3D Positron Emission Tomography Research Articles

Development of a new fully three-dimensional methodology for tumours delineation in functional images

Delineation of tumours in Positron Emission Tomography (PET) plays a crucial role in accurate diagnosis and radiotherapy treatment planning. In this context, it is of outmost importance to devise efficient and operator-independent segmentation algorithms capable of reconstructing the tumour three-dimensional (3D) shape. In previous work, we proposed a system for 3D tumour delineation on PET data (expressed in terms of Standardized Uptake Value - SUV), based on a two-step approach. Step 1 identified the slice enclosing the maximum SUV and generated a rough contour surrounding it. Such contour was then used to initialize step 2, where the 3D shape of the tumour was obtained by separately segmenting 2D PET slices, leveraging the slice-by-slice marching approach. Additionally, we combined active contours and machine learning components to improve performance. Despite its success, the slice marching approach poses unnecessary limitations that are naturally removed by performing the segmentation directly in 3D. In this paper, we migrate our system into 3D. In particular, the segmentation in step 2 is now performed by evolving an active surface directly in the 3D space. The key points of such an advancement are that it performs the shape reconstruction on the whole stack of slices simultaneously, naturally leveraging cross-slice information that could not be exploited before. Additionally, it does not require any specific stopping condition, as the active surface naturally reaches a stable topology once convergence is achieved.Performance of this fully 3D approach is evaluated on the same dataset discussed in our previous work, which comprises fifty PET scans of lung, head and neck, and brain tumours. The results have confirmed that a benefit is indeed achieved in practice for all investigated anatomical districts, both quantitatively, through a set of commonly used quality indicators (dice similarity coefficient >87.66%, Hausdorff distance < 1.48 voxel and Mahalanobis distance < 0.82 voxel), and qualitatively in terms of Likert score (>3 in 54% of the tumours).

Technical Note: A digital reference object representing Hoffman's 3D brain phantom for PET scanner simulations.

Physical and digital phantoms play a key role in the development and testing of nuclear medicine instrumentation and processing algorithms for clinical and research applications, including neuroimaging using positron emission tomography (PET). We have developed and tested a digital reference object (DRO) version of the original segmented magnetic resonance imaging (MRI) data used for the three-dimensional (3D) PET brain phantom developed by Hoffman et al., which is used as the basis of a commercially available physical test phantom. The DRO was constructed by subdividing the MRI image planes the original phantom was based on to create equal-thickness slices and re-labeling voxels. The digital data was then embedded in a PET Digital Imaging and Communications in Medicine format and tested for compliance. We then tested the DRO by comparing it to computed tomography (CT) images of the physical phantom summed to form composite slices with axial extent similar to the DRO, but with a factor of two better in-slice resolution. For composite slices, 91% of voxels were labeled in full agreement, 5% of the voxels were 50-75% accurate, and the remaining 4% of voxels had 25% or less agreement. This DRO can be used as an input for PET scanner simulation studies or for comparing simulations to measured Hoffman phantom images.

An Optimized Registration Method Based on Distribution Similarity and DVF Smoothness for 3D PET and CT Images

A fusion image combining both anatomical and functional information obtained by registering medical images of two different modalities, Positron Emission Tomography (PET) and Computed Tomography (CT), is of great significance for medical image analysis and diagnosis. Medical image registration relies on similarity measure which is low between PET/CT image voxels and therefore PET/CT registration is a challenging task. To address this issue, this paper presents an unsupervised end-to-end method, DenseRegNet, for deformable 3D PET/CT image registration. The method consists of two stages: (1) predicting 3D displacement vector field (DVF); and (2) registering 3D image. In the 3D DVF prediction stage, a two-level similarity measure together with a deformation regularization is proposed as loss function to optimize network training.In the image registration stage, a resampler and a spatial transformer are utilized to obtain the registration results. In this paper, 663 pairs of Uptake Value (SUV) and Hounsfield Unit (Hu) patches of 106 patients, 227 pairs of SUV and Hu patches of 35 patients and 259 pairs of SUV and Hu patches of 35 patients are randomly selected as training, validation and test set, respectively. Normalized cross correlation (NCC), intersection over union (IoU) of liver bounding box and euclidean distance (ED) on landmark points are used to evaluate the registration results. Experiment results show that the proposed method, DenseRegNet, achieves the best results in terms of liver bounding box IoU and ED, and the second highest value of NCC. For a trained model, given a new pair of PET/CT images, the registration result can be obtained with only one forward calculation within 10 seconds. Through qualitative and quantitative analyses, we demonstrate that, compared with other deep learning registration models, the proposed DenseRegNet achieves improved results in the challenging deformable PET/CT registration task.

