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.

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.

UTE-mDixon-based thorax synthetic CT generation.

PURPOSE: Accurate photon attenuation assessment from MR data remains an unmet challenge in the thorax due to tissue heterogeneity and the difficulty of MR lung imaging. As thoracic tissues encompass the whole physiologic range of photon absorption, large errors can occur when using, for example, a uniform, water-equivalent or a soft-tissue-only approximation. The purpose of this study was to introduce a method for voxel-wise thoracic synthetic CT (sCT) generation from MR data attenuation correction (AC) for PET/MR or for MR-only radiation treatment planning (RTP). METHODS: Acquisition: A radial stack-of-stars combining ultra-short-echo time (UTE) and modified Dixon (mDixon) sequence was optimized for thoracic imaging. The UTE-mDixon pulse sequence collects MR signals at three TE times denoted as UTE, Echo1, and Echo2. Three-point mDixon processing was used to reconstruct water and fat images. Bias field correction was applied in order to avoid artifacts caused by inhomogeneity of the MR magnetic field. ANALYSIS: Water fraction and R2* maps were estimated using the UTE-mDixon data to produce a total of seven MR features, that is UTE, Echo1, Echo2, Dixon water, Dixon fat, Water fraction, and R2*. A feature selection process was performed to determine the optimal feature combination for the proposed automatic, 6-tissue classification for sCT generation. Fuzzy c-means was used for the automatic classification which was followed by voxel-wise attenuation coefficient assignment as a weighted sum of those of the component tissues. Performance evaluation: MR data collected using the proposed pulse sequence were compared to those using a traditional two-point Dixon approach. Image quality measures, including image resolution and uniformity, were evaluated using an MR ACR phantom. Data collected from 25 normal volunteers were used to evaluate the accuracy of the proposed method compared to the template-based approach. Notably, the template approach is applicable here, that is normal volunteers, but may not be robust enough for patients with pathologies. RESULTS: The free breathing UTE-mDixon pulse sequence yielded images with quality comparable to those using the traditional breath holding mDixon sequence. Furthermore, by capturing the signal before T2* decay, the UTE-mDixon image provided lung and bone information which the mDixon image did not. The combination of Dixon water, Dixon fat, and the Water fraction was the most robust for tissue clustering and supported the classification of six tissues, that is, air, lung, fat, soft tissue, low-density bone, and dense bone, used to generate the sCT. The thoracic sCT had a mean absolute difference from the template-based (reference) CT of less than 50 HU and which was better agreement with the reference CT than the results produced using the traditional Dixon-based data. CONCLUSION: MR thoracic acquisition and analyses have been established to automatically provide six distinguishable tissue types to generate sCT for MR-based AC of PET/MR and for MR-only RTP.

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.

Abdominal, multi-organ, auto-contouring method for online adaptive magnetic resonance guided radiotherapy: An intelligent, multi-level fusion approach.

BACKGROUND: Manual contouring remains the most laborious task in radiation therapy planning and is a major barrier to implementing routine Magnetic Resonance Imaging (MRI) Guided Adaptive Radiation Therapy (MR-ART). To address this, we propose a new artificial intelligence-based, auto-contouring method for abdominal MR-ART modeled after human brain cognition for manual contouring. METHODS/MATERIALS: Our algorithm is based on two types of information flow, i.e. top-down and bottom-up. Top-down information is derived from simulation MR images. It grossly delineates the object based on its high-level information class by transferring the initial planning contours onto daily images. Bottom-up information is derived from pixel data by a supervised, self-adaptive, active learning based support vector machine. It uses low-level pixel features, such as intensity and location, to distinguish each target boundary from the background. The final result is obtained by fusing top-down and bottom-up outputs in a unified framework through artificial intelligence fusion. For evaluation, we used a dataset of four patients with locally advanced pancreatic cancer treated with MR-ART using a clinical system (MRIdian, Viewray, Oakwood Village, OH, USA). Each set included the simulation MRI and onboard T1 MRI corresponding to a randomly selected treatment session. Each MRI had 144 axial slices of 266 × 266 pixels. Using the Dice Similarity Index (DSI) and the Hausdorff Distance Index (HDI), we compared the manual and automated contours for the liver, left and right kidneys, and the spinal cord. RESULTS: The average auto-segmentation time was two minutes per set. Visually, the automatic and manual contours were similar. Fused results achieved better accuracy than either the bottom-up or top-down method alone. The DSI values were above 0.86. The spinal canal contours yielded a low HDI value. CONCLUSION: With a DSI significantly higher than the usually reported 0.7, our novel algorithm yields a high segmentation accuracy. To our knowledge, this is the first fully automated contouring approach using T1 MRI images for adaptive radiotherapy.

