3D conditional generative adversarial networks for high-quality PET image estimation at low dose.

Positron emission tomography (PET) is a widely used imaging modality, providing insight into both the biochemical and physiological processes of human body. Usually, a full dose radioactive tracer is required to obtain high-quality PET images for clinical needs. This inevitably raises concerns about potential health hazards. On the other hand, dose reduction may cause the increased noise in the reconstructed PET images, which impacts the image quality to a certain extent. In this paper, in order to reduce the radiation exposure while maintaining the high quality of PET images, we propose a novel method based on 3D conditional generative adversarial networks (3D c-GANs) to estimate the high-quality full-dose PET images from low-dose ones. Generative adversarial networks (GANs) include a generator network and a discriminator network which are trained simultaneously with the goal of one beating the other. Similar to GANs, in the proposed 3D c-GANs, we condition the model on an input low-dose PET image and generate a corresponding output full-dose PET image. Specifically, to render the same underlying information between the low-dose and full-dose PET images, a 3D U-net-like deep architecture which can combine hierarchical features by using skip connection is designed as the generator network to synthesize the full-dose image. In order to guarantee the synthesized PET image to be close to the real one, we take into account of the estimation error loss in addition to the discriminator feedback to train the generator network. Furthermore, a concatenated 3D c-GANs based progressive refinement scheme is also proposed to further improve the quality of estimated images. Validation was done on a real human brain dataset including both the normal subjects and the subjects diagnosed as mild cognitive impairment (MCI). Experimental results show that our proposed 3D c-GANs method outperforms the benchmark methods and achieves much better performance than the state-of-the-art methods in both qualitative and quantitative measures.

MR-based attenuation correction for PET/MRI neurological studies with continuous-valued attenuation coefficients for bone through a conversion from R2* to CT-Hounsfield units.

AIM: MR-based correction for photon attenuation in PET/MRI remains challenging, particularly for neurological applications requiring quantitation of data. Existing methods are either not sufficiently accurate or are limited by the computation time required. The goal of this study was to develop an MR-based attenuation correction method that accurately separates bone tissue from air and provides continuous-valued attenuation coefficients for bone. MATERIALS AND METHODS: PET/MRI and CT datasets were obtained from 98 subjects (mean age [±SD]: 66yrs [±9.8], 57 females) using an IRB-approved protocol and with informed consent. Subjects were injected with 352±29MBq of (18)F-Florbetapir tracer, and PET acquisitions were begun either immediately or 50min after injection. CT images of the head were acquired separately using a PET/CT system. Dual echo ultrashort echo-time (UTE) images and two-point Dixon images were acquired. Regions of air were segmented via a threshold of the voxel-wise multiplicative inverse of the UTE echo 1 image. Regions of bone were segmented via a threshold of the R2* image computed from the UTE echo 1 and UTE echo 2 images. Regions of fat and soft tissue were segmented using fat and water images decomposed from the Dixon images. Air, fat, and soft tissue were assigned linear attenuation coefficients (LACs) of 0, 0.092, and 0.1cm(-1), respectively. LACs for bone were derived from a regression analysis between corresponding R2* and CT values. PET images were reconstructed using the gold standard CT method and the proposed CAR-RiDR method. RESULTS: The RiDR segmentation method produces mean Dice coefficient±SD across subjects of 0.75±0.05 for bone and 0.60±0.08 for air. The CAR model for bone LACs greatly improves accuracy in estimating CT values (28.2%±3.0 mean error) compared to the use of a constant CT value (46.9%±5.8, p<10(-6)). Finally, the CAR-RiDR method provides a low whole-brain mean absolute percent-error (MAPE±SD) in PET reconstructions across subjects of 2.55%±0.86. Regional PET errors were also low and ranged from 0.88% to 3.79% in 24 brain ROIs. CONCLUSION: We propose an MR-based attenuation correction method (CAR-RiDR) for quantitative PET neurological imaging. The proposed method employs UTE and Dixon images and consists of two novel components: 1) accurate segmentation of air and bone using the inverse of the UTE1 image and the R2* image, respectively and 2) estimation of continuous LAC values for bone using a regression between R2* and CT-Hounsfield units. From our analysis, we conclude that the proposed method closely approaches (<3% error) the gold standard CT-scaled method in PET reconstruction accuracy.

Development of a new positron emission tomography tracer for targeting tumor angiogenesis: synthesis, small animal imaging, and radiation dosimetry.

