RNF149 Destabilizes IFNGR1 in Macrophages to Favor Post-Infarction Cardiac Repair.

BACKGROUND: Macrophage-driven inflammation critically involves in cardiac injury and repair following myocardial infarction (MI). However, the intrinsic mechanisms that halt the immune response of macrophages, which is critical to preserve homeostasis and effective infarct repair, remain to be fully defined. Here, we aimed to determine the ubiquitination-mediated regulatory effects on averting exaggerated inflammatory responses in cardiac macrophages. METHODS: We used transcriptome analysis of mouse cardiac macrophages and bone marrow-derived macrophages to identify the E3 ubiquitin ligase RNF149 (RING finger protein 149) as a modulator of macrophage response to MI. Employing loss-of-function methodologies, bone marrow transplantation approaches, and adenovirus-mediated RNF149 overexpression in macrophages, we elucidated the functional role of RNF149 in MI. We explored the underlying mechanisms through flow cytometry, transcriptome analysis, immunoprecipitation/mass spectrometry analysis, and functional experiments. RNF149 expression was measured in the cardiac tissues of patients with acute MI and healthy controls. RESULTS: RNF149 was highly expressed in murine and human cardiac macrophages at the early phase of MI. Knockout of RNF149, transplantation of Rnf149-/- bone marrow, and bone marrow macrophage-specific RNF149-knockdown markedly exacerbated cardiac dysfunction in murine MI models. Conversely, overexpression of RNF149 in macrophages attenuated the ischemia-induced decline in cardiac contractile function. RNF149 deletion increased infiltration of proinflammatory monocytes/macrophages, accompanied by a hastened decline in reparative subsets, leading to aggravation of myocardial apoptosis and impairment of infarct healing. Our data revealed that RNF149 in infiltrated macrophages restricted inflammation by promoting ubiquitylation-dependent proteasomal degradation of IFNGR1 (interferon gamma receptor 1). Loss of IFNGR1 rescued deleterious effects of RNF149 deficiency on MI. We further demonstrated that STAT1 activation induced Rnf149 transcription, which, in turn, destabilized the IFNGR1 protein to counteract type-II IFN (interferon) signaling, creating a feedback control mechanism to fine-tune macrophage-driven inflammation. CONCLUSIONS: These findings highlight the significance of RNF149 as a molecular brake on macrophage response to MI and uncover a macrophage-intrinsic posttranslational mechanism essential for maintaining immune homeostasis and facilitating cardiac repair following MI.

Visual and Quantitative Evaluation of Low-Concentration Bismuth in Dual-Contrast Imaging of Iodine and Bismuth Using Clinical Photon-Counting CT.

Simultaneous dual-contrast imaging of iodine and bismuth has shown promise in prior phantom and animal studies utilizing spectral CT. However, it is noted that in previous studies, Pepto-Bismol has frequently been employed as the source of bismuth, exceeding the recommended levels for human subjects. This investigation sought to assess the feasibility of visually differentiating and precisely quantifying low-concentration bismuth using clinical dual-source photon-counting CT (PCCT) in a scenario involving both iodinated and bismuth-based contrast materials. Four bismuth samples (0.6, 1.3, 2.5, and 5.1 mg/mL) were prepared using Pepto-Bismol, alongside three iodine rods (1, 2, and 5 mg/mL), inserted into multi-energy CT phantoms with three different sizes, and scanned on a PCCT system at three tube potentials (120, 140, and Sn140 kV). A generic image-based three-material decomposition method generated iodine and bismuth maps, with mean mass concentrations and noise levels measured. The root-mean-square errors for iodine and bismuth determined the optimal tube potential. The tube potential of 140 kV demonstrated optimal quantification performance when both iodine and bismuth were considered. Distinct differentiation of iodine rods with all three concentrations and bismuth samples with mass concentrations ≥ 1.3 mg/mL was observed across all phantom sizes at the optimal kV setting.

Low-contrast detectability of photon-counting-detector CT at different scan modes and image types in comparison with energy-integrating-detector CT.

