivity and capability to image dynamic radiopharmaceutical distributions in the major organs of the body simultaneously. In this manuscript, we review the design of these systems and discuss general challenges and trade-offs to maximize the performance gains of current TB-PET systems. We then describe new concepts and technology that may impact future TB-PET systems. The manuscript summarizes what has been learned from the initial sites with TB-PET and explores potential research and clinical applications of TB-PET. The current generation of TB-PET systems range in axial field-of-view (AFOV) from 1 to 2 m and serve to illustrate the benefits and opportunities of a longer AFOV for various applications in PET. In only a few years of use these new TB-PET systems have shown that they will play an important role in expanding the field of molecular imaging and benefiting clinical practice.

Compton and proximity imaging of 225Ac in vivo with a CZT gamma camera: a proof of principle with simulations

In vivo imaging of 225Ac is a major challenge in the development of targeted alpha therapy radiopharmaceuticals due to the extremely low injected doses. In this paper, we present the design of a multi-modality gamma camera that integrates both proximity and Compton imaging in order to achieve the demanding sensitivities required to image 225Ac with good image quality. We consider a dual-head camera, each of the heads consisting of two planar cadmium zinc telluride detectors acting as scatterer and absorber for Compton imaging, and with the scatterer practically in contact with the subject to allow for proximity imaging. We optimize the detector's design and characterize the detector's performance using Monte Carlo simulations. We show that Compton imaging can resolve features of up to 1.5 mm for hot rod phantoms with an activity of 1 μCi, and can reconstruct 3D images of a mouse injected with 0.5 μCi after a 15 minutes exposure and with a single bed position, for both 221Fr and 213Bi. Proximity imaging is able to resolve two 1 mm-radius sources of less than 0.1 μCi separated by 1 cm and at 1 mm from the detector, as well as it can provide planar images of 221Fr and 213Bi biodistributions of the mouse phantom in 5 minutes.

Effect on null spaces of list-mode imaging systems due to increasing the number of attributes

There are two types of uncertainty in image reconstructions from list-mode data: statistical and deterministic. One source of statistical uncertainty is the finite number of attributes of the detected particles, which are sampled from a probability distribution on the attribute space. A deterministic source of uncertainty is the effect that null functions of the imaging operator have on reconstructed pixel or voxel values. Quantifying the reduction in this deterministic source of uncertainty when more attributes are measured for each detected particle is the subject of this work. Specifically, upper bounds on an error metric are derived to quantify the error introduced in the reconstruction by the presence of null functions, and these upper bounds are shown to be reduced when the number of attributes is increased. These bounds are illustrated with an example of a two-dimensional single photon emission computed tomography (SPECT) system where the depth of interaction in the scintillation crystal is added to the attribute vector.

A back‐projection‐and‐filtering‐like (BPF‐like) reconstruction method with the deep learning filtration from listmode data in TOF‐PET

PURPOSE

The time-of-flight (TOF) information improves signal-to-noise ratio (SNR) for positron emission tomography (PET) imaging. Existing analytical algorithms for TOF PET usually follow a filtered back-projection process on reconstructing images from the sinogram data. This work aims to develop a back-projection-and-filtering-like (BPF-like) algorithm that reconstructs the TOF PET image directly from listmode data rapidly.

METHODS

We extended the 2D conventional non-TOF PET projection model to a TOF case, where projection data are represented as line integrals weighted by the one-dimensional TOF kernel along the projection direction. After deriving the central slice theorem and the TOF back-projection of listmode data, we designed a deep learning network with a modified U-net architecture to perform the spatial filtration (reconstruction filter). The proposed BP-Net method was validated via Monte Carlo simulations of TOF PET listmode data with three different time resolutions for two types of activity phantoms. The network was only trained on the simulated full-dose XCAT dataset and then evaluated on XCAT and Jaszczak data with different time resolutions and dose levels.

RESULTS

Reconstructed images show that when compared with the conventional BPF algorithm and the MLEM algorithm proposed for TOF PET, the proposed BP-Net method obtains better image quality in terms of peak signal-to-noise ratio, relative mean square error, and structure similarity index; besides, the reconstruction speed of the BP-Net is 1.75 times faster than BPF and 29.05 times faster than MLEM using 15 iterations. The results also indicate that the performance of the BP-Net degrades with worse time resolutions and lower tracer doses, but degrades less than BPF or MLEM reconstructions.

CONCLUSION

In this work, we developed an analytical-like reconstruction in the form of BPF with the reconstruction filtering operation performed via a deep network. The method runs even faster than the conventional BPF algorithm and provides accurate reconstructions from listmode data in TOF-PET, free of rebinning data to a sinogram.

Design and modeling of a high resolution and high sensitivity PET brain scanner with double-ended readout

In the wake of recent advancements in scintillator, photodetector, and low-noise fast electronics technologies, as well as in fast reconstruction software, positron emission tomography (PET) scanners have seen considerable improvements in spatial resolution, time resolution, and absolute sensitivity. To continue this trend, we present a helmet type PET brain scanner design that combines high solid angle coverage and double-ended readout of 30 mm-thick scintillator crystals to achieve excellent absolute sensitivity, depth of interaction resolution, and time resolution. This scanner comprises 598 detector arrays, each with 8×8 Lu_1.8Y_0.2SiO₅:Ce (LYSO:Ce) crystals with dimensions 3.005×3.005×30 mm³one-to-one coupled on either end to silicon photomultipliers (SiPMs). Our Monte Carlo simulations based in the platform Geant4 predict that this scanner would attain an absolute sensitivity to a 35 cm line source placed at the center of the radial field of view of (17.1±0.1) %, a depth of interaction resolution of (3.99±0.05) mm, and a coincidence time resolution of (198±5) ps. Our simulations also predict radial, tangential, and axial spatial resolutions at the center of the field of view of 3.3 mm, 3.1 mm, and 3.3 mm, respectively. As this set of simultaneous parameters compares favorably to today's most advanced clinical PET scanners and other proposed designs, this scanner has a good chance of becoming a preferred tool for high quality brain imaging.