Angiogenesis plays a key role in cancer progression and correlates with disease aggressiveness and poor clinical outcomes. Affinity ligands discovered by screening phage display random peptide libraries can be engineered to molecularly target tumor blood vessels for noninvasive imaging and early detection of tumor aggressiveness. In this study, we tested the ability of a phage-display-selected peptide sequence recognizing specifically bone marrow- derived pro-angiogenic tumor-homing cells, the QFP-peptide, radiolabeled with 64Cu radioisotope to selectively image tumor vasculature in vivo by positron emission tomography (PET). To prepare the targeted PET tracer we modified QFP-phage with the DOTA chelator and radiolabeled the purified QFP-phage-DOTA intermediate with 64Cu to obtain QFP-targeted radioconjugate with high radiopharmaceutical yield and specific activity. We evaluated the new PET tracer in vivo in a subcutaneous (s.c.) Lewis lung carcinoma (LLC) mouse model and conducted tissue distribution, small animal PET/CT imaging study, autoradiography, histology, fluorescence imaging, and dosimetry assessments. The results from this study show that, in the context of the s.c. LLC immunocompetent mouse model, the QFP-tracer can target tumor blood vessels selectively. However, further optimization of the biodistribution and dosimetry profile of the tracer is necessary to ensure efficient radiopharmaceutical applications enabled by the biological specificity of the QFP-peptide.

Striatal dopamine in anhedonia: A simultaneous [11C]raclopride positron emission tomography and functional magnetic resonance imaging investigation.

BACKGROUND: Anhedonia is hypothesized to be associated with blunted mesocorticolimbic dopamine (DA) functioning in samples with major depressive disorder. The purpose of this study was to examine linkages between striatal DA, reward circuitry functioning, anhedonia, and, in an exploratory fashion, self-reported stress, in a transdiagnostic anhedonic sample. METHODS: Participants with (n = 25) and without (n = 12) clinically impairing anhedonia completed a reward-processing task during simultaneous positron emission tomography and magnetic resonance (PET-MR) imaging with [11C]raclopride, a DA D2/D3 receptor antagonist that selectively binds to striatal DA receptors. RESULTS: Relative to controls, the anhedonia group exhibited decreased task-related DA release in the left putamen, caudate, and nucleus accumbens and right putamen and pallidum. There were no group differences in task-related brain activation (fMRI) during reward processing after correcting for multiple comparisons. General functional connectivity (GFC) findings revealed blunted fMRI connectivity between PET-derived striatal seeds and target regions in the anhedonia group. Associations were identified between anhedonia severity and the magnitude of task-related DA release to rewards in the left putamen, but not mesocorticolimbic GFC. CONCLUSIONS: Results provide evidence for reduced striatal DA functioning during reward processing and blunted mesocorticolimbic network functional connectivity in a transdiagnostic sample with clinically significant anhedonia.

Development of a surrogate biomodel for the investigation of clubfoot bracing.

Congenital talipes equinovarus (clubfoot) is a complex deformity of the lower extremity and foot occurring in 1/1000 live births. Regardless of treatment, whether conservative or surgical, clubfoot has a stubborn tendency to relapse, thus requiring postcorrection bracing. However, to date, there are no investigations specifically focused on clubfoot bracing from a bioengineering perspective. This study applied engineering principles to clubfoot bracing through construction of a surrogate biomodel. The surrogate was developed to represent an average 5-year-old human subject capable of biomechanical characteristics including joint articulation and kinematics. The components include skeleton, articulating joints, muscle-tendon systems, and ligaments. A protocol was developed to measure muscle-tendon tension in resting and braced positions of the surrogate. Measurement error ranged from 1% to 6% and was considered variance due to brace and investigator. In conclusion, this study shows that surrogate biomodeling is an accurate and repeatable method to investigate clubfoot bracing. The methodology is an effective means to evaluate wide ranging brace options and can be used to assist in future brace development and the tuning of brace parameters. Such patient-specific brace tuning may also lead to advanced braces that increase compliance.

Efficient In Vivo Selection of a Novel Tumor-Associated Peptide from a Phage Display Library.

