Imaging Algorithm Research Articles

Metasurfaces‐Enabled Advanced Multidimensional Imaging: Principle and Applications

AbstractThe efficient acquisition of multidimensional information—spatial, polarization, frequency, and phase of optical fields, is a key research focus. Metasurfaces, which are emerging as planar arrays of artificial nanostructures, offer significant advantages in advanced imaging due to their exceptional ability to manipulate electromagnetic waves through subwavelength optical resonators. Meanwhile, the rapidly developing computational imaging algorithms have also greatly enhanced the performance of metasurfaces in multi‐dimensional imaging. In this review, we focus on the research progress in metasurface‐empowered multidimensional imaging, including advanced spatial imaging, compact polarization imaging, high‐resolution spectral imaging, single‐shot phase imaging, and edge imaging. Their basic principles and imaging results will also be presented. Furthermore, a summary of several current challenges and anticipation in various future directions within this field will also be provided.

A decision support system to increase the compliance of diagnostic imaging examinations with imaging guidelines: focused on cerebrovascular diseases.

Diagnostic imaging decision support (DI-DS) system has emerged as an innovative evidence-based solution to decrease inappropriate diagnostic imaging. The aim of the present study was to design and evaluate a DI-DS system for cerebrovascular diseases. The present study was an applied piece of research. First, the conceptual model of the DI-DS system was designed based on its functional and non-functional requirements. Afterwards, to create the system's knowledge base, cerebrovascular diseases diagnostic imaging algorithms were extracted from the American College of Radiology Appropriateness Criteria (ACR-AC). Subsequently, the system was developed based on the obtained conceptual model and the extracted algorithms. The software was programmed by means of the C#. After debugging the system, it was evaluated regarding its performance and also the users' satisfaction with it. Assessing the users' satisfaction with the system demonstrated that all the evaluation criteria met the acceptable threshold (85 %). The retrospective evaluation of the system's performance indicated that from among 76 imaging examinations, which had previously been performed for 30 patients, 12 (15.78 %) were deemed inappropriate. And, the system accurately identified all the inappropriate physicians' decisions. The concurrent evaluation of the system's performance indicated that the system's recommendations helped the physicians remove 100 % (4 out of 4) of the inappropriate and 40 % (2 out of 5) of the inconclusive imaging examinations from their initial choices. A DI-DS system could increase the compliance of the physicians' decisions with diagnostic imaging guidelines, and also improve treatment outcomes through correct diagnosis and providing timely care.

Application of Imaging Algorithms for Gas–Water Two-Phase Array Fiber Holdup Meters in Horizontal Wells

Application of Imaging Algorithms for Gas–Water Two-Phase Array Fiber Holdup Meters in Horizontal Wells

HPB SO24 - Recurrence Pattern of Pancreatic Ductal Adenocarcinoma based on Time-to-Recurrence: A 5-year Single-Centre Retrospective Study

Abstract Background A vast majority of patients with pancreatic ductal adenocarcinoma (PDAC) develop early disease recurrence, even after curative-intent surgical resection. Understanding the pattern of disease recurrence will enable identification of areas for improvement within the preoperative diagnostic and management algorithms for these patients. The aim of the retrospective cohort study was to compare the recurrence pattern of PDAC in patients with early (<12 vs >12 months) and very early (<6 vs >6 months) recurrence following surgical resection. Method A retrospective review of histologically confirmed pancreatic ductal adenocarcinoma patients who underwent pancreaticoduodenectomy (N=151) between 2015-2020 at a tertiary HPB centre was undertaken. All patients underwent CT and PET-CT for initial staging before resection. Initial recurrence after resection was identified through postoperative imaging and/or HPB MDT outcomes. Early recurrence (ER) was defined as recurrence within the first 12 months after surgery (n=78). Additionally, very early recurrence (VEM) was defined as recurrence within the first six months after surgery (n=46). Statistical analysis was performed using GraphPad Prism 8.0. Results In total, 117 (77.5%) of patients experienced recurrence, with a median recurrence-free survival of 11.3 (5.1-19.6) months. Patients with ER had significantly higher pathological T staging (p=0.01), greater margin positivity rate (p=0.01) and higher lymph node ratio (p=0.046), while those in VER had significantly larger maximum tumour diameter and number of involved lymph nodes (p=0.04) compared to their counterparts. Patients with ER (47.4 vs 31.5%, p=0.046) and VER (60.9 vs 30.5%, p<0.001) had significantly greater rates of liver metastasis compared to their counterparts. There were no differences in loco-regional recurrence, peritoneal or pulmonary metastasis between the groups. Conclusion Approximately 20% of patients undergoing surgical resection for PDAC developed liver metastasis within six months of surgery. This raises concerns for the presence of micro-metastatic disease within the liver unidentified on preoperative imaging. Therefore, there is a need to enhance preoperative imaging and treatment algorithms to improve survival of patients with PDAC.