Free-moving Quantitative Gamma-ray Imaging

The ability to map and estimate the activity of radiological source distributions in unknown three-dimensional environments has applications in the prevention and response to radiological accidents or threats as well as the enforcement and verification of international nuclear non-proliferation agreements. Such a capability requires well-characterized detector response functions, accurate time-dependent detector position and orientation data, a digitized representation of the surrounding 3D environment, and appropriate image reconstruction and uncertainty quantification methods. We have previously demonstrated 3D mapping of gamma-ray emitters with free-moving detector systems on a relative intensity scale using a technique called Scene Data Fusion (SDF). Here we characterize the detector response of a multi-element gamma-ray imaging system using experimentally benchmarked Monte Carlo simulations and perform 3D mapping on an absolute intensity scale. We present experimental reconstruction results from hand-carried and airborne measurements with point-like and distributed sources in known configurations, demonstrating quantitative SDF in complex 3D environments.

Time of Flight in Perspective: Instrumental and Computational Aspects of Time Resolution in Positron Emission Tomography

The first time-of-flight positron emission tomography (TOF-PET) scanners were developed as early as in the 1980s. However, the poor light output and low detection efficiency of TOF-capable detectors available at the time limited any gain in image quality achieved with these TOF-PET scanners over the traditional non-TOF PET scanners. The discovery of LSO and other Lu-based scintillators revived interest in TOF-PET and led to the development of a second generation of scanners with high sensitivity and spatial resolution in the mid-2000s. The introduction of the silicon photomultiplier (SiPM) has recently yielded a third generation of TOF-PET systems with unprecedented imaging performance. Parallel to these instrumentation developments, much progress has been made in the development of image reconstruction algorithms that better utilize the additional information provided by TOF. Overall, the benefits range from a reduction in image variance (SNR increase), through allowing joint estimation of activity and attenuation, to better reconstructing data from limited angle systems. In this work, we review these developments, focusing on three broad areas: 1) timing theory and factors affecting the time resolution of a TOF-PET system; 2) utilization of TOF information for improved image reconstruction; and 3) quantification of the benefits of TOF compared to non-TOF PET. Finally, we offer a brief outlook on the TOF-PET developments anticipated in the short and longer term. Throughout this work, we aim to maintain a clinically driven perspective, treating TOF as one of multiple (and sometimes competitive) factors that can aid in the optimization of PET imaging performance.

Hybrid model of photon propagation based on the analytical and Monte Carlo methods for a dual-head PET system

The construction of photon propagation has a close relationship with the quality of reconstructed images. The classical Monte Carlo (MC) based method can model the photon propagation precisely, but it is time-consuming. The analytical method can often quickly construct a model, but its precision is a problem. How to fully exploit the advantages of the MC simulation and analytical model is an open problem. Inspired by the characteristics of the depth of interaction (DOI) detectors, which can help confirm the deposited position of a photon with DOI-encoding technology, we virtually discretize each crystal into several subcrystals to obtain the statistical distribution by MC-based simulation. Then, the statistical distribution is combined with a spatially variant solid-angle model. This combination strategy provides a hybrid model to describe photon propagation with relatively high accuracy and low computational cost. Three discretization schemes are compared to optimize the constructed photon propagation model. Several experiments are carried out to evaluate the performance of the proposed hybrid method. The metrics of full width at half maximum (FWHM), contrast recovery (CR), and coefficient of variation (COV) are adopted to quantitate the imaging results. The classical MC-based method is compared as a gold-standard reference. When a crystal is divided into two discretized positions, the convergent tendencies of CRs and COVs are consistent with that based on MC simulation method, respectively. In terms of FWHMs, the resolutions of using the MC-based model and the proposed hybrid model are 0.71 mm and 0.68 mm in the direction parallel to the detector head, respectively. This indicates the potential of the proposed method in positron emission tomography imaging.

Deep learning-based image reconstruction for TOF PET with DIRECT data partitioning format

Conventional positron emission tomography (PET) image reconstruction is achieved by the statistical iterative method. Deep learning provides another opportunity for speeding up the image reconstruction process. However, conventional deep learning-based image reconstruction requires a fully connected network for learning the Radon transform. The use of fully connected networks greatly complicated the network and increased hardware cost. In this study, we proposed a novel deep learning-based image reconstruction method by utilizing the DIRECT data partitioning method. The U-net structure with only convolutional layers was used in our approach. Patch-based model training and testing were used to achieve 3D reconstructions within current hardware limitations. Time-of-flight (TOF)-histoimages were first generated from the listmode data to replace conventional sinograms. Different projection angles were used as different channels in the input. A total of 15 patient data were used in this study. For each patient, the dynamic whole-body scanning protocol was used to expand the training dataset and a total of 372 separate scans were included. The leave-one-patient-out validation method was used. Two separate studies were carried out. In the first study, the measured TOF-histoimages were directly used for model training and testing, to study the performance of the method in real-world applications. In the second study, TOF-histoimages were simulated from already reconstructed images to exclude the scatters, randoms, attenuation-activity mismatch effects. This study was used to evaluate the optimal performance when all other corrections are ideal. Volumes of interests were placed in the liver and lesion region to study image noise and lesion quantitations. The reconstructed images using the proposed deep learning method showed similar image quality when compared with the conventional expectation-maximization approach. A minimal difference was observed when the simulated TOF-histoimages were used as model input and testing, suggesting the deep learning model can indeed learn the reconstruction process. Some quantitative difference was observed when the measured TOF-histoimages were used. The two studies suggested that the major difference is caused by inaccurate corrections performed by the network itself, which indicated that physics-based corrections are still required for better quantitative performance. In conclusion, we have proposed a novel deep learning-based image reconstruction method for TOF PET. With the help of the DIRECT data partitioning method, no fully connected layers were used and 3D image reconstruction can be directly achieved within the limits of the current hardware.

Path Planning for Localization of Radiation Sources Based on Principal Component Analysis

In this paper, we propose a path planning method for the localization of radiation sources using a mobile robot equipped with an imaging gamma-ray detector, which has a field of view in all directions. The ability to detect and localize radiation sources is essential for ensuring nuclear safety, security, and surveillance. To enable the autonomous localization of radiation sources, the robot must have the ability to automatically determine the next location for gamma ray measurement instead of following a predefined path. The number of incident events is approximated to be the squared inverse proportional to the distance between the radiation source and the detector. Therefore, the closer the distance to the source, the shorter the time required to obtain the same radiation counts measured by the detector. Hence, the proposed method is designed to reduce this distance to a position where a sufficient number of gamma-ray events can be obtained; then, a path to surround the radiation sources is generated. The proposed method generates this path by performing principal component analysis based on the results obtained from previous measurements. Both simulations and actual experiments demonstrate that the proposed method can automatically generate a measurement path and accurately localize radiation sources.

