Abdominopelvic MR to CT registration using a synthetic CT intermediate.

Accurate coregistration of computed tomography (CT) and magnetic resonance (MR) imaging can provide clinically relevant and complementary information and can serve to facilitate multiple clinical tasks including surgical and radiation treatment planning, and generating a virtual Positron Emission Tomography (PET)/MR for the sites that do not have a PET/MR system available. Despite the long-standing interest in multimodality co-registration, a robust, routine clinical solution remains an unmet need. Part of the challenge may be the use of mutual information (MI) maximization and local phase difference (LPD) as similarity metrics, which have limited robustness, efficiency, and are difficult to optimize. Accordingly, we propose registering MR to CT by mapping the MR to a synthetic CT intermediate (sCT) and further using it in a sCT-CT deformable image registration (DIR) that minimizes the sum of squared differences. The resultant deformation field of a sCT-CT DIR is applied to the MRI to register it with the CT. Twenty-five sets of abdominopelvic imaging data are used for evaluation. The proposed method is compared to standard MI- and LPD-based methods, and the multimodality DIR provided by a state of the art, commercially available FDA-cleared clinical software package. The results are compared using global similarity metrics, Modified Hausdorff Distance, and Dice Similarity Index on six structures. Further, four physicians visually assessed and scored registered images for their registration accuracy. As evident from both quantitative and qualitative evaluation, the proposed method achieved registration accuracy superior to LPD- and MI-based methods and can refine the results of the commercial package DIR when using its results as a starting point. Supported by these, this manuscript concludes the proposed registration method is more robust, accurate, and efficient than the MI- and LPD-based methods.

Improving Bone Mineral Density Assessment Using Spectral Detector CT.

INTRODUCTION: Bone mineral density (BMD) analysis by Dual-Energy x-ray Absorptiometry (DXA) can have some false negatives due to overlapping structures in the projections. Spectral Detector CT (SDCT) can overcome these limitations by providing volumetric information. We investigated its performance for BMD assessment and compared it to DXA and phantomless volumetric bone mineral density (PLvBMD), the latter known to systematically underestimate BMD. DXA is the current standard for BMD assessment, while PLvBMD is an established alternative for opportunistic BMD analysis using CT. Similarly to PLvBMD, spectral data could allow BMD screening opportunistically, without additional phantom calibration. METHODOLOGY: Ten concentrations of dipotassium phosphate (K2HPO4) ranging from 0 to 600 mg/ml, in an acrylic phantom were scanned using SDCT in four different, clinically-relevant scan conditions. Images were processed to estimate the K2HPO4 concentrations. A model representing a human lumbar spine (European Spine Phantom) was scanned and used for calibration via linear regression analysis. After calibration, our method was retrospectively applied to abdominal SDCT scans of 20 patients for BMD assessment, who also had PLvBMD and DXA. Performance of PLvBMD, DXA and our SDCT method were compared by sensitivity, specificity, negative predictive value and positive predictive value for decreased BMD. RESULTS: There was excellent correlation (R2 >0.99, p < 0.01) between true and measured K2HPO4 concentrations for all scan conditions. Overall mean measurement error ranged from -11.5 ± 4.7 mg/ml (-2.8 ± 6.0%) to -12.3 ± 6.3 mg/ml (-4.8 ± 3.0%) depending on scan conditions. Using DXA as a reference standard, sensitivity/specificity for detecting decreased BMD in the scanned patients were 100%/73% using SDCT, 100%/40% using PLvBMD provided T-scores, and 90-100%/40-53% using PLvBMD hydroxyapatite density classifications, respectively. CONCLUSIONS: Our results show excellent sensitivity and high specificity of SDCT for detecting decreased BMD, demonstrating clinical feasibility. Further validation in prospective clinical trials will be required.

Comparative analysis for renal stereotactic body radiotherapy using Cyberknife, VMAT and proton therapy based treatment planning.

