Structural Similarity Research Articles

Deep demosaicking convolution neural network and quantum wavelet transform-based image denoising.

Demosaicking is a popular scientific area that is being explored by a vast number of scientists. Current digital imaging technologies capture colour images with a single monochrome sensor. In addition, the colour images were captured using a sensor coupled with a Colour Filter Array (CFA). Furthermore, the demosaicking procedure is required to obtain a full-colour image. Image denoising and image demosaicking are the two important image restoration techniques, which have increased popularity in recent years. Finding a suitable strategy for multiple image restoration is critical for researchers. Hence, a deep learning (DL) based image denoising and image demosaicking is developed in this research. Moreover, the Autoregressive Circle Wave Optimization (ACWO) based Demosaicking Convolutional Neural Network (DMCNN) is designed for image demosaicking. The Quantum Wavelet Transform (QWT) is used in the image denoising process. Similarly, Quantum Wavelet Transform (QWT) is used to analyse the abrupt changes in the input image with noise. The transformed image is then subjected to a thresholding technique, which determines an appropriate threshold range. Once the threshold range has been determined, soft thresholding is applied to the resulting wavelet coefficients. After that, the extraction and reconstruction of the original image is carried out using the Inverse Quantum Wavelet Transform (IQWT). Finally, the fused image is created by combining the results of both processes using a weighted average. The denoised and demosaicked images are combined using the weighted average technique. Furthermore, the proposed QWT+DMCNN-ACWO model provided the ideal values of Peak signal-to-noise ratio (PSNR), Second derivative like measure of enhancement (SDME), Structural Similarity Index (SSIM), Figure of Merit (FOM) of 0.890, and computational time of 49.549 dB, 59.53 dB, 0.963, 0.890, and 0.571, respectively.

Antagonism of propofol anesthesia by alkyl-fluorobenzene derivatives

Despite their frequent use across many clinical settings, general anesthetics are medications with lethal side effects and no reversal agents. A fluorinated analogue of propofol has previously been shown to antagonize propofol anesthesia in tadpoles and zebrafish, but little further investigation of this class of molecules as anesthetic antagonists has been conducted. A 13-member library of alkyl-fluorobenzene derivatives was tested in an established behavioral model of anesthesia in zebrafish at 5 days post fertilization. These compounds were examined for their ability to antagonize propofol and two volatile anesthetics, as well as their interaction with the anesthetic-binding model protein apoferritin. Two compounds provided significant antagonism of propofol, and when combined, were synergistic, suggesting more than one antagonist sensitive target site. These compounds did not antagonize the volatile anesthetics, indicating some selectivity amongst general anesthetics. For the compounds with the most antagonistic potency, similarities in structure and binding to apoferritin may be suggestive of competitive antagonism; however, this was not supported by a Schild analysis. This is consistent with multiple targets contributing to general anesthesia, but whether these are physiologic antagonists or are antagonists at only some subset of the many anesthetic potential targets remains unclear, and will require additional investigation.

Patient-specific deep learning for 3D protoacoustic image reconstruction and dose verification in proton therapy.

Protoacoustic (PA) imaging has the potential to provide real-time 3D dose verification of proton therapy. However, PA images are susceptible to severe distortion due to limited angle acquisition. Our previous studies showed the potential of using deep learning to enhance PA images. As the model was trained using a limited number of patients' data, its efficacy was limited when applied to individualpatients. In this study, we developed a patient-specific deep learning method for protoacoustic imaging to improve the reconstruction quality of protoacoustic imaging and the accuracy of dose verification for individualpatients. Our method consists of two stages: in the first stage, a group model is trained from a diverse training set containing all patients, where a novel deep learning network is employed to directly reconstruct the initial pressure maps from the radiofrequency (RF) signals; in the second stage, we apply transfer learning on the pre-trained group model using patient-specific dataset derived from a novel data augmentation method to tune it into a patient-specific model. Raw PA signals were simulated based on computed tomography (CT) images and the pressure map derived from the planned dose. The reconstructed PA images were evaluated against the ground truth by using the root mean squared errors (RMSE), structural similarity index measure (SSIM) and gamma index on 10 specific prostate cancer patients. The significance level was evaluated by t-test with the p-value threshold of 0.05 compared with the results from the groupmodel. The patient-specific model achieved an average RMSE of 0.014 ( ), and an average SSIM of 0.981 ( ), out-performing the group model. Qualitative results also demonstrated that our patient-specific approach acquired better imaging quality with more details reconstructed when comparing with the group model. Dose verification achieved an average RMSE of 0.011 ( ), and an average SSIM of 0.995 ( ). Gamma index evaluation demonstrated a high agreement (97.4% [ ] and 97.9% [ ] for 1%/3 and 1%/5mm) between the predicted and the ground truth dose maps. Our approach approximately took 6 s to reconstruct PA images for each patient, demonstrating its feasibility for online 3D dose verification for prostate protontherapy. Our method demonstrated the feasibility of achieving 3D high-precision PA-based dose verification using patient-specific deep-learning approaches, which can potentially be used to guide the treatment to mitigate the impact of range uncertainty and improve the precision. Further studies are needed to validate the clinical impact of thetechnique.