Purpose: We aim to compare the low-contrast detectability of a clinical whole-body photon-counting-detector (PCD)-CT at different scan modes and image types with an energy-integrating-detector (EID)-CT. Approach: We used a channelized Hotelling observer (CHO) previously optimized for quality control purposes. An American College of Radiology CT accreditation phantom was scanned on both PCD-CT and EID-CT with 10 phantom positionings. For PCD-CT, images were generated using two scan modes, standard resolution (SR) and ultra-high-resolution (UHR); two image types, virtual monochromatic images at 70 keV and low-energy threshold (T3D); both filtered-back-projection (FBP) and iterative reconstruction (IR) reconstruction methods; and three reconstruction kernels. For each positioning, three repeated scans were acquired for each scan mode, image type, and CTDIvol of 6, 12, and 24 mGy. For EID-CT, images acquired from scans (10 positionings × 3 repeats × 3 doses) were reconstructed using the closest counterpart FBP and IR kernels. CHO was applied to calculate the index of detectability (d') on both scanners. Results: With the smooth Br44 kernel, the d' of UHR was mostly comparable with that of the SR mode (difference: -11.4% to 8.3%, p=0.020 to 0.956), and the T3D images had a higher d' (difference: 0.7% to 25.6%) than 70 keV images on PCD-CT. Compared with the EID-CT, UHR-T3D of PCD-CT had non-inferior d' (difference: -2.7% to 12.9%) with IR and non-superior d' (difference: 0.8% to 11.2%) with FBP using the Br44 kernel. PCD-CT produced higher d' than EID-CT by 61.8% to 247.1% with the sharper reconstruction kernels. Conclusions: The comparison between PCD-CT and EID-CT was significantly influenced by the reconstruction method and kernel. With a smooth kernel that is typically used in low-contrast detection tasks, the PCD-CT demonstrated low-contrast detectability that was comparable to EID-CT with IR and showed no superiority when using FBP. With the use of sharper kernels, the PCD-CT significantly outperformed EID-CT in low-contrast detectability.

Evaluation of data uncertainty for deep-learning-based CT noise reduction using ensemble patient data and a virtual imaging trial framework.

Deep learning-based image reconstruction and noise reduction (DLIR) methods have been increasingly deployed in clinical CT. Accurate assessment of their data uncertainty properties is essential to understand the stability of DLIR in response to noise. In this work, we aim to evaluate the data uncertainty of a DLIR method using real patient data and a virtual imaging trial framework and compare it with filtered-backprojection (FBP) and iterative reconstruction (IR). The ensemble of noise realizations was generated by using a realistic projection domain noise insertion technique. The impact of varying dose levels and denoising strengths were investigated for a ResNet-based deep convolutional neural network (DCNN) model trained using patient images. On the uncertainty maps, DCNN shows more detailed structures than IR although its bias map has less structural dependency, which implies that DCNN is more sensitive to small changes in the input. Both visual examples and histogram analysis demonstrated that hotspots of uncertainty in DCNN may be associated with a higher chance of distortion from the truth than IR, but it may also correspond to a better detection performance for some of the small structures.

Learned high-resolution cardiac CT imaging from ultra-high-resolution PCD-CT.

Coronary computed tomography angiography (cCTA) is a widely used non-invasive diagnostic exam for patients with coronary artery disease (CAD). However, most clinical CT scanners are limited in spatial resolution from use of energy-integrating detectors (EIDs). Radiological evaluation of CAD is challenging, as coronary arteries are small (3-4 mm diameter) and calcifications within them are highly attenuating, leading to blooming artifacts. As such, this is a task well suited for high spatial resolution. Recently, photon-counting-detector (PCD) CT became commercially available, allowing for ultra-high resolution (UHR) data acquisition. However, PCD-CTs are costly, restricting widespread accessibility. To address this problem, we propose a super resolution convolutional neural network (CNN): ILUMENATE (Improved LUMEN visualization through Artificial super-resoluTion imagEs), creating a high resolution (HR) image simulating UHR PCD-CT. The network was trained and validated using patches extracted from 8 patients with a modified U-Net architecture. Training input and labels consisted of UHR PCD-CT images reconstructed with a smooth kernel degrading resolution (LR input) and sharp kernel (HR label). The network learned the resolution difference and was tested on 5 unseen LR patients. We evaluated network performance quantitatively and qualitatively through visual inspection, line profiles to assess spatial resolution improvements, ROIs for CT number stability and noise assessment, structural similarity index (SSIM), and percent diameter luminal stenosis. Overall, ILUMENATE improved images quantitatively and qualitatively, creating sharper edges more closely resembling reconstructed HR reference images, maintained stable CT numbers with less than 4% difference, reduced noise by 28%, maintained structural similarity (average SSIM = 0.70), and reduced percent diameter stenosis with respect to input images. ILUMENATE demonstrates potential impact for CAD patient management, improving the quality of LR CT images bringing them closer to UHR PCD-CT images.