PURPOSE: We conducted this dosimetric analysis to evaluate the feasibility of a multi-center stereotactic body radiation therapy (SBRT) trial for renal cell carcinoma (RCC) using different SBRT platforms. MATERIALS/METHODS: The computed tomography (CT) simulation images of 10 patients with unilateral RCC previously treated on a Phase 1 trial at Institution 1 were anonymized and shared with Institution 2 after IRB approval. Treatment planning was generated through five different platforms aiming a total dose of 48 Gy in three fractions. These platforms included: Cyberknife and volumetric modulated arc therapy (VMAT) at institution 1, and Cyberknife, VMAT, and pencil beam scanning (PBS) Proton Therapy at institution 2. Dose constraints were based on the Phase 1 approved trial. RESULTS: Compared to Cyberknife, VMAT and PBS plans provided overall an equivalent or superior coverage to the target volume, while limiting dose to the remaining kidney, contralateral kidney, liver, spinal cord, and bowel. CONCLUSION: This dosimetric study supports the feasibility of a multi-center trial for renal SBRT using PBS, VMAT and Cyberknife.

SSC-EKE: Semi-Supervised Classification with Extensive Knowledge Exploitation.

We introduce a new, semi-supervised classification method that extensively exploits knowledge. The method has three steps. First, the manifold regularization mechanism, adapted from the Laplacian support vector machine (LapSVM), is adopted to mine the manifold structure embedded in all training data, especially in numerous label-unknown data. Meanwhile, by converting the labels into pairwise constraints, the pairwise constraint regularization formula (PCRF) is designed to compensate for the few but valuable labelled data. Second, by further combining the PCRF with the manifold regularization, the precise manifold and pairwise constraint jointly regularized formula (MPCJRF) is achieved. Third, by incorporating the MPCJRF into the framework of the conventional SVM, our approach, referred to as semi-supervised classification with extensive knowledge exploitation (SSC-EKE), is developed. The significance of our research is fourfold: 1) The MPCJRF is an underlying adjustment, with respect to the pairwise constraints, to the graph Laplacian enlisted for approximating the potential data manifold. This type of adjustment plays the correction role, as an unbiased estimation of the data manifold is difficult to obtain, whereas the pairwise constraints, converted from the given labels, have an overall high confidence level. 2) By transforming the values of the two terms in the MPCJRF such that they have the same range, with a trade-off factor varying within the invariant interval [0, 1), the appropriate impact of the pairwise constraints to the graph Laplacian can be self-adaptively determined. 3) The implication regarding extensive knowledge exploitation is embodied in SSC-EKE. That is, the labelled examples are used not only to control the empirical risk but also to constitute the MPCJRF. Moreover, all data, both labelled and unlabelled, are recruited for the model smoothness and manifold regularization. 4) The complete framework of SSC-EKE organically incorporates multiple theories, such as joint manifold and pairwise constraint-based regularization, smoothness in the reproducing kernel Hilbert space, empirical risk minimization, and spectral methods, which facilitates the preferable classification accuracy as well as the generalizability of SSC-EKE.

Outcomes and toxicities in patients treated with definitive focal therapy for primary prostate cancer: systematic review.

AIM: This systematic review summarizes the clinical data on focal therapy (FT) when used alone as definitive therapy for primary prostate cancer (PCa). METHODS: The protocol is detailed in the online PROSPERO database, registration No. CRD42014014765. Articles evaluating any form of FT alone as a definitive treatment for PCa in adult male patients were included. RESULTS: Of 10,419 identified articles, 10,401 were excluded, and thus leaving 18 for analysis. In total, 2288 patients were treated using seven modalities. The outcomes of FT in PCa seem to be similar to those observed with whole gland therapy and with fewer side effects. CONCLUSION: Further research, including prospective randomized trials, is warranted to elucidate the potential advantages of focal radiation techniques for treating PCa. Prospero Registration Number: CRD42014014765.

Knowledge-leveraged transfer fuzzy C-Means for texture image segmentation with self-adaptive cluster prototype matching.

We study a novel fuzzy clustering method to improve the segmentation performance on the target texture image by leveraging the knowledge from a prior texture image. Two knowledge transfer mechanisms, i.e. knowledge-leveraged prototype transfer (KL-PT) and knowledge-leveraged prototype matching (KL-PM) are first introduced as the bases. Applying them, the knowledge-leveraged transfer fuzzy C-means (KL-TFCM) method and its three-stage-interlinked framework, including knowledge extraction, knowledge matching, and knowledge utilization, are developed. There are two specific versions: KL-TFCM-c and KL-TFCM-f, i.e. the so-called crisp and flexible forms, which use the strategies of maximum matching degree and weighted sum, respectively. The significance of our work is fourfold: 1) Owing to the adjustability of referable degree between the source and target domains, KL-PT is capable of appropriately learning the insightful knowledge, i.e. the cluster prototypes, from the source domain; 2) KL-PM is able to self-adaptively determine the reasonable pairwise relationships of cluster prototypes between the source and target domains, even if the numbers of clusters differ in the two domains; 3) The joint action of KL-PM and KL-PT can effectively resolve the data inconsistency and heterogeneity between the source and target domains, e.g. the data distribution diversity and cluster number difference. Thus, using the three-stage-based knowledge transfer, the beneficial knowledge from the source domain can be extensively, self-adaptively leveraged in the target domain. As evidence of this, both KL-TFCM-c and KL-TFCM-f surpass many existing clustering methods in texture image segmentation; and 4) In the case of different cluster numbers between the source and target domains, KL-TFCM-f proves higher clustering effectiveness and segmentation performance than does KL-TFCM-c.