We developed a screening procedure to identify ligands from a phage display random peptide library that are selective for circulating bone marrow derived cells homing to angiogenic tumors. Panning the library on blood outgrowth endothelial cell suspension in vitro followed by in vivo selection based on homing of bone marrow-bound phage to angiogenic tumors, yielded the peptide QFPPKLTNNSML. Upon intravenous injection phage displaying this peptide homed to Lewis lung carcinoma (LLC) tumors in vivo whereas control phage did not localize to tumor tissue. Phage carrying the QFPPKLTNNSML peptide labeled with 64Cu radionuclide when administered intravenously into a tumor bearing mouse was detected noninvasively with positron emission tomography (PET) around the tumor. These proof-of-principle experiments demonstrate the ability of the QFPPKLTNNSML peptide to deliver payload (radiolabeled phage conjugates) in vivo to sites of ongoing angiogenesis and point to its potential clinical utility in a variety of physiologic and pathologic processes where neovascular growth is a critical component.

Design and characterization of a spatially distributed multibeam field emission x-ray source for stationary digital breast tomosynthesis.

Digital breast tomosynthesis (DBT) is a limited angle computed tomography technique that can distinguish tumors from its overlying breast tissues and has potentials for detection of cancers at a smaller size and earlier stage. Current prototype DBT scanners are based on the regular full-field digital mammography systems and require partial isocentric motion of an x-ray tube over certain angular range to record the projection views. This prolongs the scanning time and, in turn, degrades the imaging quality due to motion blur. To mitigate the above limitations, the concept of a stationary DBT (s-DBT) scanner has been recently proposed based on the newly developed spatially distributed multibeam field emission x-ray (MBFEX) source technique using the carbon nanotube. The purpose of this article is to evaluate the performance of the 25-beam MBFEX source array that has been designed and fabricated for the s-DBT system. The s-DBT system records all the projection images by electronically activating the multiple x-ray beams from different viewing angles without any mechanical motion. The configuration of the MBFEX source is close to the published values from the Siemens Mammomat system. The key issues including the x-ray flux, focal spot size, spatial resolution, scanning time, beam-to-beam consistency, and reliability are evaluated using the standard procedures. In this article, the authors describe the design and performance of a distributed x-ray source array specifically designed for the s-DBT system. They evaluate the emission current, current variation, lifetime, and focal spot sizes of the source array. An emission current of up to 18 mA was obtained at 0.5 x 0.3 mm effective focal spot size. The experimentally measured focal spot sizes are comparable to that of a typical commercial mammography tube without motion blurring. Trade-off between the system spatial resolution, x-ray flux, and scanning time are also discussed. Projection images of a breast phantom were collected using the x-ray source array from 25 different viewing angles without motion. These preliminary results demonstrate the feasibility of the proposed s-DBT scanner. The technology has the potential to increase the resolution and reduce the imaging time for DBT. With the present design of 25 views, they demonstrated experimentally the feasibility of achieving 11 s scanning time at full detector resolution with 0.5 x 0.3 mm source resolution without motion blur. The flexibility in configuration of the x-ray source array will also allow system designers to consider imaging geometries that are difficult to achieve with the conventional single-source rotating approach.

Improved Dynamic Cardiac Phantom Based on 4D NURBS and Tagged MRI.

We previously developed a realistic phantom for the cardiac motion for use in medical imaging research. The phantom was based upon a gated magnetic resonance imaging (MRI) cardiac study and using 4D non-uniform rational b-splines (NURBS). Using the gated MRI study as the basis for the cardiac model had its limitations. From the MRI images, the change in the size and geometry of the heart structures could be obtained, but without markers to track the movement of points on or within the myocardium, no explicit time correspondence could be established for the structures. Also, only the inner and outer surfaces of the myocardium could be modeled. We enhance this phantom of the beating heart using 4D tagged MRI data. We utilize NURBS surfaces to analyze the full 3D motion of the heart from the tagged data. From this analysis, time-dependent 3D NURBS surfaces were created for the right (RV) and left ventricles (LV). Models for the atria were developed separately since the tagged data only covered the ventricles. A 4D NURBS surface was fit to the 3D surfaces of the heart creating time-continuous 4D NURBS models. Multiple 4D surfaces were created for the left ventricle (LV) spanning its entire volume. The multiple surfaces for the LV were spline-interpolated about an additional dimension, thickness, creating a 4D NURBS solid model for the LV with the ability to represent the motion of any point within the volume of the LV myocardium at any time during the cardiac cycle. Our analysis of the tagged data was found to produce accurate models for the RV and LV at each time frame. In a comparison with segmented structures from the tagged dataset, LV and RV surface predictions were found to vary by a maximum of 1.5 mm's and 3.4 mm's respectively. The errors can be attributed to the tag spacing in the data (7.97 mm's). The new cardiac model was incorporated into the 4D NURBS-based Cardiac-Torso (NCAT) phantom widely used in imaging research. With its enhanced abilities, the model will provide a useful tool in the study of cardiac imaging and the effects of cardiac motion in medical images.