Automated Web-based Software for CT Quality Control Testing of Low-contrast Detectability using Model Observers.

The Channelized Hotelling observer (CHO) is well correlated with human observer performance in many CT detection/classification tasks but has not been widely adopted in routine CT quality control and performance evaluation, mainly because of the lack of an easily available, efficient, and validated software tool. We developed a highly automated solution - CT image quality evaluation and Protocol Optimization (CTPro), a web-based software platform that includes CHO and other traditional image quality assessment tools such as modulation transfer function and noise power spectrum. This tool can allow easy access to the CHO for both the research and clinical community and enable efficient, accurate image quality evaluation without the need of installing additional software. Its application was demonstrated by comparing the low-contrast detectability on a clinical photon-counting-detector (PCD)-CT with a traditional energy-integrating-detector (EID)-CT, which showed UHR-T3D had 6.2% higher d' than EID-CT with IR (p = 0.047) and 4.1% lower d' without IR (p = 0.122).

Image quality evaluation in deep-learning-based CT noise reduction using virtual imaging trial methods: Contrast-dependent spatial resolution.

BACKGROUND: Deep-learning-based image reconstruction and noise reduction methods (DLIR) have been increasingly deployed in clinical CT. Accurate image quality assessment of these methods is challenging as the performance measured using physical phantoms may not represent the true performance of DLIR in patients since DLIR is trained mostly on patient images. PURPOSE: In this work, we aim to develop a patient-data-based virtual imaging trial framework and, as a first application, use it to measure the spatial resolution properties of a DLIR method. METHODS: The patient-data-based virtual imaging trial framework consists of five steps: (1) insertion of lesions into projection domain data using the acquisition geometry of the patient exam to simulate different lesion characteristics; (2) insertion of noise into projection domain data using a realistic photon statistical model of the CT system to simulate different dose levels; (3) creation of DLIR-processed images from projection or image data; (4) creation of ensembles of DLIR-processed patient images from a large number of noise and lesion realizations; and (5) evaluation of image quality using ensemble DLIR images. This framework was applied to measure the spatial resolution of a ResNet based deep convolutional neural network (DCNN) trained on patient images. Lesions in a cylindrical shape and different contrast levels (-500, -100, -50, -20, -10 HU) were inserted to the lower right lobe of the liver in a patient case. Multiple dose levels were simulated (50%, 25%, 12.5%). Each lesion and dose condition had 600 noise realizations. Multiple reconstruction and denoising methods were used on all the noise realizations, including the original filtered-backprojection (FBP), iterative reconstruction (IR), and the DCNN method with three different strength setting (DCNN-weak, DCNN-medium, and DCNN-strong). Mean lesion signal was calculated by performing ensemble averaging of all the noise realizations for each lesion and dose condition and then subtracting the lesion-present images from the lesion absent images. Modulation transfer functions (MTFs) both in-plane and along the z-axis were calculated based on the mean lesion signals. The standard deviations of MTFs at each condition were estimated with bootstrapping: randomly sampling (with replacement) all the DLIR/FBP/IR images from the ensemble data (600 samples) at each condition. The impact of varying lesion contrast, dose levels, and denoising strengths were evaluated. Statistical analysis with paired t-test was used to compare the z-axis and in-plane spatial resolution of five algorithms for five different contrasts and three dose levels. RESULTS: The in-plane and z-axis spatial resolution degradation of DCNN becomes more severe as the contrast or radiation dose decreased, or DCNN denoising strength increased. In comparison with FBP, a 59.5% and 4.1% reduction of in-plane and z-axis MTF (in terms of spatial frequencies at 50% MTF), respectively, was observed at low contrast (-10 HU) for DCNN with the highest denoising strength at 25% routine dose level. When the dose level reduces from 50% to 12.5% of routine dose, the in-plane and z-axis MTFs reduces from 92.1% to 76.3%, and from 98.9% to 95.5%, respectively, at contrast of -100 HU, using FBP as the reference. For most conditions of contrasts and dose levels, significant differences were found among the five algorithms, with the following relationship in both in-plane and cross-plane spatial resolution: FBP > DCNN-Weak > IR > DCNN-Medium > DCNN-Strong. The spatial resolution difference among algorithms decreases at higher contrast or dose levels. CONCLUSIONS: A patient-data-based virtual imaging trial framework was developed and applied to measuring the spatial resolution properties of a DCNN noise reduction method at different contrast and dose levels using real patient data. As with other non-linear image reconstruction and post-processing techniques, the evaluated DCNN method degraded the in-plane and z-axis spatial resolution at lower contrast levels, lower radiation dose, and higher denoising strength.