DeCoGAN: MVCT image denoising via coupled generative adversarial network

Objective. In helical tomotherapy, image-guided radiotherapy employs megavoltage computed tomography (MVCT) for precise targeting. However, the high voltage of megavoltage radiation introduces substantial noise, significantly compromising MVCT image clarity. This study aims to enhance MVCT image quality using a deep learning-based denoising method. Approach. We propose an unpaired MVCT denoising network using a coupled generative adversarial network framework (DeCoGAN). Our approach assumes that a universal latent code within a shared latent space can reconstruct any given pair of images. By employing an encoder, we enforce this shared-latent space constraint, facilitating the conversion of low-quality (noisy) MVCT images into high-quality (denoised) counterparts. The network learns the joint distribution of images from both domains by leveraging samples from their respective marginal distributions, enhanced by adversarial training for effective denoising. Main Results. Compared to an analytical algorithm (BM3D) and three deep learning-based methods (RED-CNN, WGAN-VGG and CycleGAN), the proposed method excels in preserving image details and enhancing human visual perception by removing most noise and retaining structural features. Quantitative analysis demonstrates that our method achieves the highest peak signal-to-noise ratio and Structural Similarity Index Measurement values, indicating superior denoising performance. Significance. The proposed DeCoGAN method shows remarkable MVCT denoising performance, making it a promising tool in the field of radiation therapy.

Multi-planar dual adversarial network based on dynamic 3D features for MRI-CT head and neck image synthesis.

Head and neck radiotherapy planning requires electron densities from different tissues for dose calculation.
Dose calculation from imaging modalities such as MRI remains an unsolved problem since this imaging modality does not provide information about the density of electrons. 
Approach. We propose a generative adversarial network (GAN) approach that synthesizes CT (sCT) images from T1-weighted MRI acquisitions in head and neck cancer patients. Our contribution is to exploit new features that are relevant for improving multimodal image synthesis, and thus improving the quality of the generated CT images. More precisely, we propose a Dual branch generator based on the U-Net architecture and on an augmented multi-planar branch. The augmented branch learns specific 3D dynamic features, which describe the dynamic image shape variations and are extracted from different view-points of the volumetric input MRI. The architecture of the proposed model relies on an end-to-end convolutional U-Net embedding network.
Results. The proposed model achieves a mean absolute error (MAE) of $18.76 (5.167)$ in the target Hounsfield unit (HU) space on sagittal head and neck patients, with a mean structural similarity (MSSIM) of $0.95 (0.09)$ and a Frechet inception distance (FID) of $145.60 (8.38)$. The model yields a MAE of $26.83 (8.27)$ to generate specific primary tumor regions on axial patient acquisitions, with a Dice score of $0.73 (0.06)$ and a FID distance equal to $122.58 (7.55)$. The improvement of our model over other state-of-the-art GAN approaches is of $3.8\%$, on a tumor test set. On both sagittal and axial acquisitions, the model yields the best peak signal-to-noise ratio (PSNRs) of $27.89 (2.22)$ and $26.08 (2.95)$ to synthesize MRI from CT input.
Significance. The proposed model synthesizes both sagittal and axial CT tumor images, used for radiotherapy treatment planning in head and neck cancer cases. The performance analysis across different imaging metrics and under different evaluation strategies demonstrates the effectiveness of our dual CT synthesis model to produce high quality sCT images compared to other state-of-the-art approaches. Our model could improve clinical tumor analysis, in which a further clinical validation remains to be explored.