Robust double encryption and watermarking algorithms for color watermark images

Abstract Current digital watermarking technologies mainly focus on the imperceptibility and robustness of watermark embedding, while the security of watermarking images is also worth further research. Considering nonlinear characteristics and the integration structure of storage and computation, memristors can be introduced into encryption algorithms to improve the effect of encryption. The paper proposes a double encryption algorithm for color watermark images based on MCNN (Memristive Cellular Neural Networks) and Arnold transform, generates chaotic sequences for watermark image encryption by introducing memristors to the CNN (Cellular Neural Networks) to construct MCNN, scrambles the images using the Arnold transform to achieve the double encryption of pixel values and pixel positions, and enhances the security of the watermark images. Adopting the SE (Spectral Entropy) complexity algorithm optimizes the parameters of MCNN, and improves the performance of the double encryption algorithm. The embedding and extraction of the encrypted watermark image is realized by the algorithm combining CT (Contourlet Transform) and SVD (Singular Value Decomposition), which enhances the ability to resist common attacks such as compression and rotation attacks. Experiment results show the proposed algorithms can better maintain the quality of the color watermark images, break the statistical characteristics of the original images, and the generated key has good randomness. In addition, the presented algorithms are highly sensitive to the key, and improve the ability to resist statistical attacks, differential attacks, exhaustive attacks and common image attacks with good security, robustness and imperceptibility.

Non-linear super-resolution computed tomography imaging algorithm based on discrete X-ray source focal spot model

Non-linear super-resolution computed tomography imaging algorithm based on discrete X-ray source focal spot model

FET PET to differentiate between post-treatment changes and recurrence in high-grade gliomas: a single center multidisciplinary clinic controlled study.

The clinico-radiological dilemma in post-treatment high-grade gliomas, between disease recurrence (TR) and treatment-related changes (TRC), still persists. FET (Fluoro-ethyl-tyrosine) PET has been extensively used as problem-solving modality for cases where MR imaging is inconclusive. We incorporated a systematic imaging and clinical follow-up algorithm in a multi-disciplinary clinic (MDC) setting to analyse our cohort of FET PET in post-treatment gliomas. We retrospectively analyzed 171 patients of post-treatment grade III and IV glioma with equivocal findings on MRI. 185-222 MBq of 18F-FET was injected and dedicated static imaging of brain was performed at 20min. TBR (Tumor to background ratio) was used as semi-quantitative parameter. Cutoff of 2.5 was used for image interpretation. Imaging findings were confirmed with histopathological diagnosis, wherever available or in a multidisciplinary joint clinic based on serial imaging. 121 of 171 patients showed recurrent disease on FET PET, on follow up, 109 were confirmed with recurrence; 7 patients showed TRC, whereas 5 were treated with bevacizumab, with no further clinico-radiological deterioration, thus confirming TRC. 50 patients showed TRC on FET PET, on follow up on follow up, 40 were confirmed as true-negative. 10 patients who showed TBR less than 2.5 had confirmed TR on subsequent MR imaging. The overall sensitivity and specificity was 91.6 and 76.9% respectively, with a diagnostic accuracy of 87.13%. There is potential for FET PET to be used along with MRI in the post treatment algorithm of high-grade glial tumors.