Channelized hotelling observer-based low-contrast detectability on the ACR CT accreditation phantom: Part II. Repeatability study.

BACKGROUND: Objective and quantitative evaluation for low-contrast detectability that correlates with human observer performance is lacking for routine CT quality control testing. Channelized Hotelling observer (CHO) is considered a strong candidate to fill the need but has long been deemed impractical to implement due to its requirement of a large number of repeated scans in order to provide accurate and precise estimates of index of detectability (d'). In our previous work, we optimized a CHO model observer on the American College of Radiology (ACR) CT accreditation phantom and achieved accurate measurement of d' with only 1-3 repeat scans. PURPOSE: In this work, we aim to validate the repeatability of the proposed CHO-based low-contrast evaluation on four scanner models using the ACR CT accreditation phantom. METHODS: The repeatability test was performed on four different scanners from two major CT manufacturers: Siemens Force and Alpha; Canon Prism and Prime SP. An ACR CT phantom was scanned 10 times, each time after repositioning of the phantom. For each repositioning, 3 repeated scans were acquired at 24, 12, and 6 mGy on all four scanner models. CHO was applied at the measured dose levels for different low-contrast object sizes (4-6 mm). The CHO was also applied to images created using deep learning-based reconstructions on Canon Prism and to four different scan/reconstruction modes on the Siemens Alpha, a photon-counting-detector (PCD)-CT. The repeatability was evaluated by the probability that a measurement would fall within the ±15% tolerance (P<15% ). RESULTS: With the CHO setting optimized for the ACR phantom and the use of 3 repeated scans and 9 non-overlapping slices per scan, the CHO measurement could provide high repeatability with P<15% of 98.8%-99.9% at 12 mGy with IR reconstruction on all four scanners. On scanner A, P<15% were 91.5%-99.9% at the three dose levels and for all three object sizes while the numbers were 93.6%-99.998% on scanner B. P<15% were 96.5%-97.2% for the two deep learning reconstructions and 97.0%-99.97% for the four scan/reconstruction modes on the PCD-CT. CONCLUSION: The CHO provided highly repeatable measurements with over 95% probability that a CHO measurement would lie within the ±15% tolerance for most of the dose levels and object sizes on the ACR phantom. The repeatability was maintained when the CHO was applied to images created with a commercial deep learning-based reconstruction and various scan/reconstruction modes on a PCD-CT. This study demonstrates that practical implementation of CHO for routine quality control and performance evaluation is feasible.

Self-trained deep convolutional neural network for noise reduction in CT.

Purpose: Supervised deep convolutional neural network (CNN)-based methods have been actively used in clinical CT to reduce image noise. The networks of these methods are typically trained using paired high- and low-quality data from a massive number of patients and/or phantom images. This training process is tedious, and the network trained under a given condition may not be generalizable to patient images acquired and reconstructed under different conditions. We propose a self-trained deep CNN (ST_CNN) method for noise reduction in CT that does not rely on pre-existing training datasets. Approach: The ST_CNN training was accomplished using extensive data augmentation in the projection domain, and the inference was applied to the data itself. Specifically, multiple independent noise insertions were applied to the original patient projection data to generate multiple realizations of low-quality projection data. Then, rotation augmentation was adopted for both the original and low-quality projection data by applying the rotation angle directly on the projection data so that images were rotated at arbitrary angles without introducing additional bias. A large number of paired low- and high-quality images from the same patient were reconstructed and paired for training the ST_CNN model. Results: No significant difference was found between the ST_CNN and conventional CNN models in terms of the peak signal-to-noise ratio and structural similarity index measure. The ST_CNN model outperformed the conventional CNN model in terms of noise texture and homogeneity in liver parenchyma as well as better subjective visualization of liver lesions. The ST_CNN may sacrifice the sharpness of vessels slightly compared to the conventional CNN model but without affecting the visibility of peripheral vessels or diagnosis of vascular pathology. Conclusions: The proposed ST_CNN method trained from the data itself may achieve similar image quality in comparison with conventional deep CNN denoising methods pre-trained on external datasets.