Effect on null spaces of list-mode imaging systems due to increasing the size of attribute space

An upper bound is derived for a figure of merit that quantifies the error in reconstructed pixel or voxel values induced by the presence of null functions for any list-mode system. It is shown that this upper bound decreases as the region in attribute space occupied by the allowable attribute vectors expands. This upper bound allows quantification of the reduction in this error when this type of expansion is implemented. Of course, reconstruction error is also caused by system noise in the data, which has to be treated statistically, but we will not be addressing that problem here. This method is not restricted to pixelized or voxelized reconstructions and can in fact be applied to any region of interest. The upper bound for pixelized reconstructions is demonstrated on a list-mode 2D Radon transform example. The expansion in the attribute space is implemented by doubling the number of views. The results show how the pixel size and number of views both affect the upper bound on reconstruction error from null functions. This reconstruction error can be averaged over all pixels to give a single number or can be plotted as a function on the pixel grid. Both approaches are demonstrated for the example system. In conclusion, this method can be applied to any list-mode system for which the system operator is known and could be used in the design of the systems and reconstruction algorithms.

3D/4D Reconstruction and Quantitative Total Body Imaging

Total-body PET image reconstruction follows a similar procedure to the image reconstruction process for standard whole-body PET scanners. One unique aspect of total-body imaging is simultaneous coverage of the entire human body, which makes it convenient to perform total-body dynamic PET scans. Therefore, four-dimensional dynamic PET reconstruction and parametric imaging are of great interest in total-body imaging. This article covers some basics of PET image reconstruction and then focuses on three- and four-dimensional PET reconstruction for total-body imaging. Methods for image formation from raw measurements in total-body PET are described. Challenges and opportunities in total-body PET image reconstruction are discussed.

Compton-camera-based SPECT for thyroid cancer imaging

Thyroid cancer is the most common type of endocrine-related cancer and the most common cancer in young women. Currently, single photon emission computed tomography (SPECT) and computed tomography (CT) are used with radioiodine scintigraphy to evaluate patients with thyroid cancer. The gamma camera for SPECT contains a mechanical collimator that greatly compromises dose efficiency and limits diagnostic sensitivity. Fortunately, the Compton camera is emerging as an ideal approach for mapping the distribution of radiopharmaceuticals inside the thyroid. In this preliminary study, based on the state-of-the-art readout chip Timepix3, we investigate the feasibility of using Compton camera for radiotracer SPECT imaging in thyroid cancer. A thyroid phantom is designed to mimic human neck, the mechanism of Compton camera-based event detection is simulated to generate realistic list-mode data, and a weighted back-projection method is developed to reconstruct the original distribution of the emission source. Study results show that the Compton camera can improve the detection efficiency for two or higher orders of magnitude comparing with the conventional gamma cameras. The thyroid gland regions can be reconstructed from the Compton camera measurements in terms of radiotracer distribution. This makes the Compton-camera-based SPECT imaging a promising modality for future clinical applications with significant benefits for dose reduction, scattering artifact reduction, temporal resolution enhancement, scan throughput increment, and others.

3D Compton image reconstruction method for whole gamma imaging

Compton imaging or Compton camera imaging has been studied well, but its advantages in nuclear medicine and molecular imaging have not been demonstrated yet. Therefore, the aim of this work was to compare Compton imaging with positron emission tomography (PET) by using the same imaging platform of whole gamma imaging (WGI). WGI is a concept that combines PET with Compton imaging by inserting a scatterer ring into a PET ring. This concept utilizes diverse types of gamma rays for 3D tomographic imaging. In this paper, we remodeled our previous WGI prototype for small animal imaging, and we developed an image reconstruction method based on a list-mode ordered subset expectation maximization algorithm incorporating detector response function modeling, random correction and normalization (sensitivity correction) for either PET and Compton imaging. To the best of our knowledge, this is the world's first realization of a full-ring Compton imaging system. We selected ⁸⁹Zr as an imaging target because a ⁸⁹Zr nuclide emits a 909 keV single-gamma ray as well as a positron, and we can directly compare Compton imaging of 909 keV photons with PET, a well-established modality. We measured a cylindrical phantom and a small rod phantom filled with ⁸⁹Zr solutions of 10.3 MBq and 10.2 MBq activity, respectively, for 1 h each. The uniform radioactivity distribution of the cylindrical phantom was reconstructed with normalization in both PET and Compton imaging. Coefficients of variation for region-of-interest values were 4.2% for Compton imaging and 3.3% for PET; the difference might be explained by the difference in the detected count number. The small rod phantom experiment showed that the WGI Compton imaging had spatial resolution better than 3.0 mm at the peripheral region although the center region had lower resolution. PET resolved 2.2 mm rods clearly at any location. We measured a mouse for 1 h, 1 d after injection of 9.8 MBq ⁸⁹Zr oxalate. The ⁸⁹Zr assimilated in the mouse bony structures was clearly depicted, and Compton imaging results agreed well with PET images, especially for the region inside the scatterer ring. In conclusion, we demonstrated the performance of WGI using the developed Compton image reconstruction method. We realized Compton imaging with a quality approaching that of PET, which is supporting a future expectation that Compton imaging outperforms PET.

Penalized maximum-likelihood reconstruction for improving limited-angle artifacts in a dedicated head and neck PET system

Positron emission tomography (PET) suffers from limited spatial resolution in current head and neck cancer management. We are building a dual-panel high-resolution PET system to aid the detection of tumor involvement in small lymph nodes ([Formula: see text]10 mm in diameter). The system is based on cadmium zinc telluride (CZT) detectors with cross-strip electrode readout (1 mm anode pitch and 5 mm cathode pitch). One challenge of the dual-panel system is that the limited angular coverage of the imaging volume leads to artifacts in reconstructed images, such as the elongation of lesions. In this work, we leverage a penalized maximum-likelihood (PML) reconstruction for the limited-angle PET system. The dissimilarity between the image to be reconstructed and a prior image from a low-resolution whole-body scanner is penalized. An image-based resolution model is incorporated into the regularization. Computer simulations were used to evaluate the performance of the method. Results demonstrate that the elongation of the 6-mm and 8-mm diameter hot spheres is eliminated with the regularization strength γ being 0.02 or larger. The PML reconstruction yields higher contrast recovery coefficient (CRC) of hot spheres compared to the maximum-likelihood reconstruction, as well as the low-resolution whole-body image, across all hot sphere sizes tested (3, 4, 6, and 8 mm). The method studied in this work provides a way to mitigate the limited-angle artifacts in the reconstruction from limited-angle PET data, making the high-resolution dual-panel dedicated head and neck PET system promising for head and neck cancer management.