3D Auto-Context-Based Locality Adaptive Multi-Modality GANs for PET Synthesis.

Positron emission tomography (PET) has been substantially used recently. To minimize the potential health risk caused by the tracer radiation inherent to PET scans, it is of great interest to synthesize the high-quality PET image from the low-dose one to reduce the radiation exposure. In this paper, we propose a 3D auto-context-based locality adaptive multi-modality generative adversarial networks model (LA-GANs) to synthesize the high-quality FDG PET image from the low-dose one with the accompanying MRI images that provide anatomical information. Our work has four contributions. First, different from the traditional methods that treat each image modality as an input channel and apply the same kernel to convolve the whole image, we argue that the contributions of different modalities could vary at different image locations, and therefore a unified kernel for a whole image is not optimal. To address this issue, we propose a locality adaptive strategy for multi-modality fusion. Second, we utilize 1 ×1 ×1 kernel to learn this locality adaptive fusion so that the number of additional parameters incurred by our method is kept minimum. Third, the proposed locality adaptive fusion mechanism is learned jointly with the PET image synthesis in a 3D conditional GANs model, which generates high-quality PET images by employing large-sized image patches and hierarchical features. Fourth, we apply the auto-context strategy to our scheme and propose an auto-context LA-GANs model to further refine the quality of synthesized images. Experimental results show that our method outperforms the traditional multi-modality fusion methods used in deep networks, as well as the state-of-the-art PET estimation approaches.

Locality Adaptive Multi-modality GANs for High-Quality PET Image Synthesis.

Positron emission topography (PET) has been substantially used in recent years. To minimize the potential health risks caused by the tracer radiation inherent to PET scans, it is of great interest to synthesize the high-quality full-dose PET image from the low-dose one to reduce the radiation exposure while maintaining the image quality. In this paper, we propose a locality adaptive multi-modality generative adversarial networks model (LA-GANs) to synthesize the full-dose PET image from both the low-dose one and the accompanying T1-weighted MRI to incorporate anatomical information for better PET image synthesis. This paper has the following contributions. First, we propose a new mechanism to fuse multi-modality information in deep neural networks. Different from the traditional methods that treat each image modality as an input channel and apply the same kernel to convolute the whole image, we argue that the contributions of different modalities could vary at different image locations, and therefore a unified kernel for a whole image is not appropriate. To address this issue, we propose a method that is locality adaptive for multimodality fusion. Second, to learn this locality adaptive fusion, we utilize 1 × 1 × 1 kernel so that the number of additional parameters incurred by our method is kept minimum. This also naturally produces a fused image which acts as a pseudo input for the subsequent learning stages. Third, the proposed locality adaptive fusion mechanism is learned jointly with the PET image synthesis in an end-to-end trained 3D conditional GANs model developed by us. Our 3D GANs model generates high quality PET images by employing large-sized image patches and hierarchical features. Experimental results show that our method outperforms the traditional multi-modality fusion methods used in deep networks, as well as the state-of-the-art PET estimation approaches.

Magnetic Resonance-Derived Improvements in PET Imaging.

Simultaneous PET-MR imaging improves deficiencies in PET images. The primary areas in which magnetic resonance (MR) has been applied to guide PET results are in correction for patient motion and in improving the effects of PET resolution and partial volume averaging. MR-guided motion correction of PET has been applied to respiratory, cardiac, and gross body movements and shown to improve lesion detectability and contrast. Partial volume correction or resolution improvement of PET governed by MR imaging anatomic information improves visualization of structures and quantitative accuracy. Evaluation in clinical applications is needed to determine their true impacts.

Semisupervised Tripled Dictionary Learning for Standard-Dose PET Image Prediction Using Low-Dose PET and Multimodal MRI.

