FIRST: Fast Iterative Reconstruction Software for (PET) tomography

OIF-Net: An Optical Flow Registration-Based PET/MR Cross-Modal Interactive Fusion Network for Low-Count Brain PET Image Denoising

The short frames of low-count positron emission tomography (PET) images generally cause high levels of statistical noise. Thus, improving the quality of low-count images by using image postprocessing algorithms to achieve better clinical diagnoses has attracted widespread attention in the medical imaging community. Most existing deep learning-based low-count PET image enhancement methods have achieved satisfying results, however, few of them focus on denoising low-count PET images with the magnetic resonance (MR) image modality as guidance. The prior context features contained in MR images can provide abundant and complementary information for single low-count PET image denoising, especially in ultralow-count (2.5%) cases. To this end, we propose a novel two-stream dual PET/MR cross-modal interactive fusion network with an optical flow pre-alignment module, namely, OIF-Net. Specifically, the learnable optical flow registration module enables the spatial manipulation of MR imaging inputs within the network without any extra training supervision. Registered MR images fundamentally solve the problem of feature misalignment in the multimodal fusion stage, which greatly benefits the subsequent denoising process. In addition, we design a spatial-channel feature enhancement module (SC-FEM) that considers the interactive impacts of multiple modalities and provides additional information flexibility in both the spatial and channel dimensions. Furthermore, instead of simply concatenating two extracted features from these two modalities as an intermediate fusion method, the proposed cross-modal feature fusion module (CM-FFM) adopts cross-attention at multiple feature levels and greatly improves the two modalities' feature fusion procedure. Extensive experimental assessments conducted on real clinical datasets, as well as an independent clinical testing dataset, demonstrate that the proposed OIF-Net outperforms the state-of-the-art methods.

UMC-PET: a fast and flexible Monte Carlo PET simulator

Objective.The GPU-based Ultra-fast Monte Carlo positron emission tomography simulator (UMC-PET) incorporates the physics of the emission, transport and detection of radiation in PET scanners. It includes positron range, non-colinearity, scatter and attenuation, as well as detector response. The objective of this work is to present and validate UMC-PET as a a multi-purpose, accurate, fast and flexible PET simulator.Approach.We compared UMC-PET against PeneloPET, a well-validated MC PET simulator, both in preclinical and clinical scenarios. Different phantoms for scatter fraction (SF) assessment following NEMA protocols were simulated in a 6R-SuperArgus and a Biograph mMR scanner, comparing energy histograms, NEMA SF, and sensitivity for different energy windows. A comparison with real data reported in the literature on the Biograph scanner is also shown.Main results.NEMA SF and sensitivity estimated by UMC-PET where within few percent of PeneloPET predictions. The discrepancies can be attributed to small differences in the physics modeling. Running in a 11 GB GeForce RTX 2080 Ti GPU, UMC-PET is ∼1500 to ∼2000 times faster than PeneloPET executing in a single core Intel(R) Xeon(R) CPU W-2155 @ 3.30 GHz.Significance.UMC-PET employs a voxelized scheme for the scanner, patient adjacent objects (such as shieldings or the patient bed), and the activity distribution. This makes UMC-PET extremely flexible. Its high simulation speed allows applications such as MC scatter correction, faster SRM estimation for complex scanners, or even MC iterative image reconstruction.

Reconstruction of multi-animal PET acquisitions with anisotropically variant PSF

Among other factors such as random, attenuation and scatter corrections, uniform spatial resolution is key to performing accurate quantitative studies in Positron emission tomography (PET). Particularly in preclinical PET studies involving simultaneous acquisition of multiple animals, the degradation of image resolution due to the depth of interaction (DOI) effect far from the center of the Field of View (FOV) becomes a significant concern. In this work, we incorporated a spatially-variant resolution model into a real time iterative reconstruction code to obtain accurate images of multi-animal acquisition. We estimated the spatially variant point spread function (SV-PSF) across the FOV using measurements and Monte Carlo (MC) simulations. The SV-PSF obtained was implemented in a GPU-based Ordered subset expectation maximization (OSEM) reconstruction code, which includes scatter, attenuation and random corrections. The method was evaluated with acquisitions from two preclinical PET/CT scanners of the SEDECAL Argus family: a Derenzo phantom placed 2 cm off center in the 4R-SuperArgus, and a multi-animal study with 4 mice in the 6R-SuperArgus. The SV-PSF reconstructions showed uniform spatial resolution without significant increase in reconstruction time, with superior image quality compared to the uniform PSF model.

In vivo production of fluorine-18 in a chicken egg tumor model of breast cancer for proton therapy range verification