Machine learning-based dual-energy CT parametric mapping.

The aim is to develop and evaluate machine learning methods for generating quantitative parametric maps of effective atomic number (Zeff), relative electron density (ρ e), mean excitation energy (I x ), and relative stopping power (RSP) from clinical dual-energy CT data. The maps could be used for material identification and radiation dose calculation. Machine learning methods of historical centroid (HC), random forest (RF), and artificial neural networks (ANN) were used to learn the relationship between dual-energy CT input data and ideal output parametric maps calculated for phantoms from the known compositions of 13 tissue substitutes. After training and model selection steps, the machine learning predictors were used to generate parametric maps from independent phantom and patient input data. Precision and accuracy were evaluated using the ideal maps. This process was repeated for a range of exposure doses, and performance was compared to that of the clinically-used dual-energy, physics-based method which served as the reference. The machine learning methods generated more accurate and precise parametric maps than those obtained using the reference method. Their performance advantage was particularly evident when using data from the lowest exposure, one-fifth of a typical clinical abdomen CT acquisition. The RF method achieved the greatest accuracy. In comparison, the ANN method was only 1% less accurate but had much better computational efficiency than RF, being able to produce parametric maps in 15 s. Machine learning methods outperformed the reference method in terms of accuracy and noise tolerance when generating parametric maps, encouraging further exploration of the techniques. Among the methods we evaluated, ANN is the most suitable for clinical use due to its combination of accuracy, excellent low-noise performance, and computational efficiency.

Effects of the Acute and Chronic Ethanol Intoxication on Acetate Metabolism and Kinetics in the Rat Brain.

BACKGROUND: Ethanol (EtOH) intoxication inhibits glucose transport and decreases overall brain glucose metabolism; however, humans with long-term EtOH consumption were found to have a significant increase in [1-11 C]-acetate uptake in the brain. The relationship between the cause and effect of [1-11 C]-acetate kinetics and acute/chronic EtOH intoxication, however, is still unclear. METHODS: [1-11 C]-acetate positron emission tomography (PET) with dynamic measurement of K1 and k2 rate constants was used to investigate the changes in acetate metabolism in different brain regions of rats with acute or chronic EtOH intoxication. RESULTS: PET imaging demonstrated decreased [1-11 C]-acetate uptake in rat brain with acute EtOH intoxication, but this increased with chronic EtOH intoxication. Tracer uptake rate constant K1 and clearance rate constant k2 were decreased in acutely intoxicated rats. No significant change was noted in K1 and k2 in chronic EtOH intoxication, although 6 of 7 brain regions showed slightly higher k2 than baseline. These results indicate that acute EtOH intoxication accelerated acetate transport and metabolism in the rat brain, whereas chronic EtOH intoxication status showed no significant effect. CONCLUSIONS: In vivo PET study confirmed the modulatory role of EtOH, administered acutely or chronically, in [1-11 C]-acetate kinetics and metabolism in the rat brain. Acute EtOH intoxication may inhibit the transport and metabolism of acetate in the brain, whereas chronic EtOH exposure may lead to the adaptation of the rat brain to EtOH in acetate utilization. [1-11 C]-acetate PET imaging is a feasible approach to study the effect of EtOH on acetate metabolism in rat brain.

NADPH oxidase subunit 4-mediated reactive oxygen species contribute to cycling hypoxia-promoted tumor progression in glioblastoma multiforme.

