Image Reconstruction Research Articles

Metal artifact reduction method based on single spectral CT (MARSS).

For patients with metal implants, computed tomography (CT) imaging results suffer from metal artifacts, which seriously affect image evaluation and even lead to misdiagnosis. Because spectral CT technology has the advantage of quantitative imaging, basis material decomposition, and so on, the current metal artifact reduction methods are utilizing spectral information to reduce metal artifacts with good results. However, they usually require projection data from multiple spectra or energy-windows, which is difficult to realize in conventional CT. To satisfy the status quo, the aim of this work is to propose a metal artifact reduction (MAR) method based on single spectral CT (MARSS). By incorporating prior information, the average density of some base materials, and a constrained image reconstruction model is established. It forces the solution spaces of the materials to be discrete and finite, making the model easier tosolve. The MARSS method uses the idea of discrete tomography to establish a constrained reconstruction model. By incorporating priori knowledge, the constraint forces the solution spaces of some materials to be discrete, which can effectively downsize the solution space and reduce the ill-posedness of this problem. Then, an iteration algorithm is developed to solve this model. This algorithm iterates alternately between reconstruction and discretization. It ensures that the solution spaces are discrete while the polychromatic projection of the reconstructed image converges to that of the scannedobject. The MRASS method significantly reduces artifacts and restores structures near metal to a large extent. Unlike the comparison MAR methods, it effectively prevents the introduction of new artifacts and distortion of thestructure. The MARSS method can achieve MAR based on single spectral CT. Subjective and quantitative evaluation of the results show that the method significantly improves image quality compared to competingmethods.

Disentangled Lifespan Synthesis via Transformer‐Based Nonlinear Regression

AbstractLifespan face age transformation aims to generate facial images that accurately depict an individual's appearance at different age stages. This task is highly challenging due to the need for reasonable changes in facial features while preserving identity characteristics. Existing methods tend to synthesize unsatisfactory results, such as entangled facial attributes and low identity preservation, especially when dealing with large age gaps. Furthermore, over‐manipulating the style vector may deviate it from the latent space and damage image quality. To address these issues, this paper introduces a novel nonlinear regression model‐Disentangled Lifespan face Aging (DL‐Aging) to achieve high‐quality age transformation images. Specifically, we propose an age modulation encoder to extract age‐related multi‐scale facial features as key and value, and use the reconstructed style vector of the image as the query. The multi‐head cross‐attention in the W+ space is utilized to update the query for aging image reconstruction iteratively. This nonlinear transformation enables the model to learn a more disentangled mode of transformation, which is crucial for alleviating facial attribute entanglement. Additionally, we introduce a W+ space age regularization term to prevent excessive manipulation of the style vector and ensure it remains within the W+ space during transformation, thereby improving generation quality and aging accuracy. Extensive qualitative and quantitative experiments demonstrate that the proposed DL‐Aging outperforms state‐of‐the‐art methods regarding aging accuracy, image quality, attribute disentanglement, and identity preservation, especially for large age gaps.

Reconstruction of reservoir rock using attention-based convolutional recurrent neural network

The digital reconstruction of reservoir rock or porous media is important as it enables us to visualize and explore their real internal structures. The reservoir rocks (such as sandstone and carbonate) contain both spatial and temporal characteristics, which pose a big challenge in their characterization through routine core analysis or x-ray microcomputer tomography. While x-ray micro-computed tomography gives us three-dimensional images of the porous media, it is often impossible to quantify the variability of the pore, grains, structure, and orientation experimentally. Recently, machine learning has successfully demonstrated the reconstruction ability of reservoir rock images or any porous media. These reservoir rock images are crucial for the digital characterization of the reservoir. We propose a novel algorithm consisting of the convolutional neural network, an attention mechanism, and a recurrent neural network for the reconstruction of reservoir rock or porous media images. The attention-based convolutional recurrent neural network (ACRNN) can reconstruct a representative sample of reservoir rocks. The reconstructed image quality was checked by comparing them with the original Parker sandstone, Leopard sandstone, carbonate shale, Berea sandstone, and sandy medium images. We evaluated the reconstruction by measuring pore and throat properties, two-point probability function, and structural similarity index. Results show that ACRNN can reconstruct reservoir rock or porous media of different scales with approximately the same geometrical, statistical, and topological parameters of the reservoir rock images. This deep learning method is computationally efficient, fast, and reliable for synthetic image realizations. The model was trained and validated on real images, and the reconstructed images showed excellent concordance with the real images having almost the same pore and grain structures. The deep learning-based digital rock reconstruction of reservoir rock or porous media images can aid in rapid image generation to better understand reservoir rock or subsurface formation.