Range verification of clinical protontherapy systems via positron-emission tomography (PET) is not a mature technology, suffering from two major issues: insufficient signal from low-energy protons in the Bragg peak area and biological washout of PET emitters. The use of contrast agents including ¹⁸O, ⁶⁸Zn or ⁶³Cu, isotopes with a high cross section for low-energy protons in nuclear reactions producing PET emitters, has been proposed to enhance the PET signal in the last millimeters of the proton path. However, it remains a challenge to achieve sufficient concentrations of these isotopes in the target volume. Here we investigate the possibilities of ¹⁸O-enriched water (18-W), a potential contrast agent that could be incorporated in large proportions in live tissues by replacing regular water. We hypothesize that 18-W could also mitigate the problem of biological washout, as PET (¹⁸F) isotopes created inside live cells would remain trapped in the form of fluoride anions (F-), allowing its signal to be detected even hours after irradiation. To test our hypothesis, we designed an experiment with two main goals: first, prove that 18-W can incorporate enough ¹⁸O into a living organism to produce a detectable signal from ¹⁸F after proton irradiation, and second, determine the amount of activity that remains trapped inside the cells. The experiment was performed on a chicken embryo chorioallantoic membrane tumor model of head and neck cancer. Seven eggs with visible tumors were infused with 18-W and irradiated with 8-MeV protons (range in water: 0.74 mm), equivalent to clinical protons at the end of particle range. The activity produced after irradiation was detected and quantified in a small-animal PET-CT scanner, and further studied by placing ex-vivo tumours in a gamma radiation detector. In the acquired images, specific activity of ¹⁸F (originating from 18-W) could be detected in the tumour area of the alive chicken embryo up to 9 h after irradiation, which confirms that low-energy protons can indeed produce a detectable PET signal if a suitable contrast agent is employed. Moreover, dynamic PET studies in two of the eggs evidenced a minimal effect of biological washout, with 68% retained specific ¹⁸F activity at 8 h after irradiation. Furthermore, ex-vivo analysis of 4 irradiated tumours showed that up to 3% of oxygen atoms in the targets were replaced by ¹⁸O from infused 18-W, and evidenced an entrapment of 59% for specific activity of ¹⁸F after washing, supporting our hypothesis that F- ions remain trapped within the cells. An infusion of 18-W can incorporate ¹⁸O in animal tissues by replacing regular water inside cells, producing a PET signal when irradiated with low-energy protons that could be used for range verification in protontherapy. ¹⁸F produced inside cells remains entrapped and suffers from minimal biological washout, allowing for a sharper localization with longer PET acquisitions. Further studies must evaluate the feasibility of this technique in dosimetric conditions closer to clinical practice, in order to define potential protocols for its use in patients.

Monitoring PSMA Responses to ADT in Prostate Cancer Patient-Derived Xenograft Mouse Models Using [18F]DCFPyL PET Imaging

PURPOSE

PSMA overexpression has been associated with aggressive prostate cancer (PCa). However, PSMA PET imaging has revealed highly variable changes in PSMA expression in response to ADT treatment ranging from increases to moderate decreases. To better understand these PSMA responses and potential relationship to progressive PCa, the PET imaging agent, [¹⁸F]DCFPyL, was used to assess changes in PSMA expression in response to ADT using genomically characterized LuCaP patient-derived xenograft mouse models (LuCaP-PDXs) which were found to be sensitive to ADT (LuCaP73 and LuCaP136;CS) or resistant (LuCaP167;CR).

METHODS

[¹⁸F]DCFPyL (2-(3-{1-carboxy-5-[(6-[¹⁸F]fluoro-pyridine-3-carbonyl)-amino]-pentyl}-ureido)-pentanedioic acid) was used to assess PSMA in vitro (saturation assays) in LuCaP tumor membrane homogenates and in vivo (imaging/biodistribution) in LuCaP-PDXs. Control and ADT-treated LuCaPs were imaged before ADT (0 days) and 2-, 7-, 14-, and 21-days post-ADT from which tumor:muscle ratios (T:Ms) were determined and concurrently tumor volumes were measured (caliper). After the 21-day imaging, biodistributions and histologic/genomic (PSMA, AR) analysis were done.

RESULTS

[¹⁸F]DCFPyL exhibited high affinity for PSMA and distinguished different levels of PSMA in LuCaP tumors. Post-ADT CS LuCaP73 and LuCaP136 tumor volumes significantly decreased at day 7 or 14 respectively vs controls, whereas the CR LuCaP167 tumor volumes were minimally changed. [¹⁸F]DCFPyL imaging T:Ms were increased 3-5-fold in treated LuCaP73 tumors vs controls, while treated LuCaP136 T:Ms remained unchanged which was confirmed by day 21 biodistribution results. For treated LuCaP167, T:Ms were decreased (~ 45 %) vs controls but due to low T:M values (<2) may not be indicative of PSMA level changes. LuCaP73 tumor PSMA histologic/genomic results were comparable to imaging/biodistribution results, whereas the results for other tumor types varied.

CONCLUSION

Tumor responses to ADT varied from sensitive to resistant among these LuCaP PDXs, while only the high PSMA expressing LuCaP model exhibited an increase in PSMA levels in response to ADT. These models may be useful in understanding the clinical relevance of PSMA PET responses to ADT and potentially the relationship to disease progression as it may relate to the genomic signature.

Deep-Learning Based Positron Range Correction of PET Images

Positron emission tomography (PET) is a molecular imaging technique that provides a 3D image of functional processes in the body in vivo. Some of the radionuclides proposed for PET imaging emit high-energy positrons, which travel some distance before they annihilate (positron range), creating significant blurring in the reconstructed images. Their large positron range compromises the achievable spatial resolution of the system, which is more significant when using high-resolution scanners designed for the imaging of small animals. In this work, we trained a deep neural network named Deep-PRC to correct PET images for positron range effects. Deep-PRC was trained with modeled cases using a realistic Monte Carlo simulation tool that considers the positron energy distribution and the materials and tissues it propagates into. Quantification of the reconstructed PET images corrected with Deep-PRC showed that it was able to restore the images by up to 95% without any significant noise increase. The proposed method, which is accessible via Github, can provide an accurate positron range correction in a few seconds for a typical PET acquisition.

Deep-Learning Based Positron Range Correction of PET Images

Positron emission tomography (PET) is a molecular imaging technique that provides a 3D image of functional processes in the body in vivo. Some of the radionuclides proposed for PET imaging emit high-energy positrons, which travel some distance before they annihilate (positron range), creating significant blurring in the reconstructed images. Their large positron range compromises the achievable spatial resolution of the system, which is more significant when using high-resolution scanners designed for the imaging of small animals. In this work, we trained a deep neural network named Deep-PRC to correct PET images for positron range effects. Deep-PRC was trained with modeled cases using a realistic Monte Carlo simulation tool that considers the positron energy distribution and the materials and tissues it propagates into. Quantification of the reconstructed PET images corrected with Deep-PRC showed that it was able to restore the images by up to 95% without any significant noise increase. The proposed method, which is accessible via Github, can provide an accurate positron range correction in a few seconds for a typical PET acquisition.