Cross-domain, soft-partition clustering with diversity measure and knowledge reference.

Conventional, soft-partition clustering approaches, such as fuzzy c-means (FCM), maximum entropy clustering (MEC) and fuzzy clustering by quadratic regularization (FC-QR), are usually incompetent in those situations where the data are quite insufficient or much polluted by underlying noise or outliers. In order to address this challenge, the quadratic weights and Gini-Simpson diversity based fuzzy clustering model (QWGSD-FC), is first proposed as a basis of our work. Based on QWGSD-FC and inspired by transfer learning, two types of cross-domain, soft-partition clustering frameworks and their corresponding algorithms, referred to as type-I/type-II knowledge-transfer-oriented c-means (TI-KT-CM and TII-KT-CM), are subsequently presented, respectively. The primary contributions of our work are four-fold: (1) The delicate QWGSD-FC model inherits the most merits of FCM, MEC and FC-QR. With the weight factors in the form of quadratic memberships, similar to FCM, it can more effectively calculate the total intra-cluster deviation than the linear form recruited in MEC and FC-QR. Meanwhile, via Gini-Simpson diversity index, like Shannon entropy in MEC, and equivalent to the quadratic regularization in FC-QR, QWGSD-FC is prone to achieving the unbiased probability assignments, (2) owing to the reference knowledge from the source domain, both TI-KT-CM and TII-KT-CM demonstrate high clustering effectiveness as well as strong parameter robustness in the target domain, (3) TI-KT-CM refers merely to the historical cluster centroids, whereas TII-KT-CM simultaneously uses the historical cluster centroids and their associated fuzzy memberships as the reference. This indicates that TII-KT-CM features more comprehensive knowledge learning capability than TI-KT-CM and TII-KT-CM consequently exhibits more perfect cross-domain clustering performance and (4) neither the historical cluster centroids nor the historical cluster centroid based fuzzy memberships involved in TI-KT-CM or TII-KT-CM can be inversely mapped into the raw data. This means that both TI-KT-CM and TII-KT-CM can work without disclosing the original data in the source domain, i.e. they are of good privacy protection for the source domain. In addition, the convergence analyses regarding both TI-KT-CM and TII-KT-CM are conducted in our research. The experimental studies thoroughly evaluated and demonstrated our contributions on both synthetic and real-life data scenarios.

Development of a selective novel fluorescent substrate for sodium-dependent transporters.

AIM: To synthesize, characterize, and validate 6FGA, a fluorescent glucose modified with a Cyanine5.5 at carbon-6 position, for probing the function of sodium-dependent glucose transporters, SGLT1 and SGLT2. MAIN METHODS: The synthesis of fluorescent glucose analogue was achieved through "click chemistry" of Cyanine5.5-alkyne and 6-azido-6-deoxy-d-glucose. Cell system studies were conducted to characterize the in vivo transport properties. KEY FINDINGS: Optical analyses revealed that 6FGA displayed similar spectral profiles to Cyanine5.5 in DMSO, allowing for concentration determination, thus supporting its utility in quantitative kinetic studies within biological assays. Uptake studies in cell system SGLT models, LLC-PK1 and HEK293 cells, exhibited concentration and time-dependent behavior, indicating saturation at specific concentrations and durations which are hallmarks of transported-mediated uptake. The results of cytotoxicity assays suggested cell viability at micromolar concentrations, enabling usage in assays for at least 1 h without significant toxicity. The dependence of 6FGA uptake on sodium, the co-transported cation, was demonstrated in LLC-PK1 and HEK293 cells. Fluorescence microscopy confirmed intracellular localization of 6FGA, particularly near the nucleus. Competition studies revealed that glucose tends to weakly reduce 6FGA uptake, although the effect did not achieve statistical significance. Assessments using standard SGLT and GLUT inhibitors highlighted 6FGA's sensitivity for probing SGLT-mediated transport. SIGNIFICANCE: 6FGA is a new fluorescent glucose analog offering advantages over existing probes due to its improved photophysical properties, greater sensitivity, enabling subcellular resolution and efficient tissue penetration in near-infrared imaging. 6FGA presents practicality and cost-effectiveness, making it a promising tool for nonradioactive, microplate-based assays at investigating SGLT-mediated glucose transport mechanisms.