Cascaded hybrid convolutional autoencoder network for spectral-spatial nonlinear hyperspectral unmixing

ABSTRACT Due to the complex interaction of photons between materials, linear hyperspectral unmixing (HU) is limited in real scenarios, making nonlinear unmixing a promising alternative. With the advancement of deep learning (DL), nonlinear unmixing methods based on the convolutional autoencoder (CAE) have gained considerable traction in HU. However, these unmixing methods struggle to integrate spectral and spatial information while reducing the loss of material details during the unmixing process. Therefore, we propose a cascaded hybrid CAE nonlinear unmixing network, called CHCANet, which effectively leverages convolutional combinations to deeply explore the spectral-spatial information from hyperspectral data and preserve the material details through self-perception. Specifically, each CAE in CHCANet combines 1-D and 2-D convolutions, fully utilizing the flexibility and simplicity of 1-D convolutions to capture spectral features and the spatial correlation handling capability of the 2-D convolution. Moreover, we apply the self-perception mechanism to the nonlinear HU task, which can establish the cycle consistency of the network, strengthen mutual connections between encoders, and effectively preserve high-level semantic information. Following this, the optimized self-perception loss further enhances CHCANet’s perception capability of nonlinear components and strengthens the connection between the decoder directly associated with image reconstruction. Extensive experiments on synthetic and real datasets demonstrate the effectiveness of CHCANet and show excellent competitiveness compared to state-of-the-art unmixing methods.

Dual-Energy CT as a Well-Established CT Modality to Reduce Contrast Media Amount: A Systematic Review from the Computed Tomography Subspecialty Section of the Italian Society of Radiology

Background: Our study aims to provide an overview of existing evidence regarding the image quality of dual-energy CT (DECT) employing reduced contrast media (CM) volumes, in comparison to single-energy CT (SECT) with standard CM loads. The advantages, indications, and possible applications of DECT were investigated from the perspective of providing better patient care, minimizing CM volume and managing CM shortage. Methods: In this systematic review (PRISMA methodology), PubMed and WOS were searched from January 2010 to January 2023 by two independent reviewers. The scan and CM characteristics, radiation dose, and results of quantitative (contrast to noise ratio, CNR, and signal to noise ratio, SNR) and qualitative assessment of image quality were collected. Sixty non-duplicated records eligible for full-text screening were examined. Results: Finally, 22 articles (1818 patients) were included. The average CM reduction with DECT ranged between 43.4 ± 11%. Despite the wide variability in CT scan protocols, no differences were found in radiation doses between DECT and SECT. Conclusions: DECT scanners allow the employment of lower CM volumes with equal or better image quality evaluated by quantitative and qualitative analyses and similar dose radiation compared to SECT. Using image reconstructions at low monochromatic energy levels, DECT increases iodine conspicuity and attenuation contributing to CM containment measures.

Study on terrain acquisition and processing technology of BDS-3 auxiliary mountain highway

The operation mode of UAV equipped with LiDAR can significantly reduce the efficiency and cost of highway terrain acquisition. At present, due to the complicated terrain and weak signal of mountain road, it is difficult to collect terrain data. In view of the above problems, this paper puts forward the technology of BDS assisted terrain acquisition and processing on complex mountain roads. This paper describes in detail the new signal unfuzzy acquisition and tracking algorithm and multi-antenna positioning and attitude determination technology and equipment of BDS-3 mountain highway, through millimeter wave Lidar altimetry technology, imitation ground flight, air-ground multi-source data combined with air three-direction method and other technologies. To realize efficient 3D collection and 3D reconstruction of massive original images of terrain of complex mountain traffic lines in the range of 100 km. Finally, a case study of 100 km mountain road is taken to verify the effectiveness of the method.

Deep learning super-resolution reconstruction for fast and high-quality cine cardiovascular magnetic resonance.