Enhanced TomoSAR imaging method with complex least squares for 3D building reconstruction

ABSTRACT Synthetic aperture radar tomography (TomoSAR) has emerged as an advanced technology for the rapid acquisition of three-dimensional information, with model solutions dependent on a specific complex least squares adjustment criterion. We design two TomoSAR imaging algorithms based on two adjustment criterions minimizing the square sum of the real and imaginary components of observation vector residual, and minimizing the square sum of the L2-norm of observation vector residual. Additionally, we propose a strong scatterer recognition method that combines amplitude thresholding and density-based spatial clustering of applications with noise to alleviate the low efficiency and excessive noise in real images. The simulated and practical experiments are conducted to evaluate the performance of algorithms. Results suggested that two algorithms perform similarly in parameter estimation accuracy and noise immunity for buildings with simple structures and single scatterer. However, for buildings with multiple scatterers, the algorithm derived by minimizing the square sum of the L2-norm from the observation vector residual exhibits more robustly than the other algorithm, accurately distinguishing multiple scatterers within pixels and reconstructing 3D scenes mirroring the actual ones. The results verify the effectiveness of the proposed strong scatterer recognition method and the importance of the adjustment criterion in TomoSAR imaging.

Fluorescence Lifetime Imaging Enables In vivo Quantification of PD-L1 Expression and Inter-tumoral Heterogeneity.

Patient selection for cancer immunotherapy requires precise, quantitative readouts of biomarker expression in intact tumors that can be reliably compared across multiple subjects over time. The current clinical standard biomarker for assessing immunotherapy response is programmed death-ligand-1 (PD-L1) expression, typically quantified using immunohistochemistry. This method, however, only provides snapshots of PD-L1 expression status in microscopic regions of ex vivo specimens. While various targeted probes have been investigated for in vivo imaging of PD-L1, non-specific probe accumulation within the tumor microenvironment (TME) has hindered accurate quantification, limiting the utility for preclinical and clinical studies. Here, we demonstrated that in vivo time-domain (TD) fluorescence imaging of an anti-PD-L1 antibody tagged with the near-infrared fluorophore IRDye 800CW (αPDL1-800) can yield quantitative estimates of baseline tumor PD-L1 heterogeneity across untreated mice, as well as variations in PD-L1 expression in mice undergoing clinically relevant anti-PD1 treatment. The fluorescence lifetime (FLT) of PD-L1 bound αPDL1-800 was significantly longer than the FLT of nonspecifically accumulated αPDL1-800 in the TME. This FLT contrast allowed quantification of PD-L1 expression across mice both in superficial breast tumors using planar FLT imaging and in deep-seated liver tumors (>5 mm depth) using the asymptotic TD algorithm for fluorescence tomography. These findings suggest that fluorescence lifetime imaging can accelerate the preclinical investigation and clinical translation of new immunotherapy treatments by enabling robust quantification of receptor expression across subjects.

Intelligent leaf disease diagnosis: image algorithms using Swin Transformer and federated learning

Intelligent leaf disease diagnosis: image algorithms using Swin Transformer and federated learning

Use of HANTS algorithm in Sentinel-2 images for interpolation of missing data: a case-study for soy agricultural areas in periods of growth and harvesting

Abstract. Information about the current state of an agricultural culture can be fundamental in decision making, varying from quantity of fertilizers to be used in an area to estimating the productivity of a harvest in a period to establish credits payment. Satellite images with reasonable spatial resolution like Sentinel-2 are a powerful tool to visualize agricultural areas production, especially with the application of multispectral indexes like the Normalized Difference Vegetation Index (NDVI). Yet, due to the limitation of temporal resolution of satellites and periods with large cloudy weather can make the analysis difficult. This study proposed the use of the harmonic analysis time series algorithm (HANTS) to aid the incompleteness of time series obtained by the available satellite images in a case study of soy culture in Brazil. By applying the algorithm, a harmonic interpolation is obtained to produce a full daily NDVI time series from the beginning to end of the study period, facilitating further analysis to obtain metrics of interest. In this study, time series with gaps on specified periods are created based on full time series, then interpolated and compared with the real ones based on root mean squared error (RMSE) to access its accuracy.