Simultaneous magnetic resonance angiography and perfusion (MRAP) measurement: initial application in lower extremity skeletal muscle.

PURPOSE: To obtain a simultaneous 3D magnetic resonance angiography and perfusion (MRAP) using a single acquisition and to demonstrate MRAP in the lower extremities. A time-resolved contrast-enhanced exam was used in MRAP to simultaneously acquire a contrast-enhanced MR angiography (MRA) and dynamic contrast-enhanced (DCE) perfusion, which currently requires separate acquisitions and thus two contrast doses. MRAP can be used to assess large and small vessels in vascular pathologies such as peripheral arterial disease. MATERIALS AND METHODS: MRAP was performed on 10 volunteers following unilateral plantar flexion exercise (one leg exercised and one rested) on two separate days. Data were acquired after administration of a single dose of contrast agent using an optimized sampling strategy, parallel imaging, and partial-Fourier acquisition to obtain a high spatial resolution, 3D-MRAP frame every 4 seconds. Two radiologists assessed MRAs for image quality, a signal-to-noise ratio (SNR) analysis was performed, and pharmacokinetic modeling yielded perfusion (K(trans) ). RESULTS: MRA images had high SNR and radiologist-assessed diagnostic quality. Mean K(trans) ± standard error were 0.136 ± 0.009, 0.146 ± 0.012, and 0.191 ± 0.012 min(-1) in the resting tibialis anterior, gastrocnemius, and soleus, respectively, which significantly increased with exercise to 0.291 ± 0.018, 0.270 ± 0.019, and 0.338 ± 0.022 min(-1) . Bland-Altman analysis showed good repeatability. CONCLUSION: MRAP provides simultaneous high-resolution MRA and quantitative DCE exams to assess large and small vessels with a single contrast dose. Application in skeletal muscle shows quantitative, repeatable perfusion measurements, and the ability to measure physiological differences.

Using an Assumed Lung Mass Inaccurately Estimates the Lung Absorbed Dose in Patients Undergoing Hepatic 90Yttrium Radioembolization Therapy.

RATIONALE: Currently, the estimated absorbed radiation dose to the lung in 90Y radioembolization therapy is calculated using an assumed 1 kg lung mass for all patients. The aim of this study was to evaluate whether using a patient-specific lung mass measurement for each patient rather than a generic, assumed 1 kg lung mass would change the estimated lung absorbed dose. METHODS: A retrospective analysis was performed on 68 patients who had undergone 90Y radioembolization therapy at our institution. Individualized lung volumes were measured manually on CT scans for each patient, and these volumes were used to calculate personalized lung masses. The personalized lung masses were used to recalculate the estimated lung absorbed dose from the 90Y therapy, and this dose was compared to the estimated lung absorbed dose calculated using an assumed 1 kg lung mass. RESULTS: Patient-specific lung masses were significantly different from the generic 1 kg when compared individually for each patient (p < 0.0001). Median individualized lung mass was 0.71 (IQR: 0.59, 1.02) kg overall and was significantly different from the generic 1 kg lung mass for female patients [0.59 (0.50, 0.68) kg, (p < 0.0001)] but not for male patients [0.99 (0.71, 1.14) kg, (p = 0.24)]. Median estimated lung absorbed dose was 4.48 (2.38, 11.71) Gy using a patient-specific lung mass and 3.45 (1.81, 6.68) Gy when assuming a 1 kg lung mass for all patients. The estimated lung absorbed dose was significantly different using a patient-specific versus generic 1 kg lung mass when comparing the doses individually for each patient (p < 0.0001). The difference in the estimated lung absorbed dose between the patient-specific and generic 1 kg lung mass method was significant for female patients as a subgroup but not for male patients. CONCLUSIONS: The current method of assuming a 1 kg lung mass for all patients inaccurately estimates the lung absorbed dose in 90Y radioembolization therapy. Using patient-specific lung masses resulted in estimated lung absorbed doses that were significantly different from those calculated using an assumed 1 kg lung mass for all patients. A personalized dosimetry method that includes individualized lung masses is necessary and can warrant a 90Y dose reduction in some patients with lung masses smaller than 1 kg. LEVEL OF EVIDENCE: Level 3, Retrospective Study.