To compare standard-resolution balanced steady-state free precession (bSSFP) cine images with cine images acquired at low resolution but reconstructed with a deep learning (DL) super-resolution algorithm. Cine cardiovascular magnetic resonance (CMR) datasets (short-axis and 4-chamber views) were prospectively acquired in healthy volunteers and patients at normal (cineNR: 1.89 × 1.96 mm2, reconstructed at 1.04 × 1.04 mm2) and at a low-resolution (2.98 × 3.00 mm2, reconstructed at 1.04 × 1.04 mm2). Low-resolution images were reconstructed using compressed sensing DL denoising and resolution upscaling (cineDL). Left ventricular ejection fraction (LVEF), end-diastolic volume index (LVEDVi), and strain were assessed. Apparent signal-to-noise (aSNR) and contrast-to-noise ratios (aCNR) were calculated. Subjective image quality was assessed on a 5-point Likert scale. Student's paired t-test, Wilcoxon matched-pairs signed-rank-test, and intraclass correlation coefficient (ICC) were used for statistical analysis. Thirty participants were analyzed (37 ± 16 years; 20 healthy volunteers and 10 patients). Short-axis views whole-stack acquisition duration of cineDL was shorter than cineNR (57.5 ± 8.7 vs 98.7 ± 12.4 s; p < 0.0001). No differences were noted for: LVEF (59 ± 7 vs 59 ± 7%; ICC: 0.95 [95% confidence interval: 0.94, 0.99]; p = 0.17), LVEDVi (85.0 ± 13.5 vs 84.4 ± 13.7 mL/m2; ICC: 0.99 [0.98, 0.99]; p = 0.12), longitudinal strain (-19.5 ± 4.3 vs -19.8 ± 3.9%; ICC: 0.94 [0.88, 0.97]; p = 0.52), short-axis aSNR (81 ± 49 vs 69 ± 38; p = 0.32), aCNR (53 ± 31 vs 45 ± 27; p = 0.33), or subjective image quality (5.0 [IQR 4.9, 5.0] vs 5.0 [IQR 4.7, 5.0]; p = 0.99). Deep-learning reconstruction of cine images acquired at a lower spatial resolution led to a decrease in acquisition times of 42% with shorter breath-holds without affecting volumetric results or image quality. Question Cine CMR acquisitions are time-intensive and vulnerable to artifacts. Findings Low-resolution upscaled reconstructions using DL super-resolution decreased acquisition times by 35-42% without a significant difference in volumetric results or subjective image quality. Clinical relevance DL super-resolution reconstructions of bSSFP cine images acquired at a lower spatial resolution reduce acquisition times while preserving diagnostic accuracy, improving the clinical feasibility of cine imaging by decreasing breath hold duration.

Performance Study of an MRI Motion-Compensated Reconstruction Program on Intel CPUs, AMD EPYC CPUs, and NVIDIA GPUs

Motion-compensated image reconstruction enables new clinical applications of Magnetic Resonance Imaging (MRI), but it relies on computationally intensive algorithms. This study focuses on the Generalized Reconstruction by Inversion of Coupled Systems (GRICS) program, applied to the reconstruction of 3D images in cases of non-rigid or rigid motion. It uses hybrid parallelization with the MPI (Message Passing Interface) and OpenMP (Open Multi-Processing). For clinical integration, the GRICS needs to efficiently harness the computational resources of compute nodes. We aim to improve the GRICS’s performance without any code modification. This work presents a performance study of GRICS on two CPU architectures: Intel Xeon Gold and AMD EPYC. The roofline model is used to study the software–hardware interaction and quantify the code’s performance. For CPU–GPU comparison purposes, we propose a preliminary MATLAB–GPU implementation of the GRICS’s reconstruction kernel. We establish the roofline model of the kernel on two NVIDIA GPU architectures: Quadro RTX 5000 and A100. After the performance study, we propose some optimization patterns for the code’s execution on CPUs, first considering only the OpenMP implementation using thread binding and affinity and appropriate architecture-compilation flags and then looking for the optimal combination of MPI processes and OpenMP threads in the case of the hybrid MPI–OpenMP implementation. The results show that the GRICS performed well on the AMD EPYC CPUs, with an architectural efficiency of 52%. The kernel’s execution was fast on the NVIDIA A100 GPU, but the roofline model reported low architectural efficiency and utilization.