Fisher information analysis of list-mode SPECT emission data for joint estimation of activity and attenuation distribution

The potential to perform attenuation and scatter compensation (ASC) in single-photon emission computed tomography (SPECT) imaging without a separate transmission scan is highly significant. In this context, attenuation in SPECT is primarily due to Compton scattering, where the probability of Compton scatter is proportional to the attenuation coefficient of the tissue and the energy of the scattered photon and the scattering angle are related. Based on this premise, we investigated whether the SPECT scattered-photon data acquired in list-mode (LM) format and including the energy information can be used to estimate the attenuation map. For this purpose, we propose a Fisher-information-based method that yields the Cramer-Rao bound (CRB) for the task of jointly estimating the activity and attenuation distribution using only the SPECT emission data. In the process, a path-based formalism to process the LM SPECT emission data, including the scattered-photon data, is proposed. The Fisher information method was implemented on NVIDIA graphics processing units (GPU) for acceleration. The method was applied to analyze the information content of SPECT LM emission data, which contains up to first-order scattered events, in a simulated SPECT system with parameters modeling a clinical system using realistic computational studies with 2-D digital synthetic and anthropomorphic phantoms. The method was also applied to LM data containing up to second-order scatter for a synthetic phantom. Experiments with anthropomorphic phantoms simulated myocardial perfusion and dopamine transporter (DaT)-Scan SPECT studies. The results show that the CRB obtained for the attenuation and activity coefficients was typically much lower than the true value of these coefficients. An increase in the number of detected photons yielded lower CRB for both the attenuation and activity coefficients. Further, we observed that systems with better energy resolution yielded a lower CRB for the attenuation coefficient. Overall, the results provide evidence that LM SPECT emission data, including the scattered photons, contains information to jointly estimate the activity and attenuation coefficients.

Image reconstruction for a multi-layer Compton telescope: an analytical model for three interaction events

Compton Cameras are electronically collimated photon imagers suitable for sub-MeV to few MeV gamma-ray detection. Such features are desirable to enable in vivo range verification in hadron therapy, through the detection of secondary Prompt Gammas. A major concern with this technique is the poor image quality obtained when the incoming gamma-ray energy is unknown. Compton Cameras with more than two detector planes (multi-layer Compton Cameras) have been proposed as a solution, given that these devices incorporate more signal sequences of interactions to the conventional two interaction events. In particular, three interaction events convey more spectral information as they allow inferring directly the incident gamma-ray energy. A three-layer Compton Telescope based on continuous Lanthanum (III) Bromide crystals coupled to Silicon Photomultipliers is being developed at the IRIS group of IFIC-Valencia. In a previous work we proposed a spectral reconstruction algorithm for two interaction events based on an analytical model for the formation of the signal. To fully exploit the capabilities of our prototype, we present here an extension of the model for three interaction events. Analytical expressions of the sensitivity and the System Matrix are derived and validated against Monte Carlo simulations. Implemented in a List Mode Maximum Likelihood Expectation Maximization algorithm, the proposed model allows us to obtain four-dimensional (energy and position) images by using exclusively three interaction events. We are able to recover the correct spectrum and spatial distribution of gamma-ray sources when ideal data are employed. However, the uncertainties associated to experimental measurements result in a degradation when real data from complex structures are employed. Incorrect estimation of the incident gamma-ray interaction positions, and missing deposited energy associated with escaping secondaries, have been identified as the causes of such degradation by means of a detailed Monte Carlo study. As expected, our current experimental resolution and efficiency to three interaction events prevents us from correctly recovering complex structures of radioactive sources. However, given the better spectral information conveyed by three interaction events, we expect an improvement of the image quality of conventional Compton imaging when including such events. In this regard, future development includes the incorporation of the model assessed in this work to the two interaction events model in order to allow using simultaneously two and three interaction events in the image reconstruction.

Ultra-Fast List-Mode Reconstruction of Short PET Frames and Example Applications

Standard clinical reconstructions usually require several minutes to complete, and this time is mostly independent of the duration of the data being reconstructed. Applications such as data-driven motion estimation, which require many short frames over the duration of the scan, become unfeasible with such long reconstruction times. In this work, we present an infrastructure whereby ultra-fast list-mode reconstructions of very short frames (≤1 s) are performed. With this infrastructure, it is possible to have a dynamic series of frames that can be used for various applications, such as data-driven motion estimation, whole-body surveys, quick reconstructions of gated data to select the optimal gate for a given attenuation map, and, if the infrastructure runs simultaneously with the scan, real-time display of the reconstructed data during the scan and automated alerts for patient motion. Methods: A fast ray-tracing time-of-flight projector was implemented and parallelized. The reconstruction parameters were optimized to allow for fast performance: only a few iterations are performed, without point-spread-function modeling, and scatter correction is not used. The resulting reconstructions are thus not quantitative but are acceptable for motion estimation and visualization purposes. Data-driven motion can be estimated using image registration, with the resultant motion data being used in a fully motion-corrected list-mode reconstruction. Results: The infrastructure provided images that can be used for visualization and gating purposes and for motion estimation using image registration. Several case studies are presented, including data-driven motion estimation and correction for brain studies, abdominal studies in which respiratory and cardiac motion is visible, and a whole-body survey. Conclusion: The presented infrastructure provides the capability to quickly create a series of very short frames for PET data that can be used in a variety of applications.