Non-Rigid Multi-Modal 3D Medical Image Registration Based on Foveated Modality Independent Neighborhood Descriptor.

The non-rigid multi-modal three-dimensional (3D) medical image registration is highly challenging due to the difficulty in the construction of similarity measure and the solution of non-rigid transformation parameters. A novel structural representation based registration method is proposed to address these problems. Firstly, an improved modality independent neighborhood descriptor (MIND) that is based on the foveated nonlocal self-similarity is designed for the effective structural representations of 3D medical images to transform multi-modal image registration into mono-modal one. The sum of absolute differences between structural representations is computed as the similarity measure. Subsequently, the foveated MIND based spatial constraint is introduced into the Markov random field (MRF) optimization to reduce the number of transformation parameters and restrict the calculation of the energy function in the image region involving non-rigid deformation. Finally, the accurate and efficient 3D medical image registration is realized by minimizing the similarity measure based MRF energy function. Extensive experiments on 3D positron emission tomography (PET), computed tomography (CT), T1, T2, and (proton density) PD weighted magnetic resonance (MR) images with synthetic deformation demonstrate that the proposed method has higher computational efficiency and registration accuracy in terms of target registration error (TRE) than the registration methods that are based on the hybrid L-BFGS-B and cat swarm optimization (HLCSO), the sum of squared differences on entropy images, the MIND, and the self-similarity context (SSC) descriptor, except that it provides slightly bigger TRE than the HLCSO for CT-PET image registration. Experiments on real MR and ultrasound images with unknown deformation have also be done to demonstrate the practicality and superiority of the proposed method.

Low statistics positron activity reconstruction methods for proton therapy

Positron emission tomography (PET) is one of the most mature techniques for monitoring in ion beam therapy. PET allows to reconstruct the β + activity generated in the patient by the nuclear interaction of the ions. Taking advantage of the spatial correlation between positron emitters created along the ions path and the dose distribution, it is possible to perform a quality control of the treatment. Usually, to reconstruct the activity generated within the irradiated volume, standard 3D PET reconstruction techniques are implemented. In this work, we explore a new reconstruction method (Straight Forward Reconstruction) particularly useful for reconstructing activity distribution generated by mono-energetic pencil beams. The method was validated by measuring the produced activity distribution with the DoPET system. Irradiations performed with mono-energetic pencil beams on phantoms mimicking human tissues were used for this study. Both reconstruction methods reach an accuracy in the reconstruction of the activity distribution width of the order of 1.5 mm for 2 ⋅ 1 0 8 primaries. • PET is one of the most mature techniques for monitoring in ion beam therapy. • A new reconstruction method (Straight Forward Reconstruction) for PET is presented. • Activity distribution and accuracy by pencil beams irradiation are presented.

Radionuclide-fluorescence Reporter Gene Imaging to Track Tumor Progression in Rodent Tumor Models