Real-Time 3D PET Image with Pseudoinverse Reconstruction

Real-time positron emission tomography (PET) may provide information from first-shot images, enable PET-guided biopsies, and allow awake animal studies. Fully-3D iterative reconstructions yield the best images in PET, but they are too slow for real-time imaging. Analytical methods such as Fourier back projection (FBP) are very fast, but yield images of poor quality with artifacts due to noise or data incompleteness. In this work, an image reconstruction based on the pseudoinverse of the system response matrix (SRM) is presented. w. To implement the pseudoinverse method, the reconstruction problem is separated into two stages. First, the axial part of the SRM is pseudo-inverted (PINV) to rebin the 3D data into 2D datasets. Then, the resulting 2D slices can be reconstructed with analytical methods or by applying the pseudoinverse algorithm again. The proposed two-step PINV reconstruction yielded good-quality images at a rate of several frames per second, compatible with real time applications. Furthermore, extremely fast direct PINV reconstruction of projections of the 3D image collapsed along specific directions can be implemented.

Real-Time 3D PET Image with Pseudoinverse Reconstruction

Real-time positron emission tomography (PET) may provide information from first-shot images, enable PET-guided biopsies, and allow awake animal studies. Fully-3D iterative reconstructions yield the best images in PET, but they are too slow for real-time imaging. Analytical methods such as Fourier back projection (FBP) are very fast, but yield images of poor quality with artifacts due to noise or data incompleteness. In this work, an image reconstruction based on the pseudoinverse of the system response matrix (SRM) is presented. w. To implement the pseudoinverse method, the reconstruction problem is separated into two stages. First, the axial part of the SRM is pseudo-inverted (PINV) to rebin the 3D data into 2D datasets. Then, the resulting 2D slices can be reconstructed with analytical methods or by applying the pseudoinverse algorithm again. The proposed two-step PINV reconstruction yielded good-quality images at a rate of several frames per second, compatible with real time applications. Furthermore, extremely fast direct PINV reconstruction of projections of the 3D image collapsed along specific directions can be implemented.

Evaluation of quantitative, efficient image reconstruction for VersaPET, a compact PET system

PURPOSE

Previously we developed a high-resolution positron emission tomography (PET) system-VersaPET-characterized by a block geometry with relatively large axial and transaxial interblock gaps and a compact geometry susceptible to parallax blurring effects. In this work, we report the qualitative and quantitative evaluation of a graphic processing unit (GPU)-accelerated maximum-likelihood by expectation-maximization (MLEM) image reconstruction framework for VersaPET which features accurate system geometry and projection space point-spread-function (PSF) modeling.

METHODS

We combined the ray-tracing module from software for tomographic image reconstruction (STIR), an open-source PET image reconstruction package, with VersaPET's exact block geometry for the geometric system matrix. Point-spread-function modeling of crystal penetration and scattering was achieved by a custom Monte-Carlo simulation for projection space blurring in all dimensions. We also parallelized the reconstruction in GPU taking advantage of the system's symmetry for PSF computation. To investigate the effects of PSF width, we generated and studied multiple kernels between one that reflects the true LYSO density in the MC simulation and another that reflects geometry only (no PSF). GATE simulations of hot and cold-sphere phantoms with spheres of different sizes, real microDerenzo phantom, and human blood vessel data were used to characterize the quantitative and qualitative performances of the reconstruction.

RESULTS

Reconstruction with an accurate system geometry effectively improved image quality compared to STIR (version 3.0) which assumes an idealized system geometry. Reconstructions of GATE-simulated hot-sphere phantom data showed that all PSF kernels achieved superior performance in contrast recovery and bias reduction compared to using no PSF, but may introduce edge artifact and lumped background noise pattern depending on the width of PSF kernels. Cold-sphere phantom simulation results also indicated improvement in contrast recovery and quantification with PSF modeling (compared to no PSF) for 5 and 10 mm cold spheres. Real microDerenzo phantom images with the PSF kernel that reflects the true LYSO density showed degraded resolving power of small sectors that could be resolved more clearly by underestimated PSF kernels, which is consistent with recent literature despite differences in scanner geometries and in approaches to system model estimation. The human vessel results resemble those of the hot-sphere phantom simulation with the PSF kernel that reflects the true LYSO density achieving the highest peak in the time activity curve (TAC) and similar lumped noise pattern.

CONCLUSIONS

We fully evaluated a practical MLEM reconstruction framework that we developed for VersaPET in terms of qualitative and quantitative performance. Different PSF kernels may be adopted for improving the results of specific imaging tasks but the underlying reasons for the variation in optimal kernel for the real and simulation studies requires further study.

The Distribution Volume of 18F-Albumin as a Potential Biomarker of Antiangiogenic Treatment Efficacy

Objective: 18F-albumin, a vascular imaging agent, may have potential to assess tumor responses to anti-angiogenic therapies. In these studies tumor distribution volume of 18F-albumin were first determined in various human tumor xenografts from biodistribtuion measurments and then one of the tumor type was used to evaluate changes in 18F-albumin uptake in anti-angiognic tumor model. Method: 18F-albumin was synthesized via conjugation of 6-[18F]fluoronicotinic acid-2,3,5,6-tetrafluorophenyl ester, [18F]F-Py-TFP, with rat albumin. From the biodistribution of 18F-albumin in various human tumor xenografts tumor distribution volumes (DVs; tumor%ID/g:blood%ID/g) were first determined at various time points. Then, the ability of 18F-albumin to detect tumor angiogenic inhibition in one of these tumor types (U87MG) following treatment with sunitinib was evaluated by position emission tomography (PET) imaging at 0, 7, 14, and 21 days post treatment. Caliper measurements of tumor dimensions were also made at these same times. At Day 21, following imaging, biodistributions, autoradiography of tumor tissues and tumor blood vessel counts (CD31 IHC) were performed. Results: 18F-albumin retention in various tumors steadily increased over time with U87MG tumor exhibiting the highest uptake (DV) at all times. Significant decreases in 18F-albumin DVs were observed one week post-treatement (-39%) vs. controls whereas tumor caliper volumes were not significantly decreased until days 14 and 21. At day 21 the significant decrease in DVs in the treatment group (-44%) paralleled biodistribution DV measurements and was consistent with autoradiography and CD31 IHC findings. Conclusion: These data suggest that 18F-albumin DVs obtained by imaging may serve as an early biomarker of the effectiveness of anti-angiogenic therapy and thus aid in patient management and treatment planning.