OBJECTIVE: To obtain high-quality positron emission tomography (PET) image with low-dose tracer injection, this study attempts to predict the standard-dose PET (S-PET) image from both its low-dose PET (L-PET) counterpart and corresponding magnetic resonance imaging (MRI). METHODS: It was achieved by patch-based sparse representation (SR), using the training samples with a complete set of MRI, L-PET and S-PET modalities for dictionary construction. However, the number of training samples with complete modalities is often limited. In practice, many samples generally have incomplete modalities (i.e., with one or two missing modalities) that thus cannot be used in the prediction process. In light of this, we develop a semisupervised tripled dictionary learning (SSTDL) method for S-PET image prediction, which can utilize not only the samples with complete modalities (called complete samples) but also the samples with incomplete modalities (called incomplete samples), to take advantage of the large number of available training samples and thus further improve the prediction performance. RESULTS: Validation was done on a real human brain dataset consisting of 18 subjects, and the results show that our method is superior to the SR and other baseline methods. CONCLUSION: This paper proposed a new S-PET prediction method, which can significantly improve the PET image quality with low-dose injection. SIGNIFICANCE: The proposed method is favorable in clinical application since it can decrease the potential radiation risk for patients.

Multi-Level Canonical Correlation Analysis for Standard-Dose PET Image Estimation.

Positron emission tomography (PET) images are widely used in many clinical applications, such as tumor detection and brain disorder diagnosis. To obtain PET images of diagnostic quality, a sufficient amount of radioactive tracer has to be injected into a living body, which will inevitably increase the risk of radiation exposure. On the other hand, if the tracer dose is considerably reduced, the quality of the resulting images would be significantly degraded. It is of great interest to estimate a standard-dose PET (S-PET) image from a low-dose one in order to reduce the risk of radiation exposure and preserve image quality. This may be achieved through mapping both S-PET and low-dose PET data into a common space and then performing patch-based sparse representation. However, a one-size-fits-all common space built from all training patches is unlikely to be optimal for each target S-PET patch, which limits the estimation accuracy. In this paper, we propose a data-driven multi-level canonical correlation analysis scheme to solve this problem. In particular, a subset of training data that is most useful in estimating a target S-PET patch is identified in each level, and then used in the next level to update common space and improve estimation. In addition, we also use multi-modal magnetic resonance images to help improve the estimation with complementary information. Validations on phantom and real human brain data sets show that our method effectively estimates S-PET images and well preserves critical clinical quantification measures, such as standard uptake value.

Data on biodistribution and radiation absorbed dose profile of a novel (64)Cu-labeled high affinity cell-specific peptide for positron emission tomography imaging of tumor vasculature.

New peptide-based diagnostic and therapeutic approaches hold promise for highly selective targeting of cancer leading to more precise and effective diagnostic and therapeutic modalities. An important feature of these approaches is to reach the tumor tissue while limiting or minimizing the dose to normal organs. In this context, efforts to design and engineer materials with optimal in vivo targeting and clearance properties are important. This Data In Brief article reports on biodistribution and radiation absorbed dose profile of a novel high affinity radiopeptide specific for bone marrow-derived tumor vasculature. Background information on the design, preparation, and in vivo characterization of this peptide-based targeted radiodiagnostic is described in the article "Synthesis and comparative evaluation of novel 64Cu-labeled high affinity cell-specific peptides for positron emission tomography of tumor vasculature" (Merrill et al., 2016) [1]. Here we report biodistribution measurements in mice and calculate the radiation absorbed doses to normal organs using a modified Medical Internal Radiation Dosimetry (MIRD) methodology that accounts for physical and geometric factors and cross-organ beta doses.

Synthesis and comparative evaluation of novel (64)Cu-labeled high affinity cell-specific peptides for positron emission tomography imaging of tumor vasculature.