Metastasis is responsible for most cancer deaths. Despite extensive research, the mechanistic understanding of the complex processes governing metastasis remains incomplete. In vivo models are paramount for metastasis research, but require refinement. Tracking spontaneous metastasis by non-invasive in vivo imaging is now possible, but remains challenging as it requires long-time observation and high sensitivity. We describe a longitudinal combined radionuclide and fluorescence whole-body in vivo imaging approach for tracking tumor progression and spontaneous metastasis. This reporter gene methodology employs the sodium iodide symporter (NIS) fused to a fluorescent protein (FP). Cancer cells are engineered to stably express NIS-FP followed by selection based on fluorescence-activated cell sorting. Corresponding tumor models are established in mice. NIS-FP expressing cancer cells are tracked non-invasively in vivo at the whole-body level by positron emission tomography (PET) using the NIS radiotracer [18F]BF4-. PET is currently the most sensitive in vivo imaging technology available at this scale and enables reliable and absolute quantification. Current methods either rely on large cohorts of animals that are euthanized for metastasis assessment at varying time points, or rely on barely quantifiable 2D imaging. The advantages of the described method are: (i) highly sensitive non-invasive in vivo 3D PET imaging and quantification, (ii) automated PET tracer production, (iii) a significant reduction in required animal numbers due to repeat imaging options, (iv) the acquisition of paired data from subsequent imaging sessions providing better statistical data, and (v) the intrinsic option for ex vivo confirmation of cancer cells in tissues by fluorescence microscopy or cytometry. In this protocol, we describe all steps required for routine NIS-FP-afforded non-invasive in vivo cancer cell tracking using PET/CT and ex vivo confirmation of in vivo results. This protocol has applications beyond cancer research whenever in vivo localization, expansion and long-time monitoring of a cell population is of interest.

Radionuclide-fluorescence Reporter Gene Imaging to Track Tumor Progression in Rodent Tumor Models.

Metastasis is responsible for most cancer deaths. Despite extensive research, the mechanistic understanding of the complex processes governing metastasis remains incomplete. In vivo models are paramount for metastasis research, but require refinement. Tracking spontaneous metastasis by non-invasive in vivo imaging is now possible, but remains challenging as it requires long-time observation and high sensitivity. We describe a longitudinal combined radionuclide and fluorescence whole-body in vivo imaging approach for tracking tumor progression and spontaneous metastasis. This reporter gene methodology employs the sodium iodide symporter (NIS) fused to a fluorescent protein (FP). Cancer cells are engineered to stably express NIS-FP followed by selection based on fluorescence-activated cell sorting. Corresponding tumor models are established in mice. NIS-FP expressing cancer cells are tracked non-invasively in vivo at the whole-body level by positron emission tomography (PET) using the NIS radiotracer [18F]BF4-. PET is currently the most sensitive in vivo imaging technology available at this scale and enables reliable and absolute quantification. Current methods either rely on large cohorts of animals that are euthanized for metastasis assessment at varying time points, or rely on barely quantifiable 2D imaging. The advantages of the described method are: (i) highly sensitive non-invasive in vivo 3D PET imaging and quantification, (ii) automated PET tracer production, (iii) a significant reduction in required animal numbers due to repeat imaging options, (iv) the acquisition of paired data from subsequent imaging sessions providing better statistical data, and (v) the intrinsic option for ex vivo confirmation of cancer cells in tissues by fluorescence microscopy or cytometry. In this protocol, we describe all steps required for routine NIS-FP-afforded non-invasive in vivo cancer cell tracking using PET/CT and ex vivo confirmation of in vivo results. This protocol has applications beyond cancer research whenever in vivo localization, expansion and long-time monitoring of a cell population is of interest.

Effects of a novel tungsten-impregnated rubber neck shield on the quality of cerebral images acquired using 15O-labeled gas.

The present study aimed to validate the effects of a novel tungsten-impregnated rubber neck shield on the quality of phantom and clinical 15O-labeled gas positron emission tomography (PET) images. Images were acquired in the presence or absence of a neck shield from a cylindrical phantom containing [15O]H2O (phantom study) and from three individuals using [15O]CO2, [15O]O2 and [15O]CO gas (clinical study). Data were acquired in three-dimensional (3D) mode using a Discovery PET/CT 710. Values for cerebral blood flow, cerebral blood volume, oxygen extraction fraction, and cerebral metabolic rate of oxygen with and without the neck shield were calculated from 15O-labeled gas images. Arterial radioactivity and count characteristics were evaluated in the phantom and clinical studies. The coefficient of variance (CV) for the phantom study and the standard deviation (SD) for functional images were also analyzed. The neck shield decreased the random count rates by 25-59% in the phantom and clinical studies. The noise equivalent count rate (NECR) increased by 44-66% in the phantom and clinical studies. Random count rates and NECR in [15O]CO2 images significantly differed with and without the neck shield. The improvement in visual and physical image quality with the neck shield was not observed in the phantom and clinical studies. The novel neck shield reduced random count rate and improved NECR in a 3D PET study using 15O-labeled gas. The image quality with the neck shield was similar to that without the neck shield.