Whole gamma imaging: a new concept of PET combined with Compton imaging

We proposed a concept of whole gamma imaging (WGI) that utilizes all detectable gamma rays for imaging. An additional detector ring, which is used as the scatterer, is inserted in the field-of-view of a PET ring so that single gamma rays can be detected by the Compton imaging method. In particular, for the non-pure positron emitters which emit an additional gamma ray almost at the same time, triple gamma imaging will be enabled; localization on each line-of-response (LOR) is possible by using the Compton cone of the additional gamma ray. We developed a prototype to show a proof of the WGI concept. The diameters of scatterer ring and PET ring were set as 20 cm and 66 cm, respectively. For Compton imaging of the 662-keV gamma ray from a ¹³⁷Cs point source, spatial resolution obtained by the list-mode OSEM algorithm was 4.4 mm FWHM at the 8 cm off-center position and 13.1 mm FWHM at the center position. For PET imaging of a ²²Na point source, spatial resolution was about 2 mm FWHM at all positions. For the triple gamma imaging, 5.7 mm FWHM (center) and 4.8 mm FWHM (8 cm off-center) were obtained for the ²²Na point source just by plotting the intersecting points between each LOR and each Compton cone of the 1275-keV gamma ray. No image reconstruction was applied. Scandium-44 was produced as a practical candidate of the non-pure positron emitters, and 6.6 mm FWHM (center) and 5.8 mm FWHM (8 cm off-center) were obtained in the same manner. This direct imaging approach which neither requires time-consuming event integration nor iterative image reconstruction may allow in vivo real-time tracking of a tiny amount of activity. Our initial results showed the feasibility of the WGI concept, which is a novel combination of PET and Compton imaging.

A LIST-MODE OSEM-BASED ATTENUATION AND SCATTER COMPENSATION METHOD FOR SPECT

Reliable attenuation and scatter compensation (ASC) is a pre-requisite for quantification and beneficial for visual interpretation tasks in SPECT. In this paper, we develop a reconstruction method that uses the entire SPECT emission data, i.e. data in both the photopeak and scatter windows, acquired in list-mode format and including the energy attribute of the detected photon, to perform ASC. We implemented a GPU-based version of this method using an ordered subsets expectation maximization (OSEM) algorithm. The method was objectively evaluated using realistic simulation studies on the task of estimating uptake in the striatal regions of the brain in a 2-D dopamine transporter (DaT)-scan SPECT study. We observed that inclusion of data from the scatter window and using list-mode data yielded improved quantification compared to using data only from the photopeak window or using binned data. These results motivate further development of list-mode-based ASC methods that include scatter-window data for SPECT.

Quantifying the loss of information from binning list-mode data

List-mode data are increasingly being used in single photon emission computed tomography (SPECT) and positron emission tomography (PET) imaging, among other imaging modalities. However, there are still many imaging designs that effectively bin list-mode data before image reconstruction or other estimation tasks are performed. Intuitively, the binning operation should result in a loss of information. In this work, we show that this is true for Fisher information and provide a computational method for quantifying the information loss. In the end, we find that the information loss depends on three factors. The first factor is related to the smoothness of the mean data function for the list-mode data. The second factor is the actual object being imaged. Finally, the third factor is the binning scheme in relation to the other two factors.

Comments on the NEMA NU 4-2008 Standard on Performance Measurement of Small Animal Positron Emission Tomographs

The National Electrical Manufacturers Association’s (NEMA) NU 4-2008 standard specifies methodology for evaluating the performance of small-animal PET scanners. The standard’s goal is to enable comparison of different PET scanners over a wide range of technologies and geometries used. In this work, we discuss if the NEMA standard meets these goals and we point out potential flaws and improvements to the standard.For the evaluation of spatial resolution, the NEMA standard mandates the use of filtered backprojection reconstruction. This reconstruction method can introduce star-like artifacts for detectors with an anisotropic spatial resolution, usually caused by parallax error. These artifacts can then cause a strong dependence of the resulting spatial resolution on the size of the projection window in image space, whose size is not fully specified in the NEMA standard. If the PET ring has detectors which are perpendicular to a Cartesian axis, then the resolution along this axis will typically improve with larger projection windows.We show that the standard’s equations for the estimation of the random rate for PET systems with intrinsic radioactivity are circular and not satisfiable. However, a modified version can still be used to determine an approximation of the random rates under the assumption of negligible random rates for small activities and a constant scatter fraction. We compare the resulting estimated random rates to random rates obtained using a delayed coincidence window and two methods based on the singles rates. While these methods give similar estimates, the estimation method based on the NEMA equations overestimates the random rates.In the NEMA standard’s protocol for the evaluation of the sensitivity, the standard specifies to axially step a point source through the scanner and to take a different scan for each source position. Later, in the data analysis section, the standard does not specify clearly how the different scans have to be incorporated into the analysis, which can lead to unclear interpretations of publicized results.The standard’s definition of the recovery coefficients in the image quality phantom includes the maximum activity in a region of interest, which causes a positive correlation of noise and recovery coefficients. This leads to an unintended trade-off between desired uniformity, which is negatively correlated with variance (i.e., noise), and recovery.With this work, we want to start a discussion on possible improvements in a next version of the NEMA NU-4 standard.

Design study of a brain-dedicated time-of-flight PET system with a hemispherical detector arrangement

Time-of-flight (TOF) is now a standard technology for positron emission tomography (PET), but its effective use for small diameter PET systems has not been studied well. In this paper, we simulated a brain-dedicated TOF-PET system with a hemispherical detector arrangement. We modeled a Hamamatsu TOF-PET module (C13500-4075LC-12) with 280 ps coincidence resolving time (CRT), in which a 12  ×  12 array of multi pixel photon counters (MPPCs) is connected to a lutetium fine silicate (LFS) crystal array of 4.1  ×  4.1 mm² cross section each, based on one-to-one coupling. On the other hand, spatial resolution degradation due to the parallax error should be carefully addressed for the small diameter PET systems. The ideal PET detector would have both depth-of-interaction (DOI) and TOF capabilities, but typical DOI detectors that are based on light sharing tend to degrade TOF performance. Therefore, in this work, we investigated non-DOI detectors with an appropriate crystal length, which was a compromise between suppressed parallax error and decreased sensitivity. Using GEANT4, we compared two TOF detectors, a 20 mm long non-DOI and a 10 mm long non-DOI, with a non-TOF, 4-layer DOI detector with a total length of 20 mm (i.e. 5  ×  4 mm). We simulated a contrast phantom and evaluated the relationship between the contrast recovery coefficient (CRC) and the noise level (the coefficient of variation, COV) for reconstructed images. The 10 mm long non-DOI, which reduces the parallax error at the cost of sensitivity loss, showed better imaging quality than the 20 mm long non-DOI. For example, the CRC value of a 10 mm hot sphere at COV  =  20% was 72% for the 10 mm long non-DOI, which was 1.2 times higher than that of the 20 mm long non-DOI. The converged CRC values for the 10 mm long non-DOI were almost equivalent to those of the non-TOF 4-layer DOI, and the 10 mm long non-DOI converged faster than the non-TOF 4-layer DOI did. Based on the simulation results, we evaluated a one-pair prototype system of the TOF-PET detectors with 10 mm crystal length, which yielded the CRT of 250  ±  8 ps. In summary, we demonstrated support for feasibility of the brain-dedicated TOF-PET system with the hemispherical detector arrangement.