PET image reconstruction using weighted nuclear norm maximization and deep learning prior

The ill-posed Positron emission tomography (PET) reconstruction problem usually results in limited resolution and significant noise. Recently, deep neural networks have been incorporated into PET iterative reconstruction framework to improve the image quality. In this paper, we propose a new neural network-based iterative reconstruction method by using weighted nuclear norm (WNN) maximization, which aims to recover the image details in the reconstruction process. The novelty of our method is the application of WNN maximization rather than WNN minimization in PET image reconstruction. Meanwhile, a neural network is used to control the noise originated from WNN maximization. Our method is evaluated on simulated and clinical datasets. The simulation results show that the proposed approach outperforms state-of-the-art neural network-based iterative methods by achieving the best contrast/noise tradeoff with a remarkable contrast improvement on the lesion contrast recovery. The study on clinical datasets also demonstrates that our method can recover lesions of different sizes while suppressing noise in various low-dose PET image reconstruction tasks. Our code is available athttps://github.com/Kuangxd/PETReconstruction.

PICASSO: a universal brain phantom for positron emission tomography based on the activity painting technique

Objective. This study presents a universal phantom for positron emission tomography (PET) that allows arbitrary static and dynamic activity distributions of various complexities to be generated using a single PET acquisition.Approach. We collected a high-statistics dataset (with a total of 22.4 × 109prompt coincidences and an event density of 2.75 × 106events mm-3) by raster-scanning a single plane with a22Na point source mounted on a robotic arm in the field-of-view of the uEXPLORER PET/CT scanner. The source position was determined from the reconstructed dynamic frames. Uniquely, true coincidences were separated from scattered and random events based on the distance between their line-of-response and the known source location. Finally, we randomly sampled the dataset to generate the desired activity distributions modeling several different phantoms.Main results. Overall, the target and the reconstructed phantom images had good agreement. The analysis of a simple geometric distribution showed high quantitative accuracy of the phantom, with mean error of <-3.0% relative to the ground truth for activity concentrations ranging from 5.3 to 47.7 kBq ml-1. The model of a high-resolution18F-fluorodeoxyglucose distribution in the brain illustrates the usefulness of the technique in simulating realistic static neuroimaging studies. A dynamic18F-florbetaben study was modeled based on the time-activity curves of a human study and a segmented brain phantom with no coincidences repeating between frames. For all time points, the mean voxel-wise errors ranged from -4.4% to -0.7% in grey matter and from -3.9% to +2.8% in white matter.Significance. The proposed phantom technique is highly flexible and allows modeling of static and dynamic brain PET studies with high quantitative accuracy. It overcomes several key limitations of the existing phantoms and has many promising applications for the purposes of image reconstruction, data correction methods, and system performance evaluation, particularly for new high-performance dedicated brain PET scanners.

Significance of Image Reconstruction Parameters for Future Lung Cancer Risk Prediction Using Low-Dose Chest Computed Tomography and the Open-Access Sybil Algorithm.

Sybil is a validated publicly available deep learning-based algorithm that can accurately predict lung cancer risk from a single low-dose computed tomography (LDCT) scan. We aimed to study the effect of image reconstruction parameters and CT scanner manufacturer on Sybil's performance. Using LDCTs of a subset of the National Lung Screening Trial participants, which we previously used for internal validation of the Sybil algorithm (test set), we ran the Sybil algorithm on LDCT series pairs matched on kilovoltage peak, milliampere-seconds, reconstruction interval, reconstruction diameter, and either reconstruction filter or axial slice thickness. We also evaluated the cumulative effect of these parameters by combining the best- and the worst-performing parameters. A subanalysis compared Sybil's performance by CT manufacturer. We considered any LDCT positive if future lung cancer was subsequently confirmed by biopsy or surgical resection. The areas under the curve (AUCs) for each series pair were compared using DeLong's test. There was no difference in Sybil's performance between 1049 pairs of standard versus bone reconstruction filter (AUC at 1 year 0.84 [95% confidence interval (CI): 0.70-0.99] vs 0.86 [95% CI: 0.75-0.98], P = 0.87) and 1961 pairs of standard versus lung reconstruction filter (AUC at 1 year 0.98 [95% CI: 0.97-0.99] vs 0.98 [95% CI: 0.96-0.99], P = 0.81). Similarly, there was no difference in 1288 pairs comparing 2-mm versus 5-mm axial slice thickness (AUC at 1 year 0.98 [95% CI: 0.94-1.00] vs 0.99 [95% CI: 0.97-0.99], P = 0.68). The best-case scenario combining a lung reconstruction filter with 2-mm slice thickness compared with the worst-case scenario combining a bone reconstruction filter with 2.5-mm slice thickness uncovered a significantly different performance at years 2-4 (P = 0.03). Subanalysis showed no significant difference in performance between Siemens and Toshiba scanners. Sybil's predictive performance for future lung cancer risk is robust across different reconstruction filters and axial slice thicknesses, demonstrating its versatility in various imaging settings. Combining favorable reconstruction parameters can significantly enhance predictive ability at years 2-4. The absence of significant differences between Siemens and Toshiba scanners further supports Sybil's versatility.