Fully integrated 3D high-resolution multicontrast abdominal PET-MR with high scan efficiency.

To provide 3D multicontrast anatomical MR with high isotropic resolution and metabolic positron emission tomography (PET) images using a respiratory motion-compensated simultaneous PET-MR examination with high scan efficiency. Standard abdominal PET-MR examinations combine MR data obtained during multiple breath-holds with free-breathing PET acquisitions, limiting the achievable image resolution and potentially causing misalignment errors between breath-hold and free-breathing data. Here, a 3D free-breathing PET-MR acquisition is presented, yielding T1 and T2 -weighted MR images with an isotropic resolution of 1.5 mm3 . In addition, nonrigid respiratory motion information and respiratory-resolved attenuation-correction maps are obtained without an increase in scan time. Motion information is used in motion-compensated image reconstructions to improve MR and PET image quality while shortening scan times. The proposed approach was evaluated in 11 oncology patients and provided respiratory motion information with an accuracy of 1.3 ± 0.1 mm. Sharpness of anatomical features was increased by 19 ± 13% compared with the uncorrected MR images in a 54 ± 26% shorter scan time than a gated MR acquisition. The MR-based motion information improved uptake values (75 ± 94%) and resolution (16 ± 27%) of simultaneously acquired PET images. The proposed method provides motion-compensated 3D high-quality MR and PET images in a comprehensive and highly efficient examination. Magn Reson Med 79:900-911, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Neurotransmitters behind pain relief with transcranial magnetic stimulation - positron emission tomography evidence for release of endogenous opioids.

Repetitive transcranial magnetic stimulation (rTMS) at M1/S1 cortex has been shown to alleviate neuropathic pain. To investigate the possible neurobiological correlates of cortical neurostimulation for the pain relief. We studied the effects of M1/S1 rTMS on nociception, brain dopamine D2 and μ-opioid receptors using a randomized, sham-controlled, double-blinded crossover study design and 3D-positron emission tomography (PET). Ten healthy subjects underwent active and sham rTMS treatments to the right M1/S1 cortex with E-field navigated device. Dopamine D2 and μ-receptor availabilities were assessed with PET radiotracers [11 C]raclopride and [11 C]carfentanil after each rTMS treatment. Thermal quantitative sensory testing (QST), contact heat evoked potential (CHEP) and blink reflex (BR) recordings were performed between the PET scans. μ-Opioid receptor availability was lower after active than sham rTMS (P≤0.0001) suggested release of endogenous opioids in the right ventral striatum, medial orbitofrontal, prefrontal and anterior cingulate cortices, and left insula, superior temporal gyrus, dorsolateral prefrontal cortex and precentral gyrus. There were no differences in striatal dopamine D2 receptor availability between active and sham rTMS, consistent with lack of long-lasting measurable dopamine release. Active rTMS potentiated the dopamine-regulated habituation of the BR compared to sham (P=0.02). Thermal QST and CHEP remained unchanged after active rTMS. rTMS given to M1/S1 activates the endogenous opioid system in a wide brain network associated with processing of pain and other salient stimuli. Direct enhancement of top-down opioid-mediated inhibition may partly explain the clinical analgesic effects of rTMS. Neurobiological correlates of rTMS for the pain relief are unclear. rTMS on M1/S1 with 11 C-carfentanyl-PET activates endogenous opioids. Thermal and heat pain thresholds remain unchanged. rTMS induces top-down opioid-mediated inhibition but not change the sensory discrimination of painful stimuli.