Partial-ring PET image restoration using a deep learning based method

PET scanners with partial-ring geometry have been proposed for various imaging purposes. The incomplete projection data obtained from this design cause undesirable artifacts in the reconstructed images. In this study, we investigated the performance of a deep learning (DL) based method for the recovery of partial-ring PET images. Twenty digital brain phantoms were used in the Monte Carlo simulation toolkit, SimSET, to simulate 15 min full-ring PET scans. Partial-ring PET data were generated from full-ring PET data by removing coincidence events that hit these specific detector blocks. A convolutional neural network based on the residual U-Net architecture was trained to predict full-ring data from partial-ring data in either the projection or image domain. The performance of the proposed DL-based method was evaluated by comparing with the PET images reconstructed using the full-ring projection data in terms of the mean squared error (MSE), structural similarity (SSIM) index and recovery coefficient (RC). The MSE results showed the superiority of the image-domain approach in reduction of 91.7% in contrast to 14.3% for the projection-domain approach. Therefore, the image-domain approach was used to study the influence of the number of detector block removal. The SSIM results were 0.998, 0.996 and 0.993 for 3, 5 and 7 detector block removals, respectively. The activity of gray and white matters could be fully recovered even with 7 detector block removal, while the RCs of two artificially inserted small lesions (3 pixels in diameter) in the testing data were 94%, 89% and 79% for 3, 5, and 7 detector block removals, respectively. Our simulation results suggest that DL has the potential to recover partial-ring PET images.

Towards continuous-to-continuous 3D imaging in the real world

Imaging systems are often modeled as continuous-to-discrete mappings that map the object (i.e. a function of continuous variables such as space, time, energy, wavelength, etc) to a finite set of measurements. When it comes to reconstruction, some discretized version of the object is almost always assumed, leading to a discrete-to-discrete representation of the imaging system. In this paper, we discuss a method for single-photon emission computed tomography (SPECT) imaging that avoids discrete representations of the object or the imaging system, thus allowing reconstruction on an arbitrarily fine set of points.

Advances in Nuclear Radiation Sensing: Enabling 3-D Gamma-Ray Vision

The enormous advances in sensing and data processing technologies in combination with recent developments in nuclear radiation detection and imaging enable unprecedented and “smarter” ways to detect, map, and visualize nuclear radiation. The recently developed concept of three-dimensional (3-D) Scene-data fusion allows us now to “see” nuclear radiation in three dimensions, in real time, and specific to radionuclides. It is based on a multi-sensor instrument that is able to map a local scene and to fuse the scene data with nuclear radiation data in 3-D while the instrument is freely moving through the scene. This new concept is agnostic of the deployment platform and the specific radiation detection or imaging modality. We have demonstrated this 3-D Scene-data fusion concept in a range of configurations in locations, such as the Fukushima Prefecture in Japan or Chernobyl in Ukraine on unmanned and manned aerial and ground-based platforms. It provides new means in the detection, mapping, and visualization of radiological and nuclear materials relevant for the safe and secure operation of nuclear and radiological facilities or in the response to accidental or intentional releases of radioactive materials where a timely, accurate, and effective assessment is critical. In addition, the ability to visualize nuclear radiation in 3-D and in real time provides new means in the communication with public and facilitates to overcome one of the major public concerns of not being able to “see” nuclear radiation.

Rigid Motion Correction for Brain PET/MR Imaging using Optical Tracking

A significant challenge during high-resolution PET brain imaging on PET/MR scanners is patient head motion. This challenge is particularly significant for clinical patient populations who struggle to remain motionless in the scanner for long periods of time. Head motion also affects the MR scan data. An optical motion tracking technique, which has already been demonstrated to perform MR motion correction during acquisition, is used with a list-mode PET reconstruction algorithm to correct the motion for each recorded event and produce a corrected reconstruction. The technique is demonstrated on real Alzheimer's disease patient data for the GE SIGNA PET/MR scanner.

Astatine-211 imaging by a Compton camera for targeted radiotherapy

Astatine-211 is a promising radionuclide for targeted radiotherapy. It is required to image the distribution of targeted radiotherapeutic agents in a patient's body for optimization of treatment strategies. We proposed to image ²¹¹At with high-energy photons to overcome some problems in conventional planar or single-photon emission computed tomography imaging. We performed an imaging experiment of a point-like ²¹¹At source using a Compton camera, and demonstrated the capability of imaging ²¹¹At with the high-energy photons for the first time.

A low-count reconstruction algorithm for Compton-based prompt gamma imaging

The Compton camera is an imaging device which has been proposed to detect prompt gammas (PGs) produced by proton-nuclear interactions within tissue during proton beam irradiation. Compton-based PG imaging has been developed to verify proton ranges because PG rays, particularly characteristic ones, have strong correlations with the distribution of the proton dose. However, accurate image reconstruction from characteristic PGs is challenging because the detector efficiency and resolution are generally low. Our previous study showed that point spread functions can be incorporated into the reconstruction process to improve image resolution. In this study, we proposed a low-count reconstruction algorithm to improve the image quality of a characteristic PG emission by pooling information from other characteristic PG emissions. PGs were simulated from a proton beam irradiated on a water phantom, and a two-stage Compton camera was used for PG detection. The results show that the image quality of the reconstructed characteristic PG emission is improved with our proposed method in contrast to the standard reconstruction method using events from only one characteristic PG emission. For the 4.44 MeV PG rays, both methods can be used to predict the positions of the peak and the distal falloff with a mean accuracy of 2 mm. Moreover, only the proposed method can improve the estimated positions of the peak and the distal falloff of 5.25 MeV PG rays, and a mean accuracy of 2 mm can be reached.