Tumor angiogenesis, the formation of new tumor blood supply, has been recognized as a hallmark of cancer and represents an important target for clinical management of various angiogenesis-dependent solid tumors. Previously, by screening a bacteriophage peptide library we have discovered the FHT-peptide sequence that binds specifically to bone marrow-derived tumor vasculature with high affinity. Here in an effort to determine the potential of the FHT-peptide for in vivo positron emission tomography (PET) imaging of aggressive tumor vasculature we studied four FHT-derivatives: NOTA-FHT, NOTA-(FHT)2, NOTA-PEG-FHT, and NOTA-PEG-(FHT)2. These peptide analogs were synthesized, labeled with the PET radionuclide (64)Cu, and characterized side-by-side with small animal PET and computed tomography imaging (microPET/CT) at 1 h, 4 h, and 24 h post injection in a subcutaneous Lewis lung carcinoma (LLC) tumor model. Because of its excellent in vivo kinetic properties and high tumor-to-background ratio, the (64)Cu-NOTA-FHT radiopeptide was selected for more detailed evaluation. Blocking studies with excess of unlabeled peptide showed specific and peptide mediated (64)Cu-NOTA-FHT tumor uptake. Biodistribution experiments in the same tumor model confirmed microPET/CT imaging results. Human radiation absorbed dose extrapolated from rodent biodistribution of (64)Cu-NOTA-FHT revealed favorable dosimetry profile. The findings from this investigation warrant further development of (64)Cu-NOTA-FHT as a potential targeted diagnostic radiopharmaceutical for PET imaging of aggressive tumor vasculature.

Predicting standard-dose PET image from low-dose PET and multimodal MR images using mapping-based sparse representation.

Positron emission tomography (PET) has been widely used in clinical diagnosis for diseases and disorders. To obtain high-quality PET images requires a standard-dose radionuclide (tracer) injection into the human body, which inevitably increases risk of radiation exposure. One possible solution to this problem is to predict the standard-dose PET image from its low-dose counterpart and its corresponding multimodal magnetic resonance (MR) images. Inspired by the success of patch-based sparse representation (SR) in super-resolution image reconstruction, we propose a mapping-based SR (m-SR) framework for standard-dose PET image prediction. Compared with the conventional patch-based SR, our method uses a mapping strategy to ensure that the sparse coefficients, estimated from the multimodal MR images and low-dose PET image, can be applied directly to the prediction of standard-dose PET image. As the mapping between multimodal MR images (or low-dose PET image) and standard-dose PET images can be particularly complex, one step of mapping is often insufficient. To this end, an incremental refinement framework is therefore proposed. Specifically, the predicted standard-dose PET image is further mapped to the target standard-dose PET image, and then the SR is performed again to predict a new standard-dose PET image. This procedure can be repeated for prediction refinement of the iterations. Also, a patch selection based dictionary construction method is further used to speed up the prediction process. The proposed method is validated on a human brain dataset. The experimental results show that our method can outperform benchmark methods in both qualitative and quantitative measures.

Alternate Metabolic Programs Define Regional Variation of Relevant Biological Features in Renal Cell Carcinoma Progression.

PURPOSE: Clear cell renal cell carcinoma (ccRCC) has recently been redefined as a highly heterogeneous disease. In addition to genetic heterogeneity, the tumor displays risk variability for developing metastatic disease, therefore underscoring the urgent need for tissue-based prognostic strategies applicable to the clinical setting. We have recently employed the novel PET/magnetic resonance (MR) image modality to enrich our understanding of how tumor heterogeneity can relate to gene expression and tumor biology to assist in defining individualized treatment plans. EXPERIMENTAL DESIGN: ccRCC patients underwent PET/MR imaging, and these images subsequently used to identify areas of varied intensity for sampling. Samples from 8 patients were subjected to histologic, immunohistochemical, and microarray analysis. RESULTS: Tumor subsamples displayed a range of heterogeneity for common features of hypoxia-inducible factor expression and microvessel density, as well as for features closely linked to metabolic processes, such as GLUT1 and FBP1. In addition, gene signatures linked with disease risk (ccA and ccB) also demonstrated variable heterogeneity, with most tumors displaying a dominant panel of features across the sampled regions. Intriguingly, the ccA- and ccB-classified samples corresponded with metabolic features and functional imaging levels. These correlations further linked a variety of metabolic pathways (i.e., the pentose phosphate and mTOR pathways) with the more aggressive, and glucose avid ccB subtype. CONCLUSIONS: Higher tumor dependency on exogenous glucose accompanies the development of features associated with the poor risk ccB subgroup. Linking these panels of features may provide the opportunity to create functional maps to enable enhanced visualization of the heterogeneous biologic processes of an individual's disease. Clin Cancer Res; 22(12); 2950-9. ©2016 AACR.

An observer study methodology for evaluating detection of motion abnormalities in gated myocardial perfusion SPECT.