Evaluation of Low-contrast Detectability of Photon-Counting-Detector CT Using Channelized Hotelling Observer and an ACR Accreditation Phantom.

The purpose of this work is to evaluate the low-contrast detectability on a clinical whole-body photon-counting-detector (PCD)-CT scanner and compare it with an energy-integrating-detector (EID) CT scanner, using an efficient Channelized Hotelling observer (CHO)-based method previously developed and optimized on the American College of Radiology (ACR) CT accreditation phantom for routine quality control (QC) purpose. The low-contrast module of an ACR CT phantom was scanned on both the PCD-CT and EID-CT scanners, each with 10 different positionings. For PCD-CT, data were acquired at 120 kV with two major scan modes, standard resolution (SR) (collimation: 144×0.4 mm) and ultra-high-resolution (UHR) (120×0.2 mm). Images were reconstructed with two major modes: virtual monochromatic energy at 70 keV and low-energy threshold (T3D), each with filtered-backprojection (Br44) and iterative reconstruction (Br44-3) kernels. For each positioning, 3 repeated scans were acquired for each scan mode at a fixed radiation dose setting (CTDIvol = 12 mGy). For EID-CT, scans (10 positionings × 3 repeated scans) were performed at a matched CTDIvol, and images were reconstructed using the same kernels with FBP and IR. A recently developed CHO-based method dedicated for QC of low-contrast performance on the ACR phantom was applied to calculate the low-contrast detectability (d') for each scan and reconstruction condition. Results showed that there was no significant difference in low-contrast detectability (d') between the UHR mode and SR mode (p = 0.360-0.942), and the T3D reconstruction resulted in 7.7%-14.6% higher d' than 70keV (p < 0.0016). Similar detectability levels were observed on PCD-CT and EID-CT. The PCD-CT: UHR-T3D had 6.2% higher d' than EID-CT with IR (p = 0.047) and 4.1% lower d' without IR (p = 0.122).

High-resolution computed tomography angiography of the orbit using a photon-counting computed tomography scanner.

BACKGROUND AND PURPOSE: Recent introduction of photon counting detector (PCD) computed tomography (CT) scanners into clinical practice further improve CT angiography (CTA) depiction of orbital arterial vasculature compared to conventional energy integrating detector (EID) CT scanners. PCD-CTA of the orbit can provide a detailed arterial roadmap of the orbit which can de diagnostic on its own or serve as a helpful planning adjunct for both diagnostic and therapeutic catheter-based angiography of the orbit. METHODS: For this review, EID and PCD-CT imaging was obtained in 28 volunteers. The volume CT dose index was closely matched. A dual-energy scanning protocol was used on EID-CT. An ultra-high-resolution (UHR) scan mode was used on PCD-CT. Images were reconstructed at 0.6 mm slice thickness using a closely matched medium-sharp standard resolution (SR) kernel. High-resolution (HR) images with the sharpest quantitative kernel were also reconstructed on PCD-CT at the thinnest slice thickness of 0.2 mm. A denoising algorithm was applied to the HR image series. RESULTS: The imaging description of the orbital vascular anatomy presented in this work was derived from these patients' PCD-CTA images in combination with review of the literature. We found that orbital arterial anatomy is much better depicted with PCD-CTA, and this work can serve primarily as an imaging atlas of the normal orbital vascular anatomy. CONCLUSION: With recent advances in technology, arterial anatomy of the orbit is much better depicted with PCD-CTA as opposed to EID-CTA. Current orbital PCD-CTA technology approaches the necessary resolution threshold for reliable evaluation of central retinal artery occlusion.