A new Michaelis-Menten-based kinetic model for transport and phosphorylation of glucose and its analogs in skeletal muscle.

PURPOSE: A new model is introduced that individually resolves the delivery, transport, and phosphorylation steps of metabolism of glucose and its analogs in skeletal muscle by interpreting dynamic positron emission tomography (PET) data. METHODS: The model uniquely utilizes information obtained from the competition between glucose and its radiolabeled analogs. Importantly, the model avoids use of a lumped constant which may depend on physiological state. Four basic physiologic quantities constitute our model parameters, including the fraction of total tissue space occupied by interstitial space (f(IS)), a flow-extraction product and interstitial (IS(g)) and intracellular (IC(g)) glucose concentrations. Using the values of these parameters, cellular influx (CI) and efflux (CE) of glucose, glucose phosphorylation rate (PR), and maximal transport (V(G)) and phosphorylation capacities (V(H)) can all be determined. Herein, the theoretical derivation of our model is addressed and characterizes its properties via simulation. Specifically, the model performance is evaluated by simulation of basal and euglycemic hyperinsulinemic (EH) conditions. RESULTS: In fitting the model-generated, synthetic data (including noise), mean estimates of all but IC(g) of the parameter values are within 5% of their values for both conditions. In addition, mean errors of CI, PR, and V(G) are less than 5% whereas those of VH and CE are not. CONCLUSIONS: It is concluded that under the conditions tested, the novel model can provide accurate parameter estimates and physiological quantities, except IC(g) and two quantities that are dependent on IC(g), namely CE and VH. However, the ability to estimate IC(g) seems to improve with increases in intracellular glucose concentrations as evidenced by comparing IC(g) estimates under basal vs EH conditions.

Treating Recurrent Brain Metastases Using GammaTile Brachytherapy: A Case Report and Dosimetric Modeling Method.

One of the treatment options for recurrent brain metastases is surgical resection combined with intracranial brachytherapy. GammaTile® (GT) (GT Medical Technologies, Tempe, Arizona) is a tile-shaped permanent brachytherapy device with cesium 131 (131Cs) seeds embedded within a collagen carrier. We report a case of treating a patient with recurrent brain metastases with GT and demonstrate a dosimetric modeling method.

Transforming UTE-mDixon MR Abdomen-Pelvis Images Into CT by Jointly Leveraging Prior Knowledge and Partial Supervision.

Computed tomography (CT) provides information for diagnosis, PET attenuation correction (AC), and radiation treatment planning (RTP). Disadvantages of CT include poor soft tissue contrast and exposure to ionizing radiation. While MRI can overcome these disadvantages, it lacks the photon absorption information needed for PET AC and RTP. Thus, an intelligent transformation from MR to CT, i.e., the MR-based synthetic CT generation, is of great interest as it would support PET/MR AC and MR-only RTP. Using an MR pulse sequence that combines ultra-short echo time (UTE) and modified Dixon (mDixon), we propose a novel method for synthetic CT generation jointly leveraging prior knowledge as well as partial supervision (SCT-PK-PS for short) on large-field-of-view images that span abdomen and pelvis. Two key machine learning techniques, i.e., the knowledge-leveraged transfer fuzzy c-means (KL-TFCM) and the Laplacian support vector machine (LapSVM), are used in SCT-PK-PS. The significance of our effort is threefold: 1) Using the prior knowledge-referenced KL-TFCM clustering, SCT-PK-PS is able to group the feature data of MR images into five initial clusters of fat, soft tissue, air, bone, and bone marrow. Via these initial partitions, clusters needing to be refined are observed and for each of them a few additionally labeled examples are given as the partial supervision for the subsequent semi-supervised classification using LapSVM; 2) Partial supervision is usually insufficient for conventional algorithms to learn the insightful classifier. Instead, exploiting not only the given supervision but also the manifold structure embedded primarily in numerous unlabeled data, LapSVM is capable of training multiple desired tissue-recognizers; 3) Benefiting from the joint use of KL-TFCM and LapSVM, and assisted by the edge detector filter based feature extraction, the proposed SCT-PK-PS method features good recognition accuracy of tissue types, which ultimately facilitates the good transformation from MR images to CT images of the abdomen-pelvis. Applying the method on twenty subjects' feature data of UTE-mDixon MR images, the average score of the mean absolute prediction deviation (MAPD) of all subjects is 140.72 ± 30.60 HU which is statistically significantly better than the 241.36 ± 21.79 HU obtained using the all-water method, the 262.77 ± 42.22 HU obtained using the four-cluster-partitioning (FCP, i.e., external-air, internal-air, fat, and soft tissue) method, and the 197.05 ± 76.53 HU obtained via the conventional SVM method. These results demonstrate the effectiveness of our method for the intelligent transformation from MR to CT on the body section of abdomen-pelvis.