Open-loop narrowband magnetic particle imaging based on mixed-frequency harmonic magnetization response

IntroductionMagnetic particle imaging (MPI), a radiation-free, dynamic, and targeted imaging technique, has gained significant traction in both research and clinical settings worldwide. Signal-to-noise ratio (SNR) is a crucial factor influencing MPI image quality and detection sensitivity, and it is affected by ambient noise, system thermal noise, and the magnetization response of superparamagnetic nanoparticles. Therefore to address the high amplitude system and inherent thermal noise present in conventional MPI systems is essential to improve detection sensitivity and imaging resolution.MethodThis study introduces a novel open-loop, narrow-band MPI signal acquisition system based on mixed-frequency harmonic magnetization response. Allowing superparamagnetic nanoparticles to be excited by low frequency, high amplitude magnetic fields and high frequency, low amplitude magnetic fields, the excitation coil generates a mixed excitation magnetic field at a mixed frequency of 8.664 kHz (fH + 2fL), and the tracer of superparamagnetic nanoparticles can generate a locatable superparamagnetic magnetization signal with rich harmonic components in the mixed excitation magnetic field and positioning magnetic field. The third harmonic signal is detected by a Gradiometer coil with high signal-to-noise ratio, and the voltage cloud image is formed.ResultThe experimental results show that the external noise caused by the excitation coil can be effectively reduced from 12 to about 1.5 μV in the imaging area of 30 mm × 30 mm, which improves the stability of the detection signal of the Gradiometer coil, realizes the detection of high SNR, and makes the detection sensitivity reach 10 μg Fe. By mixing excitation, the total intensity of the excitation field is reduced, resulting in a slight improvement of the resolution under the same gradient field, and the spatial resolution of the image reconstruction is increased from 2 mm under the single frequency excitation (20.7 kHz) in the previous experiment to 1.5 mm under the mixed excitation (8.664 kHz).ConclusionsThese experimental results highlight the effectiveness of the proposed open-loop narrowband MPI technique in improving signal detection sensitivity, achieving high signal-to-noise ratio detection and improving the quality of reconstructed images by changing the excitation magnetic field frequency of the excitation coil, providing novel design ideas and technical pathways for future MPI systems.

Super Resolution Reconstruction of Fluorescence Microscopy Image by Convolutional Network with Physical Priors

Super Resolution Reconstruction of Fluorescence Microscopy Image by Convolutional Network with Physical Priors

Adaptive Wireless Image Transmission Transformer Architecture for Image Transmission and Reconstruction

The advancement of 6G (6th Generation Mobile Networks) communication technology has posed challenges for traditional communication network architectures in meeting the demands for communication efficiency and quality. Semantic communication technology, characterized by its “understand before transmit” approach, has emerged as a pivotal technology driving the progress of 6G due to its ability to enhance communication efficiency and quality. The Wireless Image Transmission Transformer (WITT) model, which operates as a semantic communication system leveraging vision transformer technology for the transmission of semantic images, has shown efficacy in transmitting input images through processes of feature extraction and channel adaptation. This study introduces an advanced channel adaptive module that is informed by deep learning methodologies and the adaptive modulation principles of the Variational Information Bottleneck (VIB). This innovation enhances the original WITT model, resulting in the development of the Adaptive Wireless Image Transmission Transformer (ADWITT) architecture. Comprehensive experimental results have unequivocally shown that the transmission performance of the ADWITT architecture substantially surpasses that of the conventional WITT (Wavelet Image Transmission Technique) model, particularly in scenarios characterized by harsh and detrimental channel conditions. These findings underscore the robustness and adaptability of the ADWITT approach, which is poised to improve the field of image transmission by offering superior performance and resilience in environments where traditional methods falter.