Contrast- and noise-dependent spatial resolution measurement for deep convolutional neural network-based noise reduction in CT using patient data.

Deep convolutional neural network (DCNN)-based noise reduction methods have been increasingly deployed in clinical CT. Accurate assessment of their spatial resolution properties is required. Spatial resolution is typically measured on physical phantoms, which may not represent the true performance of DCNN in patients as it is typically trained and tested with patient images and the generalizability of DNN to physical phantoms is questionable. In this work, we proposed a patient-data-based framework to measure the spatial resolution of DCNN methods, which involves lesion- and noise-insertion in projection domain, lesion ensemble averaging, and modulation transfer function measurement using an oversampled edge spread function from the cylindrical lesion signal. The impact of varying lesion contrast, dose levels, and CNN denoising strengths were investigated for a ResNet-based DCNN model trained using patient images. The spatial resolution degradation of DCNN reconstructions becomes more severe as the contrast or radiation dose decreased, or DCNN denoising strength increased. The measured 50%/10% MTF spatial frequencies of DCNN with highest denoising strength were (-500 HU:0.36/0.72 mm-1; -100 HU:0.32/0.65 mm-1; -50 HU:0.27/0.53 mm-1; -20 HU:0.18/0.36 mm-1; -10 HU:0.15/0.30 mm-1), while the 50%/10% MTF values of FBP were almost kept constant of 0.38/0.76 mm-1.

Multislice input for 2D and 3D residual convolutional neural network noise reduction in CT.

Purpose: Deep convolutional neural network (CNN)-based methods are increasingly used for reducing image noise in computed tomography (CT). Current attempts at CNN denoising are based on 2D or 3D CNN models with a single- or multiple-slice input. Our work aims to investigate if the multiple-slice input improves the denoising performance compared with the single-slice input and if a 3D network architecture is better than a 2D version at utilizing the multislice input. Approach: Two categories of network architectures can be used for the multislice input. First, multislice images can be stacked channel-wise as the multichannel input to a 2D CNN model. Second, multislice images can be employed as the 3D volumetric input to a 3D CNN model, in which the 3D convolution layers are adopted. We make performance comparisons among 2D CNN models with one, three, and seven input slices and two versions of 3D CNN models with seven input slices and one or three output slices. Evaluation was performed on liver CT images using three quantitative metrics with full-dose images as reference. Visual assessment was made by an experienced radiologist. Results: When the input channels of the 2D CNN model increases from one to three to seven, a trend of improved performance was observed. Comparing the three models with the seven-slice input, the 3D CNN model with a one-slice output outperforms the other models in terms of noise texture and homogeneity in liver parenchyma as well as subjective visualization of vessels. Conclusions: We conclude the that multislice input is an effective strategy for improving performance for 2D deep CNN denoising models. The pure 3D CNN model tends to have a better performance than the other models in terms of continuity across axial slices, but the difference was not significant compared with the 2D CNN model with the same number of slices as the input.

Application of a Denoising High-Resolution Deep Convolutional Neural Network to Improve Conspicuity of CSF-Venous Fistulas on Photon-Counting CT Myelography.

Photon-counting detector CT myelography is a recently described technique that has several advantages for the detection of CSF-venous fistulas, one of which is improved spatial resolution. To maximally leverage the high spatial resolution of photon-counting detector CT, a sharp kernel and a thin section reconstruction are needed. Sharp kernels and thin slices often result in increased noise, degrading image quality. Here, we describe a novel deep-learning-based algorithm used to denoise photon-counting detector CT myelographic images, allowing the sharpest and thinnest quantitative reconstruction available on the scanner to be used to enhance diagnostic image quality. Currently, the algorithm requires 4-6 hours to create diagnostic, denoised images. This algorithm has the potential to increase the sensitivity of photon-counting detector CT myelography for detecting CSF-venous fistulas, and the technique may be valuable for institutions attempting to optimize photon-counting detector CT myelography imaging protocols.

Efficient Evaluation of Low-contrast Detectability of Deep-CNN-based CT Reconstruction Using Channelized Hotelling Observer on the ACR Accreditation Phantom.