Synthetic CT Generation of the Pelvis in Patients With Cervical Cancer: A Single Input Approach Using Generative Adversarial Network.

Multi-modality imaging constitutes a foundation of precision medicine, especially in oncology where reliable and rapid imaging techniques are needed in order to insure adequate diagnosis and treatment. In cervical cancer, precision oncology requires the acquisition of 18F-labeled 2-fluoro-2-deoxy-D-glucose (FDG) positron emission tomography (PET), magnetic resonance (MR), and computed tomography (CT) images. Thereafter, images are co-registered to derive electron density attributes required for FDG-PET attenuation correction and radiation therapy planning. Nevertheless, this traditional approach is subject to MR-CT registration defects, expands treatment expenses, and increases the patient's radiation exposure. To overcome these disadvantages, we propose a new framework for cross-modality image synthesis which we apply on MR-CT image translation for cervical cancer diagnosis and treatment. The framework is based on a conditional generative adversarial network (cGAN) and illustrates a novel tactic that addresses, simplistically but efficiently, the paradigm of vanishing gradient vs. feature extraction in deep learning. Its contributions are summarized as follows: 1) The approach -termed sU-cGAN-uses, for the first time, a shallow U-Net (sU-Net) with an encoder/decoder depth of 2 as generator; 2) sU-cGAN's input is the same MR sequence that is used for radiological diagnosis, i.e. T2-weighted, Turbo Spin Echo Single Shot (TSE-SSH) MR images; 3) Despite limited training data and a single input channel approach, sU-cGAN outperforms other state of the art deep learning methods and enables accurate synthetic CT (sCT) generation. In conclusion, the suggested framework should be studied further in the clinical settings. Moreover, the sU-Net model is worth exploring in other computer vision tasks.

Myocardial insulin resistance induced by high fat feeding in heart failure is associated with preserved contractile function.

Previous studies have reported that high fat feeding in mild to moderate heart failure (HF) results in the preservation of contractile function. Recent evidence has suggested that preventing the switch from fatty acid to glucose metabolism in HF may ameliorate dysfunction, and insulin resistance is one potential mechanism for regulating substrate utilization. This study was designed to determine whether peripheral and myocardial insulin resistance exists with HF and/or a high-fat diet and whether myocardial insulin signaling was altered accordingly. Rats underwent coronary artery ligation (HF) or sham surgery and were randomized to normal chow (NC; 14% kcal from fat) or a high-fat diet (SAT; 60% kcal from fat) for 8 wk. HF + SAT animals showed preserved systolic (+dP/dt and stroke work) and diastolic (-dP/dt and time constant of relaxation) function compared with HF + NC animals. Glucose tolerance tests revealed peripheral insulin resistance in sham + SAT, HF + NC, and HF + SAT animals compared with sham + NC animals. PET imaging confirmed myocardial insulin resistance only in HF + SAT animals, with an uptake ratio of 2.3 ± 0.3 versus 4.6 ± 0.7, 4.3 ± 0.4, and 4.2 ± 0.6 in sham + NC, sham + SAT, and HF + NC animals, respectively; the myocardial glucose utilization rate was similarly decreased in HF + SAT animals only. Western blot analysis of insulin signaling protein expression was indicative of cardiac insulin resistance in HF + SAT animals. Specifically, alterations in Akt and glycogen synthase kinase-3ß protein expression in HF + SAT animals compared with HF + NC animals may be involved in mediating myocardial insulin resistance. In conclusion, HF animals fed a high-saturated fat exhibited preserved myocardial contractile function, peripheral and myocardial insulin resistance, decreased myocardial glucose utilization rates, and alterations in cardiac insulin signaling. These results suggest that myocardial insulin resistance may serve a cardioprotective function with high fat feeding in mild to moderate HF.