Super-Resolution Reconstruction of 3D PET Images Using Two Respiratory-Phase Low-Dose CT Images

Positron emission tomography (PET) images usually suffer from incorrect quantification of the radioactive uptake of small lesions due to low spatial resolution. To improve the spatial resolution, we previously proposed super-resolution (SR) algorithms based on wobble scanning. The proposed algorithms, however, require mechanical motion of the patient bed or a system gantry for wobble scanning. In this paper, we propose a framework for SR reconstruction of 3D PET images based on the use of respiratory motion rather than wobble motion. As in the conventional protocol of PET imaging, gated list-mode PET data are acquired in a free breathing condition. In addition, we acquire two low-dose CT images in a breath-hold manner at exhale and inhale phases, without increasing the radiation burden to a patient. Using the two low-dose CT images, we estimate the 4D motion vector field (MVF) and correspondingly generate a virtual 4D CT image that are matched to the 4D PET image. The 3D CT images have much better spatial resolution than PET images and therefore the corresponding estimated 3D MVFs can be considered reliable for PET SR reconstruction. We then estimate space-variant point spread functions (PSFs) in the imaging field of view using a minimum number of PSFs obtained through Monte-Carlo simulations. Finally, SR reconstruction is performed by incorporating the estimated 3D MVFs and space-variant PSFs. In the SR reconstruction, to avoid the resolution degradation in the evenly spaced parallel-beam rebinning and to reduce the computational time on the graphics processing unit, we introduce a parallel-friendly spanned line of response reconstruction technique based on fan-beam reordering. The proposed framework is evaluated via Monte-Carlo simulations with the digital XCAT phantom and via experiments with several patient datasets including moving lung lesions. Both the simulation and experiment results show that the proposed framework provides much clearer organ boundaries as well as more accurate quantitative lesion information than the conventional methods, with a considerable reduction of computational time.

Impact of moving target on measurement accuracy in 3D and 4D PET imaging—a phantom study

PurposeThe purpose of this study was to evaluate the impact of tumor motion on maximum standardized uptake value (SUVmax) and metabolic tumor volume (MTV) measurements in both 3-dimensional and respiratory-correlated, 4-dimensional positron emission tomography (PET) imaging. We also evaluated the effect of implementing different attenuation correction methods in 4-dimensional PET image reconstruction on SUVmax and MTV. Methods and materialsAn anthropomorphic thorax phantom with a spherical ball as a surrogate for a tumor was used. Different types of motion were imposed on the ball to mimic a patient's breathing motion. Three-dimensional PET imaging of the phantom without tumor motion was performed and used as the reference. The ball was then set in motion with different breathing motion traces and imaged with both 3- and 4-dimensional PET methods. The clinical 4-dimensional PET imaging protocol was modified so that 3 different types of attenuation correction images were used for reconstructions: the same free-breathing computed tomography (CT) for all PET phases, the same average intensity projection CT for all PET phases, and 4-dimensional CT for phase-matched attenuation correction. Tumor SUVmax and MTV values that were measured from the moving phantom were compared with the reference values. ResultsSUVmax that was measured in 3-dimensional PET imaging was different from the reference value by 20.4% on average for the motions that were investigated; this difference decreased to 2.6% with 4-dimensional PET imaging. The measurement of MTV in 4-dimensional PET also showed a similar magnitude of reduction of deviation compared with 3-dimensional PET. Four-dimensional PET with use of phase-matched 4-dimensional CT for attenuation correction showed less variation in SUVmax and MTV among phases compared with 4-dimensional PET with free-breathing CT or average intensity projection CT for attenuation correction. ConclusionsFour-dimensional PET imaging reduces the impact of motion on measured SUVmax and MTV when compared with 3-dimensional PET imaging. Clinical 4-dimensional PET imaging protocols should consider phase-matched 4-dimensional CT imaging for attenuation correction to achieve more accurate measurements.

Respiratory motion compensation for simultaneous PET/MR based on highly undersampled MR data.