BACKGROUND: Cycling and chronic tumor hypoxia are involved in tumor development and growth. However, the impact of cycling hypoxia and its molecular mechanism on glioblastoma multiforme (GBM) progression remain unclear. METHODOLOGY: Glioblastoma cell lines, GBM8401 and U87, and their xenografts were exposed to cycling hypoxic stress in vitro and in vivo. Reactive oxygen species (ROS) production in glioblastoma cells and xenografts was assayed by in vitro ROS analysis and in vivo molecular imaging studies. NADPH oxidase subunit 4 (Nox4) RNAi-knockdown technology was utilized to study the role of Nox4 in cycling hypoxia-mediated ROS production and tumor progression. Furthermore, glioblastoma cells were stably transfected with a retroviral vector bearing a dual reporter gene cassette that allowed for dynamic monitoring of HIF-1 signal transduction and tumor cell growth in vitro and in vivo, using optical and nuclear imaging. Tempol, an antioxidant compound, was used to investigate the impact of ROS on cycling hypoxia-mediated HIF-1 activation and tumor progression. PRINCIPAL FINDINGS: Glioblastoma cells and xenografts were compared under cycling hypoxic and normoxic conditions; upregulation of NOX4 expression and ROS levels were observed under cycling hypoxia in glioblastoma cells and xenografts, concomitant with increased tumor cell growth in vitro and in vivo. However, knockdown of Nox4 inhibited these effects. Moreover, in vivo molecular imaging studies demonstrated that Tempol is a good antioxidant compound for inhibiting cycling hypoxia-mediated ROS production, HIF-1 activation, and tumor growth. Immunofluorescence imaging and flow cytometric analysis for NOX4, HIF-1 activation, and Hoechst 3342 in glioblastoma also revealed high localized NOX4 expression predominantly in potentially cycling hypoxic areas with HIF-1 activation and blood perfusion within the endogenous solid tumor microenvironment. CONCLUSIONS: Cycling hypoxia-induced ROS via Nox4 is a critical aspect of cancer biology to consider for therapeutic targeting of cycling hypoxia-promoted HIF-1 activation and tumor progression in GBM.

Construction of mutant TKGFP for real-time imaging of temporal dynamics of HIF-1 signal transduction activity mediated by hypoxia and reoxygenation in tumors in living mice.

UNLABELLED: The herpes simplex virus type 1 thymidine kinase (HSV1-tk)/green fluorescent protein (TKGFP) dual-reporter gene and a multimodality imaging approach play a critical role in monitoring therapeutic gene expression, immune cell trafficking, and protein-protein interactions in translational molecular-genetic imaging. However, the cytotoxicity and low temporal resolution of TKGFP limits its application in studies that require a rapid turnover of the reporter. The purpose of this study was to construct a novel mutant TKGFP fusion reporter gene with low cytotoxicity and high temporal resolution for use in the real-time monitoring of temporal dynamics and spatial heterogeneity of hypoxia-inducible factor 1 (HIF-1) signal transduction activity mediated by hypoxia and reoxygenation in vitro and in vivo. METHODS: Destabilized TKGFP was produced by inserting the nuclear export signal (NES) sequence at the N terminus and fusing the degradation domain of mouse ornithine decarboxylase (dMODC) at the C terminus. The stability of TKGFP in living NG4TL4 cells was determined by Western blot analysis, HSV1-tk enzyme activity assay, and flow cytometric analysis. The suitability of NESTKGFP:dMODC as a transcription reporter was investigated by linking it to a promoter consisting of 8 copies of hypoxia-responsive elements, whose activities depend on HIF-1. The dynamic transcriptional events mediated by hypoxia and reoxygenation were monitored by NESTKGFP:dMODC or TKGFP and determined by optical imaging and PET. RESULTS: Unlike TKGFP, NESTKGFP:dMODC was unstable in the presence of cycloheximide and showed a short half-life of protein and enzyme activity. Rapid turnover of NESTKGFP:dMODC occurred in a 26S proteasome-dependent manner. Furthermore, NESTKGFP:dMODC showed an upregulated expression and low cytotoxicity in living cells. Studies of hypoxia-responsive TKGFP and NESTKGFP:dMODC expression showed that NESTKGFP:dMODC as a reporter gene had better temporal resolution than did TKGFP for monitoring the dynamic transcriptional events mediated by hypoxia and reoxygenation; the TKGFP expression level was not optimal for the purpose of monitoring. CONCLUSION: In translational molecular-genetic imaging, NESTKGFP:dMODC as a reporter gene, together with optical imaging and PET, allows the direct monitoring of transcription induction and easy determination of its association with other biochemical changes.