Synthetic CT generation for pelvic cases based on deep learning in multi-center datasets

Background and purposeTo investigate the feasibility of synthesizing computed tomography (CT) images from magnetic resonance (MR) images in multi-center datasets using generative adversarial networks (GANs) for rectal cancer MR-only radiotherapy.Materials and methodsConventional T2-weighted MR and CT images were acquired from 90 rectal cancer patients at Peking University People’s Hospital and 19 patients in public datasets. This study proposed a new model combining contrastive learning loss and consistency regularization loss to enhance the generalization of model for multi-center pelvic MRI-to-CT synthesis. The CT-to-sCT image similarity was evaluated by computing the mean absolute error (MAE), peak signal-to-noise ratio (SNRpeak), structural similarity index (SSIM) and Generalization Performance (GP). The dosimetric accuracy of synthetic CT was verified against CT-based dose distributions for the photon plan. Relative dose differences in the planning target volume and organs at risk were computed.ResultsOur model presented excellent generalization with a GP of 0.911 on unseen datasets and outperformed the plain CycleGAN, where MAE decreased from 47.129 to 42.344, SNRpeak improved from 25.167 to 26.979, SSIM increased from 0.978 to 0.992. The dosimetric analysis demonstrated that most of the relative differences in dose and volume histogram (DVH) indicators between synthetic CT and real CT were less than 1%.ConclusionThe proposed model can generate accurate synthetic CT in multi-center datasets from T2w-MR images. Most dosimetric differences were within clinically acceptable criteria for photon radiotherapy, demonstrating the feasibility of an MRI-only workflow for patients with rectal cancer.

The Relationship between Exposome and Microbiome

Currently, exposome studies include a raft of different monitoring tools, including remote sensors, smartphones, omics analyses, distributed lag models, etc. The similarity in structure between the exposome and the microbiota plus their functions led us to pose three pertinent questions from this viewpoint, looking at the actual relationship between the exposome and the microbiota. In terms of the exposome, a bistable equilibrium between health and disease depends on constantly dealing with an ever-changing totality of exposures that together shape an individual from conception to death. Regarding scientific knowledge, the exposome is still lagging in certain areas, like the importance of microorganisms in the equation. The human microbiome is defined as an aggregate assemblage of gut commensals that are hosted by our surfaces related to the external environment. Commensals’ resistance to a variety of environmental exposures, such as antibiotic administration, confirms that a layer of these organisms is protected within the host. The exposome is a conceptual framework defined as the environmental component of the science-inspired systems ideology that shifts from a specificity-based medical approach to reasoning in terms of complexity. A parallel concept in population health research and precision public health is the human flourishing index, which aims to account for the numerous environmental factors that affect individual and population well-being beyond ambient pollution.

Protein Retrieval via Integrative Molecular Ensembles (PRIME) through Extended Similarity Indices.

Molecular dynamics (MD) simulations are ideally suited to describe conformational ensembles of biomolecules such as proteins and nucleic acids. Microsecond-long simulations are now routine, facilitated by the emergence of graphical processing units. Clustering, which groups objects based on structural similarity, is typically used to process ensembles, leading to different states, their populations, and the identification of representative structures. A popular pipeline combines hierarchical clustering for clustering and selecting the cluster centroid as representative of the cluster. Here, we propose to improve on this approach, by developing a module-Protein Retrieval via Integrative Molecular Ensembles (PRIME), that consists of tools to improve the prediction of the representative in the most populated cluster using extended continuous similarity. PRIME is integrated with our Molecular Dynamics Analysis with N-ary Clustering Ensembles (MDANCE) package and can be used as a postprocessing tool for arbitrary clustering algorithms, compatible with several MD suites. PRIME predictions produced structures that when aligned to the experimental structure were better superposed (lower RMSD). A further benefit of PRIME is its linear scaling─rather than the traditional O(N2) traditionally associated with comparisons of elements in a set.