Positron emission tomography (PET) of the thorax region is impaired by respiratory patient motion. To account for motion, the authors propose a new method for PET/magnetic resonance (MR) respiratory motion compensation (MoCo), which uses highly undersampled MR data with acquisition times as short as 1 min/bed. The proposed PET/MR MoCo method (4D jMoCo PET) uses radial MR data to estimate the respiratory patient motion employing MR joint motion estimation and image reconstruction with temporal median filtering. Resulting motion vector fields are incorporated into the system matrix of the PET reconstruction. The proposed approach is evaluated for the thorax region utilizing a PET/MR simulation with 1 min MR acquisition time and simultaneous PET/MR measurements of six patients with MR acquisition times of 1 and 5 min and radial undersampling factors of 11.2 and 2.2, respectively. Reconstruction results are compared to 3D PET, 4D gated PET and a standard MoCo method (4D sMoCo PET), which performs iterative image reconstruction and motion estimation sequentially. Quantitative analysis comprises the parameters SUVmean, SUVmax, full width at half-maximum/lesion volume, contrast and signal-to-noise ratio. For simulated PET data, our quantitative analysis shows that the proposed 4D jMoCo PET approach with temporal filtering achieves the best quantification accuracy of all tested reconstruction methods with a mean absolute deviation of 2.3% when compared to the ground truth. For measured PET patient data, the mean absolute deviation of 4D jMoCo PET using a 1 min MR acquisition for motion estimation is 2.1% relative to the 5 min MR acquisition. This demonstrates a robust behavior even in case of strong undersampling at MR acquisition times as short as 1 min. In contrast, 4D sMoCo PET shows considerable reduction of quantification accuracy for the 1 min MR acquisition time. Relative to 3D PET, the proposed 4D jMoCo PET approach with temporal filtering yields an average increase of SUVmean, SUVmax, and contrast of 29.9% and 13.8% for simulated and measured PET data, respectively. Employing artifact-robust motion estimation enables PET/MR respiratory MoCo with MR acquisition times as short as 1 min/bed improving PET image quality and quantification accuracy.

Postreconstruction filtering of 3D PET images by using weighted higher-order singular value decomposition.

BackgroundPositron emission tomography (PET) always suffers from high levels of noise due to the constraints of the injected dose and acquisition time, especially in the studies of dynamic PET imaging. To improve the quality of PET image, several approaches have been introduced to suppress noise. However, traditional filters often blur the image edges, or erase small detail, or rely on multiple parameters. In order to solve such problems, nonlocal denoising methods have been adapted to denoise PET images.MethodsIn this paper, we propose to use the weighted higher-order singular value decomposition for PET image denoising. We first modeled the noise in the PET image as Poisson distribution. Then, we transformed the noise to an additive Gaussian noise by use of the anscombe root transformation. Finally, we denoised the transformed image using the proposed higher-order singular value decomposition (HOSVD)-based algorithms. The denoised results were compared with results from some general filters by performing physical phantom and mice studies.ResultsCompared to other commonly used filters, HOSVD-based denoising algorithms can preserve boundaries and quantitative accuracy better. The spatial resolution and the low activity features in PET image also can be preserved by use of HOSVD-based methods. Comparing with the standard HOSVD-based algorithm, the proposed weighted HOSVD algorithm can suppress the stair-step artifact, and the time-consumption is about half of that needed by the Wiener-augmented HOSVD algorithm.ConclusionsThe proposed weighted HOSVD denoising algorithm can suppress noise while better preserving of boundary and quantity in PET images.Electronic supplementary materialThe online version of this article (doi:10.1186/s12938-016-0221-y) contains supplementary material, which is available to authorized users.

Validation of a simplified scatter correction method for 3D brain PET with 15O.

ObjectivePositron emission tomography (PET) enables quantitative measurements of various biological functions. Accuracy in data acquisition and processing schemes is a prerequisite for this. The correction of scatter is especially important when a 3D PET scanner is used. The aim of this study was to validate the use of a simplified calculation-based scatter correction method for 15O studies in the brain.MethodsWe applied two scatter correction methods to the same 15O PET data acquired from patients with cerebrovascular disease (n = 10): a hybrid dual-energy-window scatter correction (reference method), and a deconvolution scatter correction (simplified method). The PET study included three sequential scans for 15O-CO, 15O-O2, and 15O-H2O, from which the following quantitative parameters were calculated, cerebral blood flow, cerebral blood volume, cerebral metabolic rate of oxygen, and oxygen extraction fraction.ResultsBoth scatter correction methods provided similar reconstruction images with almost identical image noise, although there were slightly greater differences in white-matter regions compared with gray matter regions. These differences were also greater for 15O-CO than for 15O-H2O and 15O-O2. Region of interest analysis of the quantitative parameters demonstrated that the differences were less than 10 % (except for cerebral blood volume in white-matter regions), and the agreement between the methods was excellent, with intraclass correlation coefficients above 0.95 for all the parameters.ConclusionsThe deconvolution scatter correction despite its simplified implementation provided similar results to the hybrid dual-energy-window scatter correction. We consider it suitable for application in a clinical 15O brain study using a 3D PET scanner.