Null functions in three-dimensional imaging of alpha and beta particles

Null functions of an imaging system are functions in the object space that give exactly zero data. Hence, they represent the intrinsic limitations of the imaging system. Null functions exist in all digital imaging systems, because these systems map continuous objects to discrete data. However, the emergence of detectors that measure continuous data, e.g. particle-processing (PP) detectors, has the potential to eliminate null functions. PP detectors process signals produced by each particle and estimate particle attributes, which include two position coordinates and three components of momentum, as continuous variables. We consider Charged-Particle Emission Tomography (CPET), which relies on data collected by a PP detector to reconstruct the 3D distribution of a radioisotope that emits alpha or beta particles, and show empirically that the null functions are significantly reduced for alpha particles if ≥3 attributes are measured or for beta particles with five attributes measured.

Use of a LYSO-based Compton camera for prompt gamma range verification in proton therapy

PURPOSE

A Compton camera (CC), which measures prompt gammas (PGs) emitted during proton therapy, is a potentially useful imaging device for proton range verification. The aim of this study was to evaluate how well the reconstructed PG images obtained from various two-stage CC configurations reproduce the distal falloff of the PG emission.

METHODS

We conducted Monte Carlo simulations to evaluate different two-stage CCs positioned orthogonal to a proton pencil beam irradiating a water phantom. The results were compared with those obtained for a three-stage CC. In particular, all detectors were made of lutetium-yttrium orthosilicate (LYSO) crystals.

RESULTS

We found that: (a) the position resolution of the detector led to more uncertainty in predicting the depth of maximum emission and distal falloff positions than did the energy resolution of the detector; (b) reducing the thickness of the absorber detector reduces the effect of position resolution on the quality of reconstructed images and improves falloff position estimates; (c) incomplete absorption of PGs can be filtered by restricting incident gamma energies to known PG energy spectral peaks; and (d) there is greater bias and less accuracy in predicting distal falloff positions with the three-stage CC compared with the two-stage CC.

CONCLUSIONS

This study demonstrates the feasibility of using various CC designs and event selection methods to improve the imaging of PG rays. In our designed two-stage CCs, the thin LYSO-based absorber can provide better predictions of the distal falloff positions than the thick one. Compared to three-stage CCs, two-stage CCs are less biased and provide more accurate range verification.

Relaxed ordered subset preconditioned alternating projection algorithm for PET reconstruction with automated penalty weight selection

PURPOSE

Performance of the preconditioned alternating projection algorithm (PAPA) using relaxed ordered subsets (ROS) with a non-smooth penalty function was investigated in positron emission tomography (PET). A higher order total variation (HOTV) regularizer was applied and a method for unsupervised selection of penalty weights based on the measured data is introduced.

METHODS

A ROS version of PAPA with HOTV penalty (ROS-HOTV-PAPA) for PET image reconstruction was developed and implemented. Two-dimensional PET data were simulated using two synthetic phantoms (geometric and brain) in geometry similar to GE D690/710 PET/CT with uniform attenuation, and realistic scatter (25%) and randoms (25%). Three count levels (high/medium/low) corresponding to mean information densities (ID¯s) of 125, 25, and 5 noise equivalent counts (NEC) per support voxel were reconstructed using ROS-HOTV-PAPA. The patients' brain and whole body PET data were acquired at similar ID¯s on GE D690 PET/CT with time-of-fight and were reconstructed using ROS-HOTV-PAPA and available clinical ordered-subset expectation-maximization (OSEM) algorithms. A power-law model of the penalty weights' dependence on ID¯ was semi-empirically derived. Its parameters were elucidated from the data and used for unsupervised selection of the penalty weights within a reduced search space. The resulting image quality was evaluated qualitatively, including reduction of staircase artifacts, image noise, spatial resolution and contrast, and quantitatively using root mean squared error (RMSE) as a global metric. The convergence rates were also investigated.

RESULTS

ROS-HOTV-PAPA converged rapidly, in comparison to non-ROS-HOTV-PAPA, with no evidence of limit cycle behavior. The reconstructed image quality was superior to optimally post-filtered OSEM reconstruction in terms of noise, spatial resolution, and contrast. Staircase artifacts were not observed. Images of the measured phantom reconstructed using ROS-HOTV-PAPA showed reductions in RMSE of 5%-44% as compared with optimized OSEM. The greatest improvement occurred in the lowest count images. Further, ROS-HOTV-PAPA reconstructions produced images with RMSE similar to images reconstructed using optimally post-filtered OSEM but at one-quarter the NEC.

CONCLUSION

Acceleration of HOTV-PAPA was achieved using ROS. This was accompanied by an improved RMSE metric and perceptual image quality that were both superior to that obtained with either clinical or optimized OSEM. This may allow up to a four-fold reduction of the radiation dose to the patients in a PET study, as compared with current clinical practice. The proposed unsupervised parameter selection method provided useful estimates of the penalty weights for the selected phantoms' and patients' PET studies. In sum, the outcomes of this research indicate that ROS-HOTV-PAPA is an appropriate candidate for clinical applications and warrants further research.

Charged-particle emission tomography

Purpose

Conventional charged‐particle imaging techniques — such as autoradiography — provide only two‐dimensional (2D) black ex vivo images of thin tissue slices. In order to get volumetric information, images of multiple thin slices are stacked. This process is time consuming and prone to distortions, as registration of 2D images is required. We propose a direct three‐dimensional (3D) autoradiography technique, which we call charged‐particle emission tomography (CPET). This 3D imaging technique enables imaging of thick tissue sections, thus increasing laboratory throughput and eliminating distortions due to registration. CPET also has the potential to enable in vivo charged‐particle imaging with a window chamber or an endoscope.

Methods

Our approach to charged‐particle emission tomography uses particle‐processing detectors (PPDs) to estimate attributes of each detected particle. The attributes we estimate include location, direction of propagation, and/or the energy deposited in the detector. Estimated attributes are then fed into a reconstruction algorithm to reconstruct the 3D distribution of charged‐particle‐emitting radionuclides. Several setups to realize PPDs are designed. Reconstruction algorithms for CPET are developed.