An automatic algorithm to exploit the symmetries of the system response matrix in positron emission tomography iterative reconstruction

Positron emission tomography (PET) iterative 3D reconstruction is a very computational demanding task. One of the main issues of the iterative reconstruction concerns the management of the system response matrix (SRM). The SRM models the relationship between the projection and the voxel space and its memory footprint can easily exceed hundreds of GB. Moreover, in order to make the reconstruction fast enough not to hinder its practical application, the SRM must be stored in the random access memory of the workstation used for the reconstruction. This issue is normally solved by implementing efficient storage schemes and by reducing the number of redundant patterns in the SRM through symmetries. However, finding a sufficient number of symmetries is often non-trivial and is typically performed using dedicated solutions that cannot be exported to different detectors and geometries. In this paper, an automatic approach to reduce the memory footprint of a pre-computed SRM is described. The proposed approach was named symmetry search algorithm (SSA) and consists in an algorithm that searches for some of the redundant patterns present in the SRM, leading to its lossy compression. This approach was built to detect translations, reflections and coordinates swap in voxel space. Therefore, it is particularly well suited for those scanners where some of the rotational symmetries are broken, e.g. small animal scanner where the modules are arranged in a polygonal ring made of few elements, and dual head planar PET systems. In order to validate this approach, the SSA is applied to the SRM of a preclinical scanner (the IRIS PET/CT). The data acquired by the scanner were reconstructed with a dedicated maximum likelihood estimation maximization algorithm with both the uncompressed and the compressed SRMs. The results achieved show that the information lost due to the SSA compression is negligible. Compression factors up to 52 when using the SSA together with manually inserted symmetries and up to 204 when using the SSA alone, can be obtained for the IRIS SRM. These results come without significant differences in the values and in the main quality metrics of the reconstructed images, i.e. spatial resolution and noise. Although the compression factors depend on the system considered, the SSA is applicable to any SRM and therefore it can be considered a general tool to reduce the footprint of a pre-computed SRM.

Improving PET Quantification of Small Animal [68Ga]DOTA-Labeled PET/CT Studies by Using a CT-Based Positron Range Correction

PURPOSE

Image quality of positron emission tomography (PET) tracers that emits high-energy positrons, such as Ga-68, Rb-82, or I-124, is significantly affected by positron range (PR) effects. PR effects are especially important in small animal PET studies, since they can limit spatial resolution and quantitative accuracy of the images. Since generators accessibility has made Ga-68 tracers wide available, the aim of this study is to show how the quantitative results of [⁶⁸Ga]DOTA-labeled PET/X-ray computed tomography (CT) imaging of neuroendocrine tumors in mice can be improved using positron range correction (PRC).

PROCEDURES

Eighteen scans in 12 mice were evaluated, with three different models of tumors: PC12, AR42J, and meningiomas. In addition, three different [⁶⁸Ga]DOTA-labeled radiotracers were used to evaluate the PRC with different tracer distributions: [⁶⁸Ga]DOTANOC, [⁶⁸Ga]DOTATOC, and [⁶⁸Ga]DOTATATE. Two PRC methods were evaluated: a tissue-dependent (TD-PRC) and a tissue-dependent spatially-variant correction (TDSV-PRC). Taking a region in the liver as reference, the tissue-to-liver ratio values for tumor tissue (TLR_tumor), lung (TLR_lung), and necrotic areas within the tumors (TLR_necrotic) and their respective relative variations (ΔTLR) were evaluated.

RESULTS

All TLR values in the PRC images were significantly different (p < 0.05) than the ones from non-PRC images. The relative differences of the tumor TLR values, respect to the case with no PRC, were ΔTLR_tumor 87 ± 41 % (TD-PRC) and 85 ± 46 % (TDSV-PRC). TLR_lung decreased when applying PRC, being this effect more remarkable for the TDSV-PRC method, with relative differences respect to no PRC: ΔTLR_lung = - 45 ± 24 (TD-PRC), - 55 ± 18 (TDSV-PRC). TLR_necrotic values also decreased when using PRC, with more noticeable differences for TD-PRC: ΔTLR_necrotic = - 52 ± 6 (TD-PRC), - 48 ± 8 (TDSV-PRC).

CONCLUSION

The PRC methods proposed provide a significant quantitative improvement in [⁶⁸Ga]DOTA-labeled PET/CT imaging of mice with neuroendocrine tumors, hence demonstrating that these techniques could also ameliorate the deleterious effect of the positron range in clinical PET imaging.

One-pot synthesis and biodistribution of fluorine-18 labeled serum albumin for vascular imaging

INTRODUCTION

Equilibrium single-photon radionuclide imaging methods for assessing cardiac function and the integrity of the vascular system have long been in use for both clinical and research purposes. However, positron-emitting blood pool agents that could provide PET equivalents to these (and other) clinical procedures have not yet been adopted despite technical imaging advantages offered by PET. Our goal was to develop a PET blood pool tracer that not only meets necessary in vivo biological requirements but can be produced with an uncomplicated and rapid synthesis method which would facilitate clinical translation. Herein, albumin labeled with fluorine-18 was synthesized using a one-pot method and evaluated in vitro and in vivo in rats.