Positron Emission Tomography: state of the art and future developments

Positron emission tomography (PET) plays a fundamental role in medical imaging, with a wide range of applications covering, among the others, oncology, neurology and cardiology. PET has undergone a steady technological evolution since its introduction in mid 20th century, from the development of 3D PET in the late 1980s, to the invention of PET/CT in the 1990s and more recently with the introduction of PET/MR scanners. The current research topics aiming to develop the next generation of PET scanners are summarized in this paper, focusing on the efforts to increase the sensitivity of the detectors, as long as improving their timing, spatial and energy resolutions, with the final goal of reducing the amount of radioactive dose received by the patients and the duration of the exams while improving at the same time the detectability of lesions.

Abstract 4213: Gold nanostar as theranostic probe for brain tumor sensitive PET-optical imaging and image-guided specific photothermal therapy

Abstract Malignant brain tumors are one of the most lethal cancer forms causing over 14,000 deaths each year. In spite of extensive research efforts over many decades, the median overall survival (OS) still remains at just 15 months for the common malignant giloma, glioblastoma multiforme (GBM). We have developed a multifunctional gold nanostar (GNS) probe that shows potential to improve cancer management by providing personalized cancer theranostics, a combination of diagnostic and therapeutic functions. The GNS nanoprobe has a tunable plasmon absorption peak in the near infrared (NIR) “tissue optical window” for deep-tissue penetration and tip-enhanced surface plasmon resonance, which makes the GNS as a superior nanoprobe for cancer detection using surface-enhanced Raman spectroscopy (SERS), two-photon photoluminescence (TPL) imaging and photothermal therapy with NIR light. We have also functionalized the GNS with iodine-124 radioisotopes for sensitive 3D positron emission tomography (PET) and achieved a 10-picomolar limit of detection (LOD). The GNS nanoprobe accumulates selectively in brain tumor due to the enhanced permeability and retention (EPR) effect, which is caused by defective endothelial tight junctions in blood vessels surrounding the tumor. On the contrary, the GNS nanoprobe has minimum uptake in normal brain tissue due to the intact blood-brain-barrier (BBB). The GNS uptake ratio of tumor-to-normal (T/N) brain tissue is measured to be up to 30:1 by using inductively coupled plasma mass spectrometry (ICP-MS). By combining the high detection sensitivity of PET imaging and specific accumulation of GNS in brain tumor, we have demonstrated the feasibility of 1-mm brain tumor detection with the I-124 labeled GNS nano radiotracer. The obtained T/N ratio is up to 10:1 with our GNS nanoprobe labeled with I-124, which is much better than that (typically less than 1.5:1) for traditional F-18 fluorodeoxyglucose (FDG) PET contrast agent due to high glucose uptake in normal brain tissue. Furthermore, we have utilized GNS's high TPL intensity to delineate brain tumor margin with excellent sensitivity and high spatial resolution (μm level). In addition to brain tumor imaging, we have successfully demonstrated photothermal ablation with a NIR laser taking advantage of GNS's superior photothermal conversion efficiency. We have also performed a 6-month toxicity study for the GNS nanoprobe and experiment results, including body weight monitoring, blood chemistry test, and H&amp;E histopathology examination, show no deleterious effect after infusion of GNS nanoprobe up to 100 mg/kg dose. In summary, the GNS nanoprobe, with limited toxicity and multifunctional capabilities, exhibits great promise to fulfill the unmet needs in brain tumor management in future translational medicine investigations. Citation Format: Yang Liu, Austin Carpenter, Hsiangkuo Yuan, Zhengyuan Zhou, Michael Zalutsky, Ganesan Vaidyanathan, Hai Yan, Tuan Vo-Dinh. Gold nanostar as theranostic probe for brain tumor sensitive PET-optical imaging and image-guided specific photothermal therapy. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 4213.