Digital Smile Design Software: An Overview

Digital Smile Design (DSD) software have revolutionized the field of aesthetic dentistry by enabling clinicians to design and visualize patient-specific treatment outcomes. This software integrates digital tools like 2D and 3D imaging, CAD/CAM technology, and simulation algorithms to create comprehensive and highly accurate treatment plans. It facilitates communication between dentists, technicians, and patients by providing a visual representation of potential results, enhancing patient understanding and confidence. This article presents an overview of digital smile design software currently available.

Estimation of direction and zero errors of satellite laser terminals in low-light conditions based on machine learning

In the assembly, launch, and on-orbit operation of satellite optical communication terminals, small deviations are difficult to avoid, which can lead to pointing errors and challenges to the establishment of optical communication links. To estimate the pointing errors of on-orbit satellite terminals, a calibration algorithm is developed based on lunar surface imagery. First, a feature extraction algorithm for low-light images is employed to process consecutive frames of low-light images to obtain a lunar surface feature map. Then, by combining the feature map and error estimation model, predictions of direction errors and zero errors were achieved. The ground validation results demonstrate the effectiveness and feasibility of the proposed on-orbit error estimation algorithm under low-signal-to-noise-ratio conditions.

REFERENCE AREAS SELECTION AFFECTS REGISTRATION OF AI-SEGMENTED MANDIBLES ACQUIRED WITH CBCT

Introduction Precise registration of sequential 3D datasets is crucial for accurate dimensional analysis. Utilizing the Local Best-Fit (LBF) algorithm and stable Registration Reference Areas (RRAs) facilitates the accurate alignment of 3D surface models. Currently, Cone-beam Computed Tomography (CBCT) and Deep Learning (DL) algorithms are at the forefront for segmenting CBCT scans to monitor morphological changes in the residual alveolar ridge. This study compares the effectiveness of different RRAs in registration sequential 3D surface models of partially edentulous mandibles. Methods DL-assisted software segmented two sequential CBCTs (T0 and T1) from 10 patients, producing sequential 3D mandibular models. These models were aligned using three distinct RRAs: (i) WHOLE, encompassing the entire surface model; (ii) MND_BODY, covering the mandibular body while excluding the unstable alveolar ridge; and (iii) SPIN_FOR, incorporating stable RRAs (mental foramina and mental spine). An innovative method assessed registration accuracy by generating centroids from cross-sectional outlines of the mandibular nerve canals at the anterior third (A), medial third (B), and posterior third (C) of the posterior edentulous areas. The distance between centroids at T0 and T1 quantified registration accuracy. Results The MND_BODY group exhibited superior accuracy, whereas the SPIN_FOR group showed the least, with accuracy decreasing from A to C, suggesting rotational misalignments. ConclusionsWhen selecting RRAs, both stability and spatial distribution must be taken into account. For optimal alignment, sequential 3D surface models should use RRAs that are both stable and widely distributed.

Airborne Multi-Channel Forward-Looking Radar Super-Resolution Imaging Using Improved Fast Iterative Interpolated Beamforming Algorithm

Radar forward-looking imaging is critical in many civil and military fields, such as aircraft landing, autonomous driving, and geological exploration. Although the super-resolution forward-looking imaging algorithm based on spectral estimation has the potential to discriminate multiple targets within the same beam, the estimation of the angle and magnitude of the targets are not accurate due to the influence of sidelobe leakage. This paper proposes a multi-channel super-resolution forward-looking imaging algorithm based on the improved Fast Iterative Interpolated Beamforming (FIIB) algorithm to solve the problem. First, the number of targets and the coarse estimates of angle and magnitude are obtained from the iterative adaptive approach (IAA). Then, the accurate estimates of angle and magnitude are achieved by the strategy of iterative interpolation and leakage subtraction in FIIB. Finally, a high-resolution forward-looking image is obtained through non-coherent accumulation. The simulation results of point targets and scenes show that the proposed algorithm can distinguish multiple targets in the same beam, effectively improve the azimuthal resolution of forward-looking imaging, and attain the accurate reconstruction of point targets and the contour reconstruction of extended targets.