As deep-learning-based denoising and reconstruction methods are gaining more popularity in clinical CT, it is of vital importance that these new algorithms undergo rigorous and objective image quality assessment beyond traditional metrics to ensure diagnostic information is not sacrificed. Channelized Hotelling observer (CHO), which has been shown to be well correlated with human observer performance in many clinical CT tasks, has a great potential to become the method of choice for objective image quality assessment for these non-linear methods. However, practical use of CHO beyond research labs have been quite limited, mostly due to the strict requirement on a large number of repeated scans to ensure sufficient accuracy and precision in CHO computation and the lack of efficient and widely acceptable phantom-based method. In our previous work, we developed an efficient CHO model observer for accurate and precise measurement of low-contrast detectability with only 1-3 repeated scans on the most widely used ACR accreditation phantom. In this work, we applied this optimized CHO model observer to evaluating the low-contrast detectability of a deep learning-based reconstruction (DLIR) equipped on a GE Revolution scanner. The commercially available DLIR reconstruction method showed consistent increase in low-contrast detectability over the FBP and the IR method at routine dose levels, which suggests potential dose reduction to the FBP reconstruction by up to 27.5%.

Functional connectomes incorporating phase synchronization for the characterization and prediction of individual differences.

BACKGROUND: Functional connectomes have been proven to be able to predict an individual's traits, acting as a fingerprint. A majority of studies use the amplitude information of fMRI signals to construct the connectivity but it remains unknown whether phase synchronization can be incorporated for improved prediction of individual cognitive behaviors. METHODS: In this paper, we address the issue by extracting phase information from the fMRI time series with a phase locking approach, followed by the construction of functional connectomes. RESULTS: We first examine the identification and prediction performance using phase-based profiles in comparison with amplitude-based connectomes. We then combine both phase-based and amplitude-based connectivity to extract subject-specific information enabled by the phase synchronization. Results show that high individual identification rates (from 82.7% to 92.6%) can be achieved by phase-based connectomes. Phase-based connectivity offers unique information complementary to amplitude-based signals. Intra-network phase-locking appears more informative for individual prediction. In addition, phase synchronization can be used to predict cognitive behaviors. COMPARISON WITH EXISTING METHOD: The amplitude-based connectivity cannot capture the subject-specific information due to neural synchronization. The comparison with other phase-based methods has been involved in the discussion session. CONCLUSIONS: Our findings suggest that neural synchronization carries subject-specific information, which can be captured by phase locking value. The incorporation of phase information into connectomes presents a promising approach to understand each individual brain's uniqueness.

Germanium Metasurfaces with Lattice Kerker Effect in Near-Infrared Photodetectors.

In O-and C-band optical communications, Ge is a promising material for detecting optical signals that are encoded into electrical signals. Herein, we study 2D periodic Ge metasurfaces that support optically induced electric dipole and magnetic dipole lattice resonances. By overlapping Mie resonances and electric dipole lattice resonances, we realize the resonant lattice Kerker effect and achieve narrowband absorption. This effect was applied to the photodetector demonstrated in this study. The absorptance of the Ge nanoantenna arrays increased 6-fold compared to that of the unpatterned Ge films. In addition, the photocurrent in such Ge metasurface photodetectors increases by approximately 5 times compared with that in plane Ge film photodetectors by the interaction of these strong near-fields with semiconductors and the further transformation of the optical energy into electricity.

Simultaneous dual-contrast imaging using energy-integrating detector multi-energy CT: An in vivo feasibility study.