MR–Consistent Simultaneous Reconstruction of Attenuation and Activity for Non–TOF PET/MR

Attenuation correction (AC) is required for accurate quantification of the reconstructed activity distribution in positron emission tomography (PET). For simultaneous PET/magnetic resonance (MR), however, AC is challenging, since the MR images do not provide direct information on the attenuating properties of the underlying tissue. Standard MR–based AC does not account for the presence of bone and thus leads to an underestimation of the activity distribution. To improve quantification for non–time–of–flight PET/MR, we propose an algorithm which simultaneously reconstructs activity and attenuation distribution from the PET emission data using available MR images as anatomical prior information. The MR information is used to derive voxel–dependent expectations on the attenuation coefficients. The expectations are modeled using Gaussian–like probability functions. An iterative reconstruction scheme incorporating the prior information on the attenuation coefficients is used to update attenuation and activity distribution in an alternating manner. We tested and evaluated the proposed algorithm for simulated 3D PET data of the head and the pelvis region. Activity deviations were below 5% in soft tissue and lesions compared to the ground truth whereas standard MR–based AC resulted in activity underestimation values of up to 12%.

The impact of audiovisual biofeedback on 4D functional and anatomic imaging: Results of a lung cancer pilot study

Background and purposeThe impact of audiovisual (AV) biofeedback on four dimensional (4D) positron emission tomography (PET) and 4D computed tomography (CT) image quality was investigated in a prospective clinical trial (NCT01172041). Material and methods4D-PET and 4D-CT images of ten lung cancer patients were acquired with AV biofeedback (AV) and free breathing (FB). The 4D-PET images were analyzed for motion artifacts by comparing 4D to 3D PET for gross tumor volumes (GTVPET) and maximum standardized uptake values (SUVmax). The 4D-CT images were analyzed for artifacts by comparing normalized cross correlation-based scores (NCCS) and quantifying a visual assessment score (VAS). A Wilcoxon signed-ranks test was used for statistical testing. ResultsThe impact of AV biofeedback varied widely. Overall, the 3D to 4D decrease of GTVPET was 1.2±1.3cm3 with AV and 0.6±1.8cm3 for FB. The 4D-PET increase of SUVmax was 1.3±0.9 with AV and 1.3±0.8 for FB. The 4D-CT NCCS were 0.65±0.27 with AV and 0.60±0.32 for FB (p=0.08). The 4D-CT VAS was 0.0±2.7. ConclusionThis study demonstrated a high patient dependence on the use of AV biofeedback to reduce motion artifacts in 4D imaging. None of the hypotheses tested were statistically significant. Future development of AV biofeedback will focus on optimizing the human–computer interface and including patient training sessions for improved comprehension and compliance.

Reconstruction for 3D PET Based on Total Variation Constrained Direct Fourier Method.

This paper presents a total variation (TV) regularized reconstruction algorithm for 3D positron emission tomography (PET). The proposed method first employs the Fourier rebinning algorithm (FORE), rebinning the 3D data into a stack of ordinary 2D data sets as sinogram data. Then, the resulted 2D sinogram are ready to be reconstructed by conventional 2D reconstruction algorithms. Given the locally piece-wise constant nature of PET images, we introduce the total variation (TV) based reconstruction schemes. More specifically, we formulate the 2D PET reconstruction problem as an optimization problem, whose objective function consists of TV norm of the reconstructed image and the data fidelity term measuring the consistency between the reconstructed image and sinogram. To solve the resulting minimization problem, we apply an efficient methods called the Bregman operator splitting algorithm with variable step size (BOSVS). Experiments based on Monte Carlo simulated data and real data are conducted as validations. The experiment results show that the proposed method produces higher accuracy than conventional direct Fourier (DF) (bias in BOSVS is 70% of ones in DF, variance of BOSVS is 80% of ones in DF).