METHODS

A ligand (NODA-Bz-TFPE), containing NODA attached to a tetrafluorophenylester (TFPE) via a phenyl linker (Bz), was labeled with aluminum fluoride (Al[18F]F). Conjugation of the serum albumin with the ligand (Al[18F]F-NODA-Bz-TFPE), followed by purification (size exclusion chromatography), yielded the final product (Al[18F]F-NODA-Bz-RSA/HSA). In vitro stability was evaluated in human serum albumin by HPLC. Rat biodistributions and whole-body PET imaging over a 4 h time course were used for the in vivo evaluation.

RESULTS

This synthesis exhibited an overall radiochemical yield of 45 ± 10% (n = 30), a 50-min radiolabeling time, a radiochemical purity >99% and apparent stability up to 4 h in human serum. Blood had the highest retention of Al[18F]F-NODA-Bz-RSA at all times with a blood half-life of 5.2 h in rats. Al[18F]F-NODA-Bz-RSA distribution in most rat tissues remained relatively constant for up to 1 h, indicating that the tissue radioactivity content represents the respective tissue plasma volume. Dynamic whole-body PET images were in agreement with these findings.

CONCLUSIONS

A new ligand has been developed and radiolabeled with Al[18F]F that allows rapid (50-min) preparation of fluorine-18 serum albumin in one-pot. In addition to increased synthetic efficiency, the construct appears to be metabolically stable in rats. This method could encourage wider use of PET to quantify cardiac function and tissue vascular integrity in both research and clinical settings.

PET performance evaluation of MADPET4: a small animal PET insert for a 7 T MRI scanner

MADPET4 is the first small animal PET insert with two layers of individually read out crystals in combination with silicon photomultiplier technology. It has a novel detector arrangement, in which all crystals face the center of field of view transaxially. In this work, the PET performance of MADPET4 was evaluated and compared to other preclinical PET scanners using the NEMA NU 4 measurements, followed by imaging a mouse-size hot-rod resolution phantom and two in vivo simultaneous PET/MRI scans in a 7 T MRI scanner. The insert had a peak sensitivity of 0.49%, using an energy threshold of 350 keV. A uniform transaxial resolution was obtained up to 15 mm radial offset from the axial center, using filtered back-projection with single-slice rebinning. The measured average radial and tangential resolutions (FWHM) were 1.38 mm and 1.39 mm, respectively. The 1.2 mm rods were separable in the hot-rod phantom using an iterative image reconstruction algorithm. The scatter fraction was 7.3% and peak noise equivalent count rate was 15.5 kcps at 65.1 MBq of activity. The FDG uptake in a mouse heart and brain were visible in the two in vivo simultaneous PET/MRI scans without applying image corrections. In conclusion, the insert demonstrated a good overall performance and can be used for small animal multi-modal research applications.

Automatic Cardiac Self-Gating of Small-Animal PET Data

PURPOSE

The cardiac gating signal (CGS) in positron emission tomography (PET) studies is usually obtained from an electrocardiography (ECG) monitor. In this work, we propose a method to obtain the CGS in small-animal PET using the acquired list-mode data without using any hardware or end-user input.

PROCEDURES

The CGS was obtained from the number of coincidences over time acquired in the lines-of-response connected with the cardiac region. This region is identified in the image as its value changes with frequencies in the range of 3 to 12 Hz. The procedure was tested in a study with 29 Wistar rats and 6 mice injected with 2-deoxy-2-[(18)F]fluoro-D-glucose, which underwent a 45-min single-bed list-mode PET scan of the heart syncronized with an ECG. The estimated signals and the reconstructed images using eight-gated frames were compared with the ones obtained using the ECG signal from the monitor.

RESULTS

The differences of the PET-based CGS with respect to the ECG relative to the duration of the heartbeats were 5.6 % in rats and 11.0 % in mice. The reconstructed gated images obtained from the proposed method do not differ qualitatively with respect to the ones obtained with the ECG. The quantitative analysis of both set of images were performed measuring the volume of the left ventricle (LV) of the rats in the end-of-systole and end-of-diastole phase. The differences found in these parameters between both methods were below 12.1 % in the ESV and 9.3 % in the EDV with a 95 % confidence interval, which are comparable to the accuracy (7 %) of the method used for segmenting the LV.

CONCLUSION

The proposed method is able to provide a valid and accurate CGS in small-animal PET list-mode data.

High-Throughput Sparsity-Based Inversion Scheme for Optoacoustic Tomography

The concept of sparsity is extensively exploited in the fields of data acquisition and image processing, contributing to better signal-to-noise and spatio-temporal performance of the various imaging methods. In the field of optoacoustic tomography, the image reconstruction problem is often characterized by computationally extensive inversion of very large datasets, for instance when acquiring volumetric multispectral data with high temporal resolution. In this article we seek to accelerate accurate model-based optoacoustic inversions by identifying various sources of sparsity in the forward and inverse models as well as in the single- and multi-frame representation of the projection data. These sources of sparsity are revealed through appropriate transformations in the signal, model and image domains and are subsequently exploited for expediting image reconstruction. The sparsity-based inversion scheme was tested with experimental data, offering reconstruction speed enhancement by a factor of 40 to 700 times as compared with the conventional iterative model-based inversions while preserving similar image quality. The demonstrated results pave the way for achieving real-time performance of model-based reconstruction in multi-dimensional optoacoustic imaging.