PURPOSE: To demonstrate the feasibility of simultaneous dual-contrast imaging in a large animal using a newly developed dual-source energy-integrating detector (EID)-based multi-energy computed tomography (MECT) system. METHODS: Two imaging tasks that may have potential clinical applications were investigated: head/neck (HN) CT angiography (CTA)/CT venography (CTV) with iodine and gadolinium, and small bowel imaging with iodine and bismuth in domestic swine. Dual-source X-ray beam configurations of 70 kV + Au120/Sn120 kV and 70 kV + Au140/Sn140 kV were used for the HN-CTA/CTV and small bowel imaging studies, respectively. A test bolus scan was performed for each study. The regions of interest (ROIs) in the carotid artery and jugular vein for HN-CTA/CTV imaging and abdominal aorta for small bowel imaging were used to determine the time-attenuation curves, based on which the timing for contrast injection and the CT scan was determined. In the HN-CTA/CTV study, an MECT scan was performed at the time point corresponding to the optimal arterial enhancement by iodine and the optimal venous enhancement by gadolinium. In the small bowel imaging study, an MECT scan was performed at the optimal time point to simultaneously capture the mesenteric arterial enhancement of iodine and the enteric enhancement of bismuth. Image-based material decomposition was performed to decompose different materials for each study. To quantitatively characterize contrast material separation and misclassification, two ROIs on left common carotid artery and left internal jugular vein in HN-CTA/CTV imaging and three ROIs on superior mesenteric artery, ileal lumen, and collapsed ileum (ileal wall) in small bowel imaging were placed to measure the mean concentration values and the standard deviations. RESULTS: In the HN-CTA/CTV study, common carotid arteries containing iodine and internal/external jugular veins containing gadolinium were clearly delineated from each other. Fine vessels such as cephalic veins and branches of external jugular veins were noticeable but clear visualization was hindered by image noise in gadolinium-specific (CTV) images, as reviewed by a neuroradiologist. In the small bowel imaging study, the mesenteric arteries and collapsed bowel wall containing iodine and the small bowel loops containing bismuth were clearly distinctive from each other in the iodine- and bismuth-specific images after material decomposition, as reviewed by an abdominal radiologist. Quantitative analyses showed that the misclassifications between the two contrast materials were less than 1.7 and 0.1 mg/ml for CTA/CTV and small bowel imaging studies, respectively. CONCLUSIONS: Feasibility of simultaneous CTA/CTV imaging in head and neck with iodine and gadolinium and simultaneous imaging of arterial and enteric phases of small bowel with iodine and bismuth, using a dual-source EID-MECT system, was demonstrated in a swine study. Compared to iodine and gadolinium in CTA/CTV, better delineation and classification of iodine and bismuth in small bowel imaging were achieved mainly due to wider separation between the corresponding two K-edge energies.

Enhanced phase retrieval via deep concatenation networks for in-line X-ray phase contrast imaging.

PURPOSE: In-line X-ray phase contrast imaging offers considerable additional information beyond that acquired from conventional absorption contrast X-ray imaging, showing promising potentials in clinical diagnosis, materials characterization and so on. Given the physically intractable factors tangled inside, conventional phase retrieval methods typically suffer from limited feasibility. A deep-learning-augmented reconstruction strategy is proposed to improve the phase retrieval in spatial resolution and noise compression. METHODS: The deep network is composed of a phase contrast refinement module and a phase retrieval module to stabilize and generalize the phase retrieval. The two modules are aggregated in a plug-and-play fashion with the final assembly finetuned using limited training data, essentially encouraging a semi-supervised training. Verification experiments were performed on simulated phase contrast images of histopathological images. The results were compared to those from conventional phase-attenuation duality method. RESULTS: The deep-learning-augmented reconstruction strategy increases structural similarity and peak signal-to-noise ratio of phase retrieval result by more than 8% and 30%, and reduces root mean squared error by 46% compared with conventional phase-attenuation duality method. CONCLUSIONS: The pilot study of deep learning deployment in in-line X-ray phase-contrast imaging exhibit advantages against conventional methods in terms of spatial resolution and noise robustness.

Predicting a Kind of Unusual Multiple-States Dimerization-Modes Transformation in Protein PD-L1 System by Computational Investigation and a Generalized Rate Theory.

The new cancer immunotherapy has been carried out with an almost messianic zeal, but its molecular basis remains unclear due to the complexity of programmed death ligand 1 (PD-L1) dimerization. In this study, a new and integral multiple dimerization-modes transformation process of PD-L1s (with a new PD-L1 dimerization mode and a new transformation path discovered) and the corresponding mechanism are predicted using theoretical and computational methods. The results of the state analysis show that 5 stable binding states exist in system. A generalized inter-state transformation rate (GITR) theory is also proposed in such multiple-states self-assembly system to explore the kinetic characteristics of inter-state transformation. A "drug insertion" path was identified as the dominant path of the PD-L1 dimerization-modes transformation. Above results can provide supports for both the relative drug design and other multiple-states self-assembly system from the theoretical chemistry perspective.