A Novel Radial Basis Neural Network-Leveraged Fast Training Method for Identifying Organs in MR Images.

We propose a new method for fast organ classification and segmentation of abdominal magnetic resonance (MR) images. Magnetic resonance imaging (MRI) is a new type of high-tech imaging examination fashion in recent years. Recognition of specific target areas (organs) based on MR images is one of the key issues in computer-aided diagnosis of medical images. Artificial neural network technology has made significant progress in image processing based on the multimodal MR attributes of each pixel in MR images. However, with the generation of large-scale data, there are few studies on the rapid processing of large-scale MRI data. To address this deficiency, we present a fast radial basis function artificial neural network (Fast-RBF) algorithm. The importance of our efforts is as follows: (1) The proposed algorithm achieves fast processing of large-scale image data by introducing the ε-insensitive loss function, the structural risk term, and the core-set principle. We apply this algorithm to the identification of specific target areas in MR images. (2) For each abdominal MRI case, we use four MR sequences (fat, water, in-phase (IP), and opposed-phase (OP)) and the position coordinates (x, y) of each pixel as the input of the algorithm. We use three classifiers to identify the liver and kidneys in the MR images. Experiments show that the proposed method achieves a higher precision in the recognition of specific regions of medical images and has better adaptability in the case of large-scale datasets than the traditional RBF algorithm.

mDixon-Based Synthetic CT Generation for PET Attenuation Correction on Abdomen and Pelvis Jointly Using Transfer Fuzzy Clustering and Active Learning-Based Classification.

We propose a new method for generating synthetic CT images from modified Dixon (mDixon) MR data. The synthetic CT is used for attenuation correction (AC) when reconstructing PET data on abdomen and pelvis. While MR does not intrinsically contain any information about photon attenuation, AC is needed in PET/MR systems in order to be quantitatively accurate and to meet qualification standards required for use in many multi-center trials. Existing MR-based synthetic CT generation methods either use advanced MR sequences that have long acquisition time and limited clinical availability or use matching of the MR images from a newly scanned subject to images in a library of MR-CT pairs which has difficulty in accounting for the diversity of human anatomy especially in patients that have pathologies. To address these deficiencies, we present a five-phase interlinked method that uses mDixon MR acquisition and advanced machine learning methods for synthetic CT generation. Both transfer fuzzy clustering and active learning-based classification (TFC-ALC) are used. The significance of our efforts is fourfold: 1) TFC-ALC is capable of better synthetic CT generation than methods currently in use on the challenging abdomen using only common Dixon-based scanning. 2) TFC partitions MR voxels initially into the four groups regarding fat, bone, air, and soft tissue via transfer learning; ALC can learn insightful classifiers, using as few but informative labeled examples as possible to precisely distinguish bone, air, and soft tissue. Combining them, the TFC-ALC method successfully overcomes the inherent imperfection and potential uncertainty regarding the co-registration between CT and MR images. 3) Compared with existing methods, TFC-ALC features not only preferable synthetic CT generation but also improved parameter robustness, which facilitates its clinical practicability. Applying the proposed approach on mDixon-MR data from ten subjects, the average score of the mean absolute prediction deviation (MAPD) was 89.78±8.76 which is significantly better than the 133.17±9.67 obtained using the all-water (AW) method (p=4.11E-9) and the 104.97±10.03 obtained using the four-cluster-partitioning (FCP, i.e., external-air, internal-air, fat, and soft tissue) method (p=0.002). 4) Experiments in the PET SUV errors of these approaches show that TFC-ALC achieves the highest SUV accuracy and can generally reduce the SUV errors to 5% or less. These experimental results distinctively demonstrate the effectiveness of our proposed TFCALC method for the synthetic CT generation on abdomen and pelvis using only the commonly-available Dixon pulse sequence.

Uptake of 18F-labeled 6-fluoro-6-deoxy-D-glucose by skeletal muscle is responsive to insulin stimulation.