NUMERICAL COMPARISON OF THREE LOCAL MULTIVARIATE GAUSSIAN-BASED INTERPOLATION SCHEMES

This study introduces the Local Explicit Radial Basis Function scheme (LE-RBF), alongside an investigation of its effectiveness compared with two other Gaussian RBF-based methods: the Modified Radial Basis Function Shepard's method (MRBFS) and the Local Radial Basis Function Karel's algorithm (KAREL), for interpolation problems. The research focuses on three challenging applications: Franke’s function in two and three dimensions, and grayscale image reconstruction. All three schemes, operating in a local interpolation manner, are assessed across multiple criteria, including accuracy, CPU time and storage, and sensitivity to parameters and node sizes. The numerical experiments conducted reveal that while all three schemes yield reasonably good results in most scenarios, the proposed LE-RBF method stands out for its higher accuracy, reduced sensitivity to nodes and shape choices, and lower CPU time and storage requirements. Notably, LE-RBF demonstrates superior performance in various node densities and shape parameter-selecting strategies, especially when combined with specific strategies like the Carlson shape at 142×142 nodes. Its efficiency in processing time further highlights its practicality for complex applications. The study concludes that LE-RBF, with its robust performance and flexibility, presents a promising avenue for future application in diverse scientific and engineering tasks.

Deep-learning reconstruction enhances image quality of Adamkiewicz Artery in low-keV dual-energy CT.

Low-keV virtual monoenergetic images (VMIs) of dual-energy computed tomography (CT) enhances iodine contrast for detecting small arteries like the Adamkiewicz artery (AKA), but image noise can be problematic. Deep-learning image reconstruction (DLIR) effectively reduces noise without sacrificing image quality. To evaluate whether DLIR on low-keV VMIs of dual-energy CT scans improves the visualization of the AKA. We enrolled 29 patients who underwent CT angiography before aortic repair. VMIs obtained at 70 and 40 keV were reconstructed using hybrid iterative reconstruction (HIR), and 40 keV VMIs were reconstructed using DLIR. The image noise of the spinal cord, the maximum CT values of the anterior spinal artery (ASA), and the contrast-to-noise ratio (CNR) of the ASA were compared. The overall image quality and the delineation of the AKA were evaluated on a 4-point score (1 = poor, 4 = excellent). The mean image noise of the spinal cord was significantly lower on 40-keV DLIR than on 40-keV HIR scans; they were significantly higher than on 70-keV HIR images. The CNR of the ASA was highest on the 40-keV DLIR images among the three reconstruction images. The mean image quality scores for 40-keV DLIR and 70-keV HIR scans were comparable, and higher than of 40-keV HIR images. The mean delineation scores for 40-keV HIR and 40-keV DLIR scans were significantly higher than for 70-keV HIR images. Visualization of the AKA was significantly better on low-keV VMIs subjected to DLIR than conventional HIR images.

Enhancing Ischemic Stroke Evaluation by a Model-Based Photoacoustic Tomography Algorithm.

Ischemic stroke (IS) is characterized by the sudden interruption of blood supply to the brain, resulting in neurological impairments and even mortality. Photoacoustic computed tomography (PACT) integrates the high contrast of optical imaging and the penetration of ultrasound imaging, enabling non-invasive IS evaluation. However, the image reconstruction quality significantly affects the oxyhemoglobin saturation (sO2) estimation. This study investigates a model-based with total variation minimized by augmented Lagrangian and alternating direction (MB-TVAL3) approach and compared it with the widely used back-projection (BP) and delay-and-sum (DAS) algorithms. Both simulations and invivo experiments are conducted to validate the performance of the MB-TVAL3 algorithm, showing a higher sO2 estimation accuracy and sensitivity in detecting infarct area compared to BP and DAS. The findings of this study emphasize the impact of acoustic inverse problem on the accuracy of sO2 estimation and the proposed approach offers valuable support for IS evaluation and cerebrovascular diagnosis.

Neural Network-Enabled Multiparametric Impedance Signal Templating for High throughput Single-Cell Deformability Cytometry Under Viscoelastic Extensional Flows.

Cellular biophysical metrics exhibit systematic alterations during processes, such as metastasis and immune cell activation, which can be used to identify and separate live cell subpopulations for targeting drug screening. Image-based biophysical cytometry under extensional flows can accurately quantify cell deformability based on cell shape alterations but needs extensive image reconstruction, which limits its inline utilization to activate cell sorting. Impedance cytometry can measure these cell shape alterations based on electric field screening, while its frequency response offers functional information on cell viability and interior structure, which are difficult to discern by imaging. Furthermore, 1-D temporal impedance signal trains exhibit characteristic shapes that can be rapidly templated in near real-time to extract single-cell biophysical metrics to activate sorting. We present a multilayer perceptron neural network signal templating approach that utilizes raw impedance signals from cells under extensional flow, alongside its training with image metrics from corresponding cells to derive net electrical anisotropy metrics that quantify cell deformability over wide anisotropy ranges and with minimal errors from cell size distributions. Deformability and electrical physiology metrics are applied in conjunction on the same cell for multiparametric classification of live pancreatic cancer cells versus cancer associated fibroblasts using the support vector machine model.