Tissue-Dependent and Spatially-Variant Positron Range Correction in 3D PET

Positron range (PR) is a significant factor that limits PET image resolution, especially with some radionuclides currently used in clinical and preclinical studies such as (82)Rb, (124)I and (68)Ga. The use of an accurate model of the PR in the image reconstruction may minimize its impact on the image quality. Nevertheless, PR distributions are difficult to model, as they may be different at each voxel and direction, depending on the materials that the positron flies through. Several approximated methods have been proposed, considering only one or several propagating media without taking into account boundaries effects. In some regions, like lungs or trachea, these methods may not be accurate enough and yield artifacts. In this work, we present an efficient method to accurately incorporate spatially-variant PR corrections. The method is based on pre-computing voxel-dependent PR kernels using a CT or a manually segmented image, and a model of the dependence of the PR on each material derived from Monte Carlo simulations. The images are convoluted with these kernels in the forward-projection step of the iterative reconstruction algorithm. This implementation of the algorithm adds a modest overhead to the overall reconstruction time and it obtains artifact-free PR-corrected images, even when the activity is concentrated at tissue boundaries with extreme changes of density. We verified the method with the preclinical Argus PET/CT scanner, but it can be also applied to other scanners and improve the image quality in clinical PET studies using isotopes with large PR.

Improved quantification for local regions of interest in preclinical PET imaging

In Positron Emission Tomography, there are several causes of quantitative inaccuracy, such as partial volume or spillover effects. The impact of these effects is greater when using radionuclides that have a large positron range, e.g. (68)Ga and (124)I, which have been increasingly used in the clinic. We have implemented and evaluated a local projection algorithm (LPA), originally evaluated for SPECT, to compensate for both partial-volume and spillover effects in PET. This method is based on the use of a high-resolution CT or MR image, co-registered with a PET image, which permits a high-resolution segmentation of a few tissues within a volume of interest (VOI) centered on a region within which tissue-activity values need to be estimated. The additional boundary information is used to obtain improved activity estimates for each tissue within the VOI, by solving a simple inversion problem. We implemented this algorithm for the preclinical Argus PET/CT scanner and assessed its performance using the radionuclides (18)F, (68)Ga and (124)I. We also evaluated and compared the results obtained when it was applied during the iterative reconstruction, as well as after the reconstruction as a postprocessing procedure. In addition, we studied how LPA can help to reduce the 'spillover contamination', which causes inaccurate quantification of lesions in the immediate neighborhood of large, 'hot' sources. Quantification was significantly improved by using LPA, which provided more accurate ratios of lesion-to-background activity concentration for hot and cold regions. For (18)F, the contrast was improved from 3.0 to 4.0 in hot lesions (when the true ratio was 4.0) and from 0.16 to 0.06 in cold lesions (true ratio  =  0.0), when using the LPA postprocessing. Furthermore, activity values estimated within the VOI using LPA during reconstruction were slightly more accurate than those obtained by post-processing, while also visually improving the image contrast and uniformity within the VOI.

Phase space determination from measured dose data for intraoperative electron radiation therapy

A procedure to characterize beams of a medical linear accelerator for their use in Monte Carlo (MC) dose calculations for intraoperative electron radiation therapy (IOERT) is presented. The procedure relies on dose measurements in homogeneous media as input, avoiding the need for detailed simulations of the accelerator head. An iterative algorithm (EM-ML) has been employed to extract the relevant details of the phase space (PHSP) of the particles coming from the accelerator, such as energy spectra, spatial distribution and angle of emission of particles. The algorithm can use pre-computed dose volumes in water and/or air, so that the machine-specific tuning with actual data can be performed in a few minutes. To test the procedure, MC simulations of a linear accelerator with typical IOERT applicators and energies, have been performed and taken as reference. A solution PHSP derived from the dose produced by the simulated accelerator has been compared to the reference PHSP. Further, dose delivered by the simulated accelerator for setups not included in the fit of the PHSP were compared to the ones derived from the solution PHSP. The results show that it is possible to derive from dose measurements PHSP accurate for IOERT MC dose estimations.

Monte Carlo simulations versus experimental measurements in a small animal PET system. A comparison in the NEMA NU 4-2008 framework

In this work a comparison between experimental and simulated data using GATE and PeneloPET Monte Carlo simulation packages is presented. All simulated setups, as well as the experimental measurements, followed exactly the guidelines of the NEMA NU 4-2008 standards using the microPET R4 scanner. The comparison was focused on spatial resolution, sensitivity, scatter fraction and counting rates performance. Both GATE and PeneloPET showed reasonable agreement for the spatial resolution when compared to experimental measurements, although they lead to slight underestimations for the points close to the edge. High accuracy was obtained between experiments and simulations of the system's sensitivity and scatter fraction for an energy window of 350-650 keV, as well as for the counting rate simulations. The latter was the most complicated test to perform since each code demands different specifications for the characterization of the system's dead time. Although simulated and experimental results were in excellent agreement for both simulation codes, PeneloPET demanded more information about the behavior of the real data acquisition system. To our knowledge, this constitutes the first validation of these Monte Carlo codes for the full NEMA NU 4-2008 standards for small animal PET imaging systems.

Simultaneous ECG-gated PET imaging of multiple mice

INTRODUCTION

We describe and illustrate a method for creating ECG-gated PET images of the heart for each of several mice imaged at the same time. The method is intended to increase "throughput" in PET research studies of cardiac dynamics or to obtain information derived from such studies, e.g. tracer concentration in end-diastolic left ventricular blood.

METHODS

An imaging bed with provisions for warming, anesthetic delivery, etc., was fabricated by 3D printing to allow simultaneous PET imaging of two side-by-side mice. After electrode attachment, tracer injection and placement of the animals in the scanner field of view, ECG signals from each animal were continuously analyzed and independent trigger markers generated whenever an R-wave was detected in each signal. PET image data were acquired in "list" mode and these trigger markers were inserted into this list along with the image data. Since each mouse is in a different spatial location in the FOV, sorting of these data using trigger markers first from one animal and then the other yields two independent and correctly formed ECG-gated image sequences that reflect the dynamical properties of the heart during an "average" cardiac cycle.