Light-induced insulator–metal transition in Sr2IrO4 reveals the nature of the insulating ground state

Sr2IrO4 has attracted considerable attention due to its structural and electronic similarities to La2CuO4, the parent compound of high-Tc superconducting cuprates. It was proposed as a strong spin-orbit-coupled Jeff = 1/2 Mott insulator, but the Mott nature of its insulating ground state has not been conclusively established. Here, we use ultrafast laser pulses to realize an insulator-metal transition in Sr2IrO4 and probe the resulting dynamics using time- and angle-resolved photoemission spectroscopy. We observe a gap closure and the formation of weakly renormalized electronic bands in the gap region. Comparing these observations to the expected temperature and doping evolution of Mott gaps and Hubbard bands provides clear evidence that the insulating state does not originate from Mott correlations. We instead propose a correlated band insulator picture, where antiferromagnetic correlations play a key role in the gap opening. More broadly, our results demonstrate that energy-momentum-resolved nonequilibrium dynamics can be used to clarify the nature of equilibrium states in correlated materials.

Bio‐based semi‐aromatic waterborne PIEK‐C sizing agent to construct a robust interface for CF/PEEK composites

AbstractDespite the impressing advances on sizing agents for CF/PEEK composites, the aromatic backbone of the polymer precursors utilized in the vast majority of reports compromises the dispersity and ductility of the slurry, subsequently resulting in inferior overall performance of the composites. Meanwhile, most resins employed for sizing agents in existing reports are based on petroleum resources. Herein, a bio‐based waterborne sizing agent utilizing semi‐aromatic poly ether ketones (PIEK‐C) derived from isosorbide and phenolphthalein has been disclosed, featured by its salutary dispersity, exceptional thermal stability, higher glass transition temperatures, and broader applicability. Due to the structural similarity between the sizing and resin matrix, the protocol endowed CF/PEEK composites with ameliorated overall performance, addressing issues related to inadequate adhesion of CF with PEEK resin, as well as environmental pollution commonly associated with existing commercial epoxy and solvent‐based sizing agents. The Tg and T5% of PIEK‐C‐5050 were 205.08 and 427.16°C, respectively. The particle size range of the PIEK‐C sizing agents spanned from 66.32 to 84.63 nm. The flexural strength and interlaminar shear strength of the composites experienced a respective 993.92 MPa (30.73%) and 83.32 MPa (36.97%) increase compared to untreated CF/PEEK composites following sizing treatment with PIEK‐C‐5050, underscoring expedient intermolecular interactions based on comparable solubility and π‐π stacking effects between the PEEK and PIEK‐C components. This study introduced an efficient and eco‐friendly approach to enhance the interfacial properties of CF/PEEK composites.Highlights PIEK‐C resin is employed for the first time to prepare carbon fiber sizing agents. The PIEK‐C sizing agent significantly fortified the surface energy and wettability of carbon fibers. The CF/PEEK composites after treatment with PIEK‐C sizing agent showed excellent heat resistance. The ILSS and bending strength of the composites were significantly increased by 36.97% and 30.73%, respectively.

Reconstructing Cancellous Bone From Down-Sampled Optical-Resolution Photoacoustic Microscopy Images With Deep Learning

ObjectiveBone diseases deteriorate the microstructure of bone tissue. Optical-resolution photoacoustic microscopy (OR-PAM) enables high spatial resolution of imaging bone tissues. However, the spatiotemporal trade-off limits the application of OR-PAM. The purpose of this study was to improve the quality of OR-PAM images without sacrificing temporal resolution. MethodsIn this study, we proposed the Photoacoustic Dense Attention U-Net (PADA U-Net) model, which was used for reconstructing full-scanning images from under-sampled images. Thereby, this approach breaks the trade-off between imaging speed and spatial resolution. ResultsThe proposed method was validated on resolution test targets and bovine cancellous bone samples to demonstrate the capability of PADA U-Net in recovering full-scanning images from under-sampled OR-PAM images. With a down-sampling ratio of [4, 1], compared to bilinear interpolation, the Peak Signal-to-Noise Ratio and Structural Similarity Index Measure values (averaged over the test set of bovine cancellous bone) of the PADA U-Net were improved by 2.325 dB and 0.117, respectively. ConclusionThe results demonstrate that the PADA U-Net model reconstructed the OR-PAM images well with different levels of sparsity. Our proposed method can further facilitate early diagnosis and treatment of bone diseases using OR-PAM.