Results

Reconstruction results from simulated data showed that a PPD enables CPET if the PPD measures more attributes than just the position from each detected particle. Experiments showed that a two‐foil charged‐particle detector is able to measure the position and direction of incident alpha particles.

Conclusions

We proposed a new volumetric imaging technique for charged‐particle‐emitting radionuclides, which we have called charged‐particle emission tomography (CPET). We also proposed a new class of charged‐particle detectors, which we have called particle‐processing detectors (PPDs). When a PPD is used to measure the direction and/or energy attributes along with the position attributes, CPET is feasible.

Flexible mini gamma camera reconstructions of extended sources using step and shoot and list mode

PURPOSE

Hand- and robot-guided mini gamma cameras have been introduced for the acquisition of single-photon emission computed tomography (SPECT) images. Less cumbersome than whole-body scanners, they allow for a fast acquisition of the radioactivity distribution, for example, to differentiate cancerous from hormonally hyperactive lesions inside the thyroid. This work compares acquisition protocols and reconstruction algorithms in an attempt to identify the most suitable approach for fast acquisition and efficient image reconstruction, suitable for localization of extended sources, such as lesions inside the thyroid.

METHODS

Our setup consists of a mini gamma camera with precise tracking information provided by a robotic arm, which also provides reproducible positioning for our experiments. Based on a realistic phantom of the thyroid including hot and cold nodules as well as background radioactivity, the authors compare "step and shoot" (SAS) and continuous data (CD) acquisition protocols in combination with two different statistical reconstruction methods: maximum-likelihood expectation-maximization (ML-EM) for time-integrated count values and list-mode expectation-maximization (LM-EM) for individually detected gamma rays. In addition, the authors simulate lower uptake values by statistically subsampling the experimental data in order to study the behavior of their approach without changing other aspects of the acquired data.

RESULTS

All compared methods yield suitable results, resolving the hot nodules and the cold nodule from the background. However, the CD acquisition is twice as fast as the SAS acquisition, while yielding better coverage of the thyroid phantom, resulting in qualitatively more accurate reconstructions of the isthmus between the lobes. For CD acquisitions, the LM-EM reconstruction method is preferable, as it yields comparable image quality to ML-EM at significantly higher speeds, on average by an order of magnitude.

CONCLUSIONS

This work identifies CD acquisition protocols combined with LM-EM reconstruction as a prime candidate for the wider introduction of SPECT imaging with flexible mini gamma cameras in the clinical practice.

Motion compensation using origin ensembles in awake small animal positron emission tomography

In emission tomographic imaging, the stochastic origin ensembles algorithm provides unique information regarding the detected counts given the measured data. Precision in both voxel and region-wise parameters may be determined for a single data set based on the posterior distribution of the count density allowing uncertainty estimates to be allocated to quantitative measures. Uncertainty estimates are of particular importance in awake animal neurological and behavioral studies for which head motion, unique for each acquired data set, perturbs the measured data. Motion compensation can be conducted when rigid head pose is measured during the scan. However, errors in pose measurements used for compensation can degrade the data and hence quantitative outcomes. In this investigation motion compensation and detector resolution models were incorporated into the basic origin ensembles algorithm and an efficient approach to computation was developed. The approach was validated against maximum liklihood-expectation maximisation and tested using simulated data. The resultant algorithm was then used to analyse quantitative uncertainty in regional activity estimates arising from changes in pose measurement precision. Finally, the posterior covariance acquired from a single data set was used to describe correlations between regions of interest providing information about pose measurement precision that may be useful in system analysis and design. The investigation demonstrates the use of origin ensembles as a powerful framework for evaluating statistical uncertainty of voxel and regional estimates. While in this investigation rigid motion was considered in the context of awake animal PET, the extension to arbitrary motion may provide clinical utility where respiratory or cardiac motion perturb the measured data.

Recent developments in time-of-flight PET

While the first time-of-flight (TOF)-positron emission tomography (PET) systems were already built in the early 1980s, limited clinical studies were acquired on these scanners. PET was still a research tool, and the available TOF-PET systems were experimental. Due to a combination of low stopping power and limited spatial resolution (caused by limited light output of the scintillators), these systems could not compete with bismuth germanate (BGO)-based PET scanners. Developments on TOF system were limited for about a decade but started again around 2000. The combination of fast photomultipliers, scintillators with high density, modern electronics, and faster computing power for image reconstruction have made it possible to introduce this principle in clinical TOF-PET systems. This paper reviews recent developments in system design, image reconstruction, corrections, and the potential in new applications for TOF-PET. After explaining the basic principles of time-of-flight, the difficulties in detector technology and electronics to obtain a good and stable timing resolution are shortly explained. The available clinical systems and prototypes under development are described in detail. The development of this type of PET scanner also requires modified image reconstruction with accurate modeling and correction methods. The additional dimension introduced by the time difference motivates a shift from sinogram- to listmode-based reconstruction. This reconstruction is however rather slow and therefore rebinning techniques specific for TOF data have been proposed. The main motivation for TOF-PET remains the large potential for image quality improvement and more accurate quantification for a given number of counts. The gain is related to the ratio of object size and spatial extent of the TOF kernel and is therefore particularly relevant for heavy patients, where image quality degrades significantly due to increased attenuation (low counts) and high scatter fractions. The original calculations for the gain were based on analytical methods. Recent publications for iterative reconstruction have shown that it is difficult to quantify TOF gain into one factor. The gain depends on the measured distribution, the location within the object, and the count rate. In a clinical situation, the gain can be used to either increase the standardized uptake value (SUV) or reduce the image acquisition time or administered dose. The localized nature of the TOF kernel makes it possible to utilize local tomography reconstruction or to separate emission from transmission data. The introduction of TOF also improves the joint estimation of transmission and emission images from emission data only. TOF is also interesting for new applications of PET-like isotopes with low branching ratio for positron fraction. The local nature also reduces the need for fine angular sampling, which makes TOF interesting for limited angle situations like breast PET and online dose imaging in proton or hadron therapy. The aim of this review is to introduce the reader in an educational way into the topic of TOF-PET and to give an overview of the benefits and new opportunities in using this additional information.