RESULTS

The described method yields two independent ECG-gated image sequences that exhibit the expected properties in each animal, e.g. variation of the ventricular cavity volumes from maximum to minimum and back during the cardiac cycle in the processed animal with little or no variation in these volumes during the cardiac cycle in the unprocessed animal.

CONCLUSION

ECG-gated image sequences for each of several animals can be created from a single list mode data collection using the described method. In principle, this method can be extended to more than two mice (or other animals) and to other forms of physiological gating, e.g. respiratory gating, when several subjects are imaged at the same time.

Attenuation correction for small animal PET images: a comparison of two methods

In order to extract quantitative parameters from PET images, several physical effects such as photon attenuation, scatter, and partial volume must be taken into account. The main objectives of this work were the evaluation of photon attenuation in small animals and the implementation of two attenuation correction methods based on X-rays CT and segmentation of emission images. The accuracy of the first method with respect to the beam hardening effect was investigated by using Monte Carlo simulations. Mouse- and rat-sized phantoms were acquired in order to evaluate attenuation correction in terms of counts increment and recovery of uniform activity concentration. Both methods were applied to mice and rat images acquired with several radiotracers such as(18)F-FDG, (11)C-acetate, (68)Ga-chloride, and (18)F-NaF. The accuracy of the proposed methods was evaluated in heart and tumour tissues using (18)F-FDG images and in liver, kidney, and spinal column tissues using (11)C-acetate, (68)Ga-chloride, and (18)F-NaF images, respectively. In vivo results from animal studies show that, except for bone scans, differences between the proposed methods were about 10% in rats and 3% in mice. In conclusion, both methods provide equivalent results; however, the segmentation-based approach has several advantages being less time consuming and simple to implement.

A low memory cost model based reconstruction algorithm exploiting translational symmetry for photoacustic microscopy

A model based reconstruction algorithm that exploits translational symmetries for photoacoustic microscopy to drastically reduce the memory cost is presented. The memory size needed to store the model matrix is independent of the number of acquisitions at different positions. This helps us to overcome one of the main limitations of previous algorithms. Furthermore, using the algebraic reconstruction technique and building the model matrix "on the fly", we have obtained fast reconstructions of simulated and experimental data on both two- and three-dimensional grids using a traditional dark field photoacoustic microscope and a standard personal computer.

Accelerating image reconstruction in dual-head PET system by GPU and symmetry properties

Positron emission tomography (PET) is an important imaging modality in both clinical usage and research studies. We have developed a compact high-sensitivity PET system that consisted of two large-area panel PET detector heads, which produce more than 224 million lines of response and thus request dramatic computational demands. In this work, we employed a state-of-the-art graphics processing unit (GPU), NVIDIA Tesla C2070, to yield an efficient reconstruction process. Our approaches ingeniously integrate the distinguished features of the symmetry properties of the imaging system and GPU architectures, including block/warp/thread assignments and effective memory usage, to accelerate the computations for ordered subset expectation maximization (OSEM) image reconstruction. The OSEM reconstruction algorithms were implemented employing both CPU-based and GPU-based codes, and their computational performance was quantitatively analyzed and compared. The results showed that the GPU-accelerated scheme can drastically reduce the reconstruction time and thus can largely expand the applicability of the dual-head PET system.

High Performance 3D PET Reconstruction Using Spherical Basis Functions on a Polar Grid

Statistical iterative methods are a widely used method of image reconstruction in emission tomography. Traditionally, the image space is modelled as a combination of cubic voxels as a matter of simplicity. After reconstruction, images are routinely filtered to reduce statistical noise at the cost of spatial resolution degradation. An alternative to produce lower noise during reconstruction is to model the image space with spherical basis functions. These basis functions overlap in space producing a significantly large number of non-zero elements in the system response matrix (SRM) to store, which additionally leads to long reconstruction times. These two problems are partly overcome by exploiting spherical symmetries, although computation time is still slower compared to non-overlapping basis functions. In this work, we have implemented the reconstruction algorithm using Graphical Processing Unit (GPU) technology for speed and a precomputed Monte-Carlo-calculated SRM for accuracy. The reconstruction time achieved using spherical basis functions on a GPU was 4.3 times faster than the Central Processing Unit (CPU) and 2.5 times faster than a CPU-multi-core parallel implementation using eight cores. Overwriting hazards are minimized by combining a random line of response ordering and constrained atomic writing. Small differences in image quality were observed between implementations.

A comparison of a Monte Carlo-based detection probability matrix with analytical probability matrix for small animal PET scanners

Positron Emission Tomography (PET) offers the possibility to quantitatively measure the radiotracer distribution in tissues. In order to obtain images of these tissues, the detection probability matrix (DPM) must be accurately determined. Usually, DPM is analytically calculated. However, this approach does not take into account the whole probabilistic interactions of the photons. On the other hand, Monte Carlo simulations (MC) are more accurate to calculate the DPM as they selectively consider diverse photon interactions. In this work, MC DPM (MCDPM) and analytically calculated DPM (ACDPM) were compared in terms of image quality. The results showed that the images obtained from the MCDPM were qualitatively better resolved and provided a significant improvement of the signal-to-noise ratio (SNR). The MCDPM yielded to an increase of up to 40% in SNR and up to 25% in contrast in comparison with ACDPM. On the other hands, MCDPM enhanced the counts distribution by more than 12% with respect to ACDPM.