DiffPlate: A Diffusion Model for Super-Resolution of License Plate Images

License plate recognition is a pivotal challenge in surveillance applications, predominantly due to the low resolution and diminutive size of license plates, which impairs recognition accuracy. The advent of AI-based super-resolution techniques offers a promising avenue to ameliorate the resolution of such images. Despite the deployment of various super-resolution methodologies, including Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs), the quest for satisfactory outcomes in license plate image enhancement persists. This paper introduces “DiffPlate”, a novel Diffusion Model specifically tailored for license plate super-resolution. Leveraging the unprecedented capabilities of Diffusion Models in image generation, DiffPlate is meticulously trained on a dataset comprising low-resolution and high-resolution pairs of Saudi license plates, curated for our surveillance application. Our empirical analysis substantiates that DiffPlate markedly eclipses state-of-the-art alternatives such as SwinIR and ESRGAN, evidencing a 26.47% and 37.32% enhancement in Peak Signal-to-Noise Ratio (PSNR) against these benchmarks, respectively. Furthermore, DiffPlate achieves superior performance in terms of Structural Similarity Index (SSIM), with a 4.88% and 16.21% improvement over SwinIR and ESRGAN, respectively. Human evaluative studies further corroborate that images refined by DiffPlate were preferred 92% more frequently compared to those processed by other algorithms. Through DiffPlate, we present a new solution to the license plate super-resolution challenge, demonstrating significant potential for adoption in real-world surveillance systems.

Implementation of two-step gradual reset scheme for enhancing state uniformity of 2D hBN-based memristors for image processing

In-memory computing facilitates efficient parallel computing based on the programmable memristor crossbar array. Proficient hardware image processing can be implemented by utilizing the analog vector-matrix operation with multiple memory states of the nonvolatile memristor in the crossbar array. Among various materials, 2D materials are great candidates for a switching layer of nonvolatile memristors, demonstrating low-power operation and electrical tunability through their remarkable physical and electrical properties. However, the intrinsic device-to-device (D2D) variation of memristors within the crossbar array can degrade the accuracy and performance of in-memory computing. Here, we demonstrate hardware image processing using the fabricated 2D hexagonal boron nitride-based memristor to investigate the effects of D2D variation on the hardware convolution process. The image quality is evaluated by peak-signal-to-noise ratio, structural similarity index measure, and Pratt’s figure of merit and analyzed according to D2D variations. Then, we propose a novel two-step gradual reset programming scheme to enhance the conductance uniformity of multiple states of devices. This approach can enhance the D2D variation and demonstrate the improved quality of the image processing result. We believe that this result suggests the precise tuning method to realize high-performance in-memory computing.

An effective video inpainting technique using morphological Haar wavelet transform with krill herd based criminisi algorithm

In recent times, video inpainting techniques have intended to fill the missing areas or gaps in a video by utilizing known pixels. The variety in brightness or difference of the patches causes the state-of-the-art video inpainting techniques to exhibit high computation complexity and create seams in the target areas. To resolve these issues, this paper introduces a novel video inpainting technique that employs the Morphological Haar Wavelet Transform combined with the Krill Herd based Criminisi algorithm (MHWT-KHCA) to address the challenges of high computational demand and visible seam artifacts in current inpainting practices. The proposed MHWT-KHCA algorithm strategically reduces computation times and enhances the seamlessness of the inpainting process in videos. Through a series of experiments, the technique is validated against standard metrics such as peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM), where it demonstrates superior performance compared to existing methods. Additionally, the paper outlines potential real-world applications ranging from video restoration to real-time surveillance enhancement, highlighting the technique’s versatility and effectiveness. Future research directions include optimizing the algorithm for diverse video formats and integrating machine learning models to advance its capabilities further.

Tamper Detection and Self-recovery in a Visual Secret Sharing based Security Mechanism for Medical Records.