EigenCWD: a spatially-varying deconvolution algorithm for single metalens imaging

EigenCWD: a spatially-varying deconvolution algorithm for single metalens imaging

Non-invasive imaging modalities for diagnosing pulsatile tinnitus: a comprehensive review and recommended imaging algorithm.

Pulsatile tinnitus (PT) is a challenging diagnostic condition arising from various vascular, neoplastic, and systemic disorders. Non-invasive imaging is essential for identifying underlying causes while minimizing risks of invasive diagnostic angiography. Although no consensus exists on the primary imaging modality for PT and currently CT, ultrasound, and MRI are used in the diagnostic pathway, MRI is increasingly preferred as the first-line screening test for its diagnostic efficacy and safety. MRI protocols such as time-of-flight, magnetic resonance angiography, diffusion-weighted imaging, and arterial spin labeling can identify serious causes, including vascular shunting lesions, venous sinus stenosis, and tumors. In this narrative review of the current literature we discuss the benefits and limitations of various non-invasive imaging modalities in identifying the characteristic imaging findings of the most common causes of PT and also provide an algorithm that clinicians can use to guide the imaging evaluation.

Application of Magnetic Resonance Imaging and Artificial Intelligence Algorithms in Cancer Screening

Application of Magnetic Resonance Imaging and Artificial Intelligence Algorithms in Cancer Screening

Non-destructive estimation for Kyoho grape shelf-life using Vis/NIR hyperspectral imaging and deep learning algorithm

Grape shelf-life estimation is a substantial challenge for the grape industry. The objective of this study is to investigate the potential of grape shelf-life estimation using HSI technique and a deep learning algorithm. The visible and near-infrared (400.68–1001.61 nm) hyperspectral reflectance images data of grape samples was acquired and preprocessed with different spectral preprocessing methods. Additionally, a stacked denoising autoencoder (SDAE)-based deep learning algorithm was developed to extract deep features from pixel-level hyperspectral data of grapes, and then these features were used as inputs to establish support vector machine (SVM) models for estimating grape shelf-life. Furthermore, SVM, one-dimensional convolutional neural network (1D CNN) and long short-term memory (LSTM) models were used as traditional machine learning and end to end models for comparison. The results demonstrated that the SDAE-SVM model achieved reasonable recognition accuracy of 100 % and 98.125 % for the shelf-life of grapes in the training and test sets, respectively. The overall results suggested that SDAE-based deep learning method can be used as a powerful tool to deal with large-scale hyperspectral data as well as this research confirms the feasibility of non-destructive estimation for grapes shelf-life by the combination of HSI technique and deep learning method, which would provide a valuable guidance for shelf-life estimation of other postharvest fruit.

A new method for judging thermal image quality with applications

Infrared thermal imaging, a non-destructive testing technology, measures the surface temperature of objects. Assessing thermal image quality is crucial for image monitoring, system design, algorithm optimization, and benchmarking. However, developing objective metrics that align with human perception is challenging due to the distinct structure of thermal images, which often feature high background temperatures and minimal variance between objects and the background. Existing methods typically target specific local features or overall image contrast, but new measures are needed to bridge the gap between objective performance and the unique characteristics of thermal images.We propose a novel image quality assessment (IQA) method inspired by the human vision system, specifically designed for thermal images, harmonizing local and global data. The primary contributions include (1) innovative local, global, and hybrid thermal quality assessment methods that deliver precise image quality predictions without needing reference images, (2) an experimental analysis evaluating the developed blind thermal IQA measure’s applicability to various thermal images, and (3) a comprehensive analysis of traditional IQA measure-based methods applied to publicly accessible thermal databases. Extensive simulations demonstrate our method’s competitive performance and strong alignment with human perception of image quality.