UNLABELLED: We are developing a methodology for the noninvasive imaging of glucose transport in vivo with PET and (18)F-labeled 6-fluoro-6-deoxy-d-glucose ((18)F-6FDG), a tracer that is transported but not phosphorylated. To validate the method, we evaluated the biodistribution of (18)F-6FDG to test whether it is consistent with the known properties of glucose transport, particularly with regard to insulin stimulation of glucose transport. METHODS: Under glucose clamp conditions, rats were imaged at the baseline and under conditions of hyperinsulinemia. RESULTS: The images showed that the radioactivity concentration in skeletal muscle was higher in the presence of insulin than at the baseline. We also found evidence that the metabolism of (18)F-6FDG was negligible in several tissues. CONCLUSION: (18)F-6FDG is a valid tracer that can be used in in vivo transport studies. PET studies performed under glucose clamp conditions demonstrated that the uptake of nonphosphorylated glucose transport tracer (18)F-6FDG is sensitive to insulin stimulation.

SLICR super-voxel algorithm for fast, robust quantification of myocardial blood flow by dynamic computed tomography myocardial perfusion imaging.

We created and evaluated a processing method for dynamic computed tomography myocardial perfusion imaging (CT-MPI) of myocardial blood flow (MBF), which combines a modified simple linear iterative clustering algorithm (SLIC) with robust perfusion quantification, hence the name SLICR. SLICR adaptively segments the myocardium into nonuniform super-voxels with similar perfusion time attenuation curves (TACs). Within each super-voxel, an α-trimmed-median TAC was computed to robustly represent the super-voxel and a robust physiological model (RPM) was implemented to semi-analytically estimate MBF. SLICR processing was compared with another voxel-wise MBF preprocessing approach, which included a spatiotemporal bilateral filter (STBF) for noise reduction prior to perfusion quantification. Image data from a digital CT-MPI phantom and a porcine ischemia model were evaluated. SLICR was ∼ 50 -fold faster than voxel-wise RPM and other model-based methods while retaining sufficient resolution to show clinically relevant features, such as a transmural perfusion gradient. SLICR showed markedly improved accuracy and precision, as compared with other methods. At a simulated MBF of 100 mL/min-100 g and a tube current-time product of 100 mAs (50% of nominal), the MBF estimates were 101 ± 12 , 94 ± 56 , and 54 ± 24 mL / min - 100 g for SLICR, the voxel-wise Johnson-Wilson model, and a singular value decomposition-model independent method with STBF, respectively. SLICR estimated MBF precisely and accurately ( 103 ± 23 mL / min - 100 g ) at 25% nominal dose, while other methods resulted in larger errors. With the porcine model, the SLICR results were consistent with the induced ischemia. SLICR simultaneously accelerated and improved the quality of quantitative perfusion processing without compromising clinically relevant distributions of perfusion characteristics.

Chemoradiotherapy-related carotid artery inflammation in head and neck cancer patients quantified by [18F]FDG PET/CT.

OBJECTIVES: Radiotherapy (RT) is associated with an increased risk of cardiovascular disease (CVD), but little is known about the mechanism for vascular injury and methods for early detection. MATERIALS AND METHODS: We conducted a prospective, pilot study of carotid artery inflammation using 18F-labeled 2-fluoro-2-deoxy-d-glucose ([18F]FDG) PET/CT imaging pre- and 3 months post-RT in head-and-neck cancer (HNC) patients. [18F]FDG uptake by the carotid arteries was measured by the maximum and mean target to background ratio (TBRMAX, TBRMEAN) and the mean partial volume corrected standardized uptake value (pvcSUVMEAN). RESULTS: Of the 22 patients who completed both pre and post-RT scans, the majority (82%) had stage III or stage IV disease and received concurrent chemotherapy. TBRMAX, TBRMEAN, and pvcSUVMEAN were all significantly higher 3 months after RT versus before RT with mean difference values (95% CI; p-value) of 0.17 (0.1-0.25; 0.0001), 0.19 (0.12-0.25; 0.0001), and 0.31 g/ml (0.12-0.5; 0.002), respectively. Fifteen patients (68%) had HPV-positive tumors, which were associated with lower pre-RT [18F]FDG signal, but a greater increase in TBRMAX (19% vs 5%), TBRMEAN (21% vs 11%) and pvcSUVMEAN (20% increase vs 3% decrease), compared to HPV negativity. CONCLUSION: There is a significant increase in carotid artery inflammation in HNC patients due to CRT that amounts to a degree that has previously been associated with higher risk for future CVD events. The subset of patients with HPV-positive tumors experienced the greatest increases in vascular inflammation due to CRT. Carotid [18F]FDG uptake may be an early biomarker of RT-related vascular injury.