Medical records contain highly sensitive patient information. These medical records are significant for better research, diagnosis, and treatment. However, ensuring secure medical records storage is paramount to protect patient confidentiality, integrity, and privacy. Conventional methods involve encrypting and storing medical records in third-party clouds. Such storage enables convenient access and remote consultation. This cloud storage poses single-point attack risks and may lead to erroneous diagnoses and treatment. To address this, a novel (n,n)VSS scheme is proposed with data embedding, permutation ordered binary number system, tamper detection, and self-recovery mechanism. This approach enables the reconstruction of medical records even in the case of tampering. The tamper detection algorithm ensures data integrity. Simulation results demonstrate the superiority of proposed method in terms of security and reconstruction quality. Here, security analysis is done by considering attacks such as brute force, differential, and tampering attacks. Similarly, the reconstruction quality is evaluated using various human visual system parameters. The results show that the proposed technique provides a lower bit error rate ( ≈ 0), high average peak signal-to-noise ratio ( ≈ 35 dB), high structured similarity ( ≈ 1), high text embedding rate ( ≈ 0.7 BPP), and lossless reconstruction in the case of attacks.

Preparation of imidazolium ionic liquid functionalized paper membrane for selective extraction of caffeic acid and its structural and functional analogues from Taraxaci Herba.

In the search for pharmaceutically active compounds from natural products, it is crucial and challenging to develop separation or purification methods that target not only structurally similar compounds but also those with specific pharmaceutical functions. The adsorption-based method is widely employed in this field and holds potential for this application, given the diverse range of functional monomers that can be chosen based on structural or functional selectivity. In this work, an imidazolium ionic liquid (IL) modified paper membrane was synthesized via microwave reaction. Caffeic acid (CA), with potential interactions with imidazolium IL and a representative component of phenolic acids in Taraxaci Herba, was chosen as a target compound. After optimization of synthesis and extraction parameters, the resulting extraction membrane could be used to quantitatively analyze CA at ng/ml level, and to extract CA's analogues from the sample matrix. Cheminformatics confirmed the presence of structural and functional similarity among these extracted compounds. This study offers a novel approach to preparing a readily synthesized extraction membrane capable of isolating compounds with structural and functional analogies, as well as developing a membrane solid-phase extraction-based analytical method for natural products.

Mask-guided dual-perception generative adversarial network for synthesizing complex maize diseased leaves to augment datasets

In practice, acquiring and annotating data in specialized domains can be costly, thereby constraining the performance and applicability of deep learning. Utilizing generative models to synthesize data proves to be an effective augmentation technique. Therefore, this research proposes a diseased leaf generation pipeline to diversify the maize disease datasets. We introduce the Dual-Perception Cycle-Consistent Generative Adversarial Network (DP-CycleGAN). During training, incorporating our proposed Structure Perception (SP) loss and Texture Perception (TP) loss functions. These losses guide the model's attention areas through activation reconstruction and mask mechanisms, thereby improving the overall perceptual quality of the generated images and the realism of the disease lesions. We constructed a maize leaf mixed disease dataset to simulate the complex conditions of real-world disease occurrence. Experimental results show that the DP-CycleGAN generates higher-quality and more realistic diseased leaf images. Compared to CycleGAN and state-of-the-art method, DP-CycleGAN shows a 29.6% and 15.7% reduction in Fréchet Inception Distance (FID) scores and a 125.3% and 61.5% increase in Structural Similarity (SSIM) values, respectively. Simultaneously, by incorporating synthetic data during training, our approach significantly enhances the performance of the recognition model in scenarios of both data abundance and scarcity, with improvement rates exceeding two times those of existing state-of-the-art methods. This contributes to the application of Artificial Intelligence (AI) in agricultural production practices.