To address the task of detecting nonischemic motion abnormalities from animated displays of gated myocardial perfusion single photon emission computed tomography data, we performed an observer study to evaluate the difference in detection performance between gating to 8 and 16 frames. Images were created from the NCAT mathematical phantom with a realistic heart simulating hypokinetic motion in the left lateral wall. Realistic noise-free projection data were simulated for both normal and defective hearts to obtain 16 frames for the cardiac cycle. Poisson noise was then simulated for each frame to create 50 realizations of each heart, All datasets were processed in two ways: reconstructed as a 16-frame set, and collapsed to 8 frames and reconstructed. Ten observers viewed the cardiac images animated with a realistic real-time frame rate. Observers trained on 100 images and tested on 100 images, rating their confidence on the presence of a motion defect on a continuous scale. None of the observers showed a significant difference in performance between the two gating methods. The 95% confidence interval on the difference in areas under the ROC curve (Az8 - Az16) was -0.029-0.085. Our test did not find a significant difference in detection performance between 8-frame gating and 16-frame gating. We conclude that, for the task of detecting abnormal motion, increasing the number of gated frames from 8 to 16 offers no apparent advantage.

Prediction of standard-dose brain PET image by using MRI and low-dose brain [18F]FDG PET images.

PURPOSE: Positron emission tomography (PET) is a nuclear medical imaging technology that produces 3D images reflecting tissue metabolic activity in human body. PET has been widely used in various clinical applications, such as in diagnosis of brain disorders. High-quality PET images play an essential role in diagnosing brain diseases/disorders. In practice, in order to obtain high-quality PET images, a standard-dose radionuclide (tracer) needs to be used and injected into a living body. As a result, it will inevitably increase the patient's exposure to radiation. One solution to solve this problem is predicting standard-dose PET images using low-dose PET images. As yet, no previous studies with this approach have been reported. Accordingly, in this paper, the authors propose a regression forest based framework for predicting a standard-dose brain [(18)F]FDG PET image by using a low-dose brain [(18)F]FDG PET image and its corresponding magnetic resonance imaging (MRI) image. METHODS: The authors employ a regression forest for predicting the standard-dose brain [(18)F]FDG PET image by low-dose brain [(18)F]FDG PET and MRI images. Specifically, the proposed method consists of two main steps. First, based on the segmented brain tissues (i.e., cerebrospinal fluid, gray matter, and white matter) in the MRI image, the authors extract features for each patch in the brain image from both low-dose PET and MRI images to build tissue-specific models that can be used to initially predict standard-dose brain [(18)F]FDG PET images. Second, an iterative refinement strategy, via estimating the predicted image difference, is used to further improve the prediction accuracy. RESULTS: The authors evaluated their algorithm on a brain dataset, consisting of 11 subjects with MRI, low-dose PET, and standard-dose PET images, using leave-one-out cross-validations. The proposed algorithm gives promising results with well-estimated standard-dose brain [(18)F]FDG PET image and substantially enhanced image quality of low-dose brain [(18)F]FDG PET image. CONCLUSIONS: In this paper, the authors propose a framework to generate standard-dose brain [(18)F]FDG PET image using low-dose brain [(18)F]FDG PET and MRI images. Both the visual and quantitative results indicate that the standard-dose brain [(18)F]FDG PET can be well-predicted using MRI and low-dose brain [(18)F]FDG PET.

The ubiquitin ligase MuRF1 regulates PPARα activity in the heart by enhancing nuclear export via monoubiquitination.

The transcriptional regulation of peroxisome proliferator-activated receptor (PPAR) α by post-translational modification, such as ubiquitin, has not been described. We report here for the first time an ubiquitin ligase (muscle ring finger-1/MuRF1) that inhibits fatty acid oxidation by inhibiting PPARα, but not PPARß/δ or PPARγ in cardiomyocytes in vitro. Similarly, MuRF1 Tg+ hearts showed significant decreases in nuclear PPARα activity and acyl-carnitine intermediates, while MuRF1-/- hearts exhibited increased PPARα activity and acyl-carnitine intermediates. MuRF1 directly interacts with PPARα, mono-ubiquitinates it, and targets it for nuclear export to inhibit fatty acid oxidation in a proteasome independent manner. We then identified a previously undescribed nuclear export sequence in PPARα, along with three specific lysines (292, 310, 388) required for MuRF1's targeting of nuclear export. These studies identify the role of ubiquitination in regulating cardiac PPARα, including the ubiquitin ligase that may be responsible for this critical regulation of cardiac metabolism in heart failure.