Online detector response calculations for high-resolution PET image reconstruction

Positron emission tomography systems are best described by a linear shift-varying model. However, image reconstruction often assumes simplified shift-invariant models to the detriment of image quality and quantitative accuracy. We investigated a shift-varying model of the geometrical system response based on an analytical formulation. The model was incorporated within a list-mode, fully 3D iterative reconstruction process in which the system response coefficients are calculated online on a graphics processing unit (GPU). The implementation requires less than 512 Mb of GPU memory and can process two million events per minute (forward and backprojection). For small detector volume elements, the analytical model compared well to reference calculations. Images reconstructed with the shift-varying model achieved higher quality and quantitative accuracy than those that used a simpler shift-invariant model. For an 8 mm sphere in a warm background, the contrast recovery was 95.8% for the shift-varying model versus 85.9% for the shift-invariant model. In addition, the spatial resolution was more uniform across the field-of-view: for an array of 1.75 mm hot spheres in air, the variation in reconstructed sphere size was 0.5 mm RMS for the shift-invariant model, compared to 0.07 mm RMS for the shift-varying model.

Design and performance evaluation of a coplanar multimodality scanner for rodent imaging

This work reports on the development and performance evaluation of the VrPET/CT, a new multimodality scanner with coplanar geometry for in vivo rodent imaging. The scanner design is based on a partial-ring PET system and a small-animal CT assembled on a rotatory gantry without axial displacement between the geometric centers of both fields of view (FOV). We report on the PET system performance based on the NEMA NU-4 protocol; the performance characteristics of the CT component are not included herein. The accuracy of inter-modality alignment and the imaging capability of the whole system are also evaluated on phantom and animal studies. Tangential spatial resolution of PET images ranged between 1.56 mm at the center of the FOV and 2.46 at a radial offset of 3.5 cm. The radial resolution varies from 1.48 mm to 1.88 mm, and the axial resolution from 2.34 mm to 3.38 mm for the same positions. The energy resolution was 16.5% on average for the entire system. The absolute coincidence sensitivity is 2.2% for a 100-700 keV energy window with a 3.8 ns coincident window. The scatter fraction values for the same settings were 11.45% for a mouse-sized phantom and 23.26% for a rat-sized phantom. The peak noise equivalent count rates were also evaluated for those phantoms obtaining 70.8 kcps at 0.66 MBq/cc and 31.5 kcps at 0.11 MBq/cc, respectively. The accuracy of inter-modality alignment is below half the PET resolution, and the image quality of biological specimens agrees with measured performance parameters. The assessment presented in this study shows that the VrPET/CT system is a good performance small-animal imager, while the cost derived from a partial ring detection system is substantially reduced as compared with a full-ring PET tomograph.

Kinetic modelling in small animal imaging with PET

Small animal imaging with positron emission tomography has undergone a major evolution. This has been driven by technical improvements and the development of dedicated PET camera's for small animals. The focus has shifted from detection of tracer uptake and visualization of the tracer distribution towards the quantification of the physiological parameters necessary to use this technique for kinetic modelling of tracers. At the moment there are still several issues which need further research and evaluation before we can fully employ the possibilities of PET as an in-vivo measurement of underlying molecular biology. These issues relate to improved quantification of measurements, improved image reconstruction and processing, and the use of blood plasma data in combination with kinetic models. Besides the more technical issues, there are two more issues which need further clarification: the effect of the anaesthesia, and the effect of radiation on the experiment itself. In this review, we will give an overview of how the technique can be used but we will also discuss the issues mentioned above. The focus will be on the three major parts of the imaging procedure: acquisition, reconstruction of images, and kinetic modelling of the data.

PeneloPET, a Monte Carlo PET simulation tool based on PENELOPE: features and validation

Monte Carlo simulations play an important role in positron emission tomography (PET) imaging, as an essential tool for the research and development of new scanners and for advanced image reconstruction. PeneloPET, a PET-dedicated Monte Carlo tool, is presented and validated in this work. PeneloPET is based on PENELOPE, a Monte Carlo code for the simulation of the transport in matter of electrons, positrons and photons, with energies from a few hundred eV to 1 GeV. PENELOPE is robust, fast and very accurate, but it may be unfriendly to people not acquainted with the FORTRAN programming language. PeneloPET is an easy-to-use application which allows comprehensive simulations of PET systems within PENELOPE. Complex and realistic simulations can be set by modifying a few simple input text files. Different levels of output data are available for analysis, from sinogram and lines-of-response (LORs) histogramming to fully detailed list mode. These data can be further exploited with the preferred programming language, including ROOT. PeneloPET simulates PET systems based on crystal array blocks coupled to photodetectors and allows the user to define radioactive sources, detectors, shielding and other parts of the scanner. The acquisition chain is simulated in high level detail; for instance, the electronic processing can include pile-up rejection mechanisms and time stamping of events, if desired. This paper describes PeneloPET and shows the results of extensive validations and comparisons of simulations against real measurements from commercial acquisition systems. PeneloPET is being extensively employed to improve the image quality of commercial PET systems and for the development of new ones.

Fast, accurate and shift-varying line projections for iterative reconstruction using the GPU

List-mode processing provides an efficient way to deal with sparse projections in iterative image reconstruction for emission tomography. An issue often reported is the tremendous amount of computation required by such algorithm. Each recorded event requires several back- and forward line projections. We investigated the use of the programmable graphics processing unit (GPU) to accelerate the line-projection operations and implement fully-3D list-mode ordered-subsets expectation-maximization for positron emission tomography (PET). We designed a reconstruction approach that incorporates resolution kernels, which model the spatially-varying physical processes associated with photon emission, transport and detection. Our development is particularly suitable for applications where the projection data is sparse, such as high-resolution, dynamic, and time-of-flight PET reconstruction. The GPU approach runs more than 50 times faster than an equivalent CPU implementation while image quality and accuracy are virtually identical. This paper describes in details how the GPU can be used to accelerate the line projection operations, even when the lines-of-response have arbitrary endpoint locations and shift-varying resolution kernels are used. A quantitative evaluation is included to validate the correctness of this new approach.