Enhancements in image quality and block detection performance for Reinforced Soil-Retaining Walls under various illuminance conditions

To ensure continuous monitoring of reinforced soil-retaining walls (RSWs) even under low-illuminance conditions, such as during the night, it is imperative to evaluate the performance of deep learning-based detection. In this study, we constructed a laboratory RSW model and generated a dataset with varying illuminance levels to assess the impact of image enhancement and block detection. Various image enhancement methods were applied to improve image quality and evaluate their effect on deep learning. RGB optimization (RO) was proposed to optimize RGB intensity and compared with gamma correction, histogram equalization, and low-light image enhancement with illumination map estimation. RO demonstrated outstanding image enhancement performance, as evidenced by lightness order error, peak signal-to-noise ratio, and structural similarity index measure, ensuring high image quality. The trained RO model using Mask R-CNN exhibited excellent accuracy, recall, and F1 score, delivering remarkable detection performance under low illuminance conditions, resulting in a 7.44 % improvement in the F1 score. Image enhancement techniques that maintain similarity, such as lightness order error and structural similarity, across varying illuminance conditions contribute to enhancing the block detection performance of Mask R-CNNs.

Quiescent frame, contrast-enhanced coronary magnetic resonance angiography reconstructed using limited number of physiologic frames from 5D free-running acquisitions

Background5D, free-running imaging resolves sets of 3D whole-heart images in both cardiac and respiratory dimensions. In an application such as coronary imaging when a single, static image is of interest, computationally expensive offline iterative reconstruction is still needed to compute the multiple 3D datasets. PurposeEvaluate how the number of physiologic bins included in the reconstruction affects the computational cost and resulting image quality of a single, static volume reconstruction. Study typeRetrospective. Subjects15 pediatric patients following Ferumoxytol infusion (4 mg/kg). Field strength/Sequence1.5 T/Ungated 5D free-running GRE sequence. AssessmentThe raw data of each subject were binned and reconstructed into a 5D (x-y-z-cardiac-respiratory) images. 1, 3, 5, 7, and 9 bins adjacent to both sides of the retrospectively determined cardiac resting phase and 1, 3 bins adjacent to the end-expiration phase are used for limited frame reconstructions. The static volume within each limited reconstruction was compared with the corresponding full 5D reconstruction using the structural similarity index measure (SSIM). A non-linear regression model was used to fit SSIM with the percentage of data used compared to full reconstruction (% data). A linear regression model was used to fit computation time with % raw data used. Coronary artery sharpness is measured on each limited reconstructed images to determine the minimal number of cardiac and respiratory bins needed to preserve image quality. Statistical testsThe coefficient of determination (R2) is computed for each regression model. ResultsThe % of data used in the reconstruction was linearly related to the computational time (R2 = 0.99). The SSIM of the static image from the limited reconstructions is non-linearly related with the % of data used (R2 = 0.85). Over the 15 patients, the model showed SSIM of 0.9 with 22% of data, and SSIM of 0.95 with 45% of data. The coronary artery sharpness of images reconstructed using no less than 5 cardiac and all respiratory phases is not significantly different from the full reconstructed images using all cardiac and respiratory bins. Data conclusionReconstruction using only a limited number of acquired physiological states can linearly reduce the computational cost while preserving similarity to the full reconstruction image. It is suggested to use no less than 5 cardiac and all respiratory phases in the limited reconstruction to best preserve the original quality seen on the full reconstructed images.

Self-Replication Without Hydrogen-Bonds: An Exobiotic Design.

Life on Earth uses DNA as the central template for self-replication, genetic encoding, and information transfer. However, there are no physical laws precluding life's existence elsewhere in space, and alternative life forms may not need DNA. In the search for exobiology, knowing what to look for as a biosignature remains a challenge-especially if it is not from the obvious list of biologic building blocks. Clues from chemicals recently discovered on Mars and in the Taurus Molecular Cloud 1 (TMC-1), show that intriguing organic compounds exist beyond Earth, which could provide a starting point for unconventional exobiotic designs. Here we present a new self-replicating system with structural similarities to recently discovered compounds on Mars and TMC-1. Rather than using DNA's hydrogen-bonding motif for reliable base-paring, our design employs sulfur-nitrogen interactions to selectively template unique benzothiadiazole units in sequence. We synthesized and studied two versions of this system, one reversible and the other irreversible, and found experimental evidence of self-replication in d-chloroform solvent. These results are part of a larger pursuit in our lab for developing a basis for a potential exobiological system using starting blocks closely related to these cosmic compounds.