Efficient Storage and Analysis of Genomic Data: A k-mer Frequency Mapping and Image Representation Method.

k-mer frequencies are crucial for understanding DNA sequence patterns and structure, with applications in motif discovery, genome classification, and short read assembly. However, the exponential increase in the dimension of frequency tables with increasing k-mer length poses storage challenges. In this study, we present a novel method for compressing k-mer data without information loss, aiming to optimize storage and analysis processes. We employed Chaos Game Representation (CGR) to map k-mers to coordinates and used these components to generate raster images of k-mers. The CGR maps were partitioned and labeled based on substrings, with each substring mapped to a subframe, creating a fractal-like structure. The entire k-mer frequency set of each genomic sequence was represented as a single image, with each pixel corresponding to a specific k-mer and its occurrence. This approach reduced file size by up to 16-fold compared to plain text and 3-fold compared to binary format. Furthermore, we demonstrated the feasibility of performing alignment-free similarity analyses on images derived from k-mer frequencies of whole genome sequences from 14 plant species. Our results highlight the potential of this method as a fast and efficient tool for accessing, processing, and analyzing large biological sequence datasets, enabling the retrieval of k-mer frequencies and image reconstruction.

Fully Automated Region-Specific Human-Perceptive-Equivalent Image Quality Assessment: Application to 18F-FDG PET Scans

Introduction We propose a fully automated framework to conduct a region-wise image quality assessment (IQA) on whole-body 18F-FDG PET scans. This framework (1) can be valuable in daily clinical image acquisition procedures to instantly recognize low-quality scans for potential rescanning and/or image reconstruction, and (2) can make a significant impact in dataset collection for the development of artificial intelligence–driven 18F-FDG PET analysis models by rejecting low-quality images and those presenting with artifacts, toward building clean datasets. Patients and Methods Two experienced nuclear medicine physicians separately evaluated the quality of 174 18F-FDG PET images from 87 patients, for each body region, based on a 5-point Likert scale. The body regisons included the following: (1) the head and neck, including the brain, (2) the chest, (3) the chest-abdomen interval (diaphragmatic region), (4) the abdomen, and (5) the pelvis. Intrareader and interreader reproducibility of the quality scores were calculated using 39 randomly selected scans from the dataset. Utilizing a binarized classification, images were dichotomized into low-quality versus high-quality for physician quality scores ≤3 versus &gt;3, respectively. Inputting the 18F-FDG PET/CT scans, our proposed fully automated framework applies 2 deep learning (DL) models on CT images to perform region identification and whole-body contour extraction (excluding extremities), then classifies PET regions as low and high quality. For classification, 2 mainstream artificial intelligence–driven approaches, including machine learning (ML) from radiomic features and DL, were investigated. All models were trained and evaluated on scores attributed by each physician, and the average of the scores reported. DL and radiomics-ML models were evaluated on the same test dataset. The performance evaluation was carried out on the same test dataset for radiomics-ML and DL models using the area under the curve, accuracy, sensitivity, and specificity and compared using the Delong test with P values &lt;0.05 regarded as statistically significant. Results In the head and neck, chest, chest-abdomen interval, abdomen, and pelvis regions, the best models achieved area under the curve, accuracy, sensitivity, and specificity of [0.97, 0.95, 0.96, and 0.95], [0.85, 0.82, 0.87, and 0.76], [0.83, 0.76, 0.68, and 0.80], [0.73, 0.72, 0.64, and 0.77], and [0.72, 0.68, 0.70, and 0.67], respectively. In all regions, models revealed highest performance, when developed on the quality scores with higher intrareader reproducibility. Comparison of DL and radiomics-ML models did not show any statistically significant differences, though DL models showed overall improved trends. Conclusions We developed a fully automated and human-perceptive equivalent model to conduct region-wise IQA over 18F-FDG PET images. Our analysis emphasizes the necessity of developing separate models for body regions and performing data annotation based on multiple experts’ consensus in IQA studies.