Image Error Research Articles

Optimizing energy threshold selection for low-concentration contrast agent quantification in small animal photon-counting CT

Objective.Gold nanoparticles (GNPs) are widely used for biological research and applications. The in-vivo concentration of GNPs is usually low due to biological safety concerns, thus posing a challenge for imaging. This work investigates on optimal energy threshold selection in photon-counting detector(PCD)-based CT (PCCT) for the quantification of low-concentration GNPs.Approach.We derived the mathematical expression of the upper bound of the material decomposition error in the gold image. Comprehensive simulations were implemented for cylindrical phantom with inserts of different GNP concentrations. CT scans of this phantom were simulated with a 140 kVp x-ray beam under a realistic pre-clinical CT dose range. The PCD energy thresholds from 30 to 110 keV were enumerated for 2,3-channel PCCT and the optimal energy thresholds were determined by searching for the lowest decomposition error.Main results.The optimal energy threshold(s) to minimize the decomposition error in gold image was 44 keV for the 2-channel PCCT and{34,40}keV for the 3-channel case. Numerical results also validated the derived upper bounds of the decomposition error.Significance.This work addressed the need for selecting appropriate energy thresholds for accurate quantification of contrast agent distributions in pre-clinical PCCT. Both the analytical expression of the upper bound of material decomposition error and simulation results showed that the balanced consideration on photon counting noise levels and the numerical properties of the decomposition matrix is required in selecting the appropriate energy thresholds to achieve the most accurate material decomposition.

A Deep Learning-Based Method for Measuring Apparent Disease Areas of Sling Sheaths

The sling sheath plays an important protective role in the sling of suspension bridges, effectively preventing accidental damage to the sling caused by wind, fatigue and other impacts. To conduct a quantitative analysis of the apparent disease of suspension bridge slings, a method for segmenting and quantifying the apparent disease of the sling sheath using deep learning and image processing was proposed. A total of 1408 disease images were obtained after image acquisition of a suspension bridge following sling replacement. MATLAB 2021a Image Labeler software was used to establish a disease dataset by manual labelling. Then, the MobileNetV2 model was trained and tested on the dataset to determine disease segmentation; additionally, an area measurement algorithm was proposed based on the images’ projection relationships. Finally, the measurement results were compared with the manually acquired crack area. The results show that the accuracy of background and sheath category pixels in the MobileNetV2 model is above 97%, indicating that the model achieves satisfactory results in these classifications. However, the accuracy of crack category pixels and the intersection over union ratio only reaches 80%, which needs to be improved by setting model correction coefficients. When measuring directly, it was found that the area measurement error of the test image mainly ranged between 8% and 30%, and the measurement error of the crack area after correction mainly ranged between −3% and 15%, indicating that the area measurement method can achieve a higher degree of measurement accuracy. The method for segmenting and quantifying the apparent disease of the sling sheath based on deep learning and image processing fills the research gap in the measurement of the surface damage area caused by apparent disease and has the advantages of high efficiency and high recognition accuracy. Reducing the maintenance costs of suspension bridge slings is crucial for promoting comprehensive intelligent detection of bridges and advancing the smart transformation of the civil engineering industry.

Case Study Analysis of Dyslexia and Dysgraphia in a 10-Year-Old Male: Intervention and Outcomes

Specific Learning Disabilities (SLD) represent a range of persistent difficulties in acquiring academic skills, often evident in areas like reading, writing, and mathematics. This study presents a detailed case analysis of a 10-year-old male diagnosed with mixed scholastic difficulties, specifically dyslexia and dysgraphia. The participant exhibited significant challenges with reading and writing, including issues with mirror images of letters and spelling errors, impacting academic performance. Comprehensive assessments, including the Malin’s Intelligence Scale for Indian Children (MISIC) and NIMHANS educational battery, were administered, revealing average IQ and academic performance equivalent to a second grader. The intervention involved 72 individualized sessions over six months, targeting attention span, reading comprehension, and spelling. Adjustments to the school curriculum, such as extra exam time and exemption from a third language, were implemented to support his learning needs. Post-intervention results indicated substantial improvements in reading, spelling accuracy, and academic confidence, as reported by teachers and parents. This study highlights the importance of personalized education plans and ongoing support for children with SLD, providing a framework for clinicians handling similar cases. Key Words: Specific Learning Disabilities (SLD), Dysgraphia, Dyslexia, Dyscalculia, Individualized Education Plan (IEP), Reading and writing difficulties, Mirror image errors, SLD Asssessments, MISIC, NIMHANS neuropsychological battery, School accommodations, Case study, Therapeutic outcomes, Therapeutic Interventions

Learning-based motion artifact correction in the Z-spectral domain for chemical exchange saturation transfer MRI.

To develop and evaluate a physics-driven, saturation contrast-aware, deep-learning-based framework for motion artifact correction in CEST MRI. A neural network was designed to correct motion artifacts directly from a Z-spectrum frequency (Ω) domain rather than an image spatial domain. Motion artifacts were simulated by modeling 3D rigid-body motion and readout-related motion during k-space sampling. A saturation-contrast-specific loss function was added to preserve amide proton transfer (APT) contrast, as well as enforce image alignment between motion-corrected and ground-truth images. The proposed neural network was evaluated on simulation data and demonstrated in healthy volunteers and brain tumor patients. The experimental results showed the effectiveness of motion artifact correction in the Z-spectrum frequency domain (MOCOΩ) compared to in the image spatial domain. In addition, a temporal convolution applied to a dynamic saturation image series was able to leverage motion artifacts to improve reconstruction results as a denoising process. The MOCOΩ outperformed existing techniques for motion correction in terms of image quality and computational efficiency. At 3 T, human experiments showed that the root mean squared error (RMSE) of APT images decreased from 4.7% to 2.1% at 1 μT and from 6.2% to 3.5% at 1.5 μT in case of "moderate" motion and from 8.7% to 2.8% at 1 μT and from 12.7% to 4.5% at 1.5 μT in case of "severe" motion, after motion artifact correction. The MOCOΩ could effectively correct motion artifacts in CEST MRI without compromising saturation transfer contrast.

Practical error prediction in UAV imagery-based 3D reconstruction: assessing the impact of image quality factors

ABSTRACT Over the past decade, Structure-from-Motion (SfM) and Multi-View Stereo (MVS) techniques have been highly effective in generating high-quality 3D point clouds, especially when integrated with Unmanned Aerial Vehicles (UAVs). However, accurately predicting errors in these point clouds remains challenging. This study introduces a predictive model for quantifying errors in point clouds generated using SfM-MVS, based on 2D image error propagation. We analysed the impact of four key image quality factors – exposure, resolution, blur (including motion and out-of-focus blur), and noise – on 2D image errors. These factors are determined using nine practical parameters belonging to camera settings (ISO, shutter speed, F-number, and pixel resolution), UAV flight methods (speed and distance), camera specifications (sensor size and focal length), and illumination conditions. To account for variations in SfM-MVS output quality due to software and camera model differences, we introduced two experimentally calibratable constants: the maximum template size and the noise sensitivity coefficient. We validated our error prediction model by comparing the predicted errors with observations from a reference indoor target, utilizing a total of 258 image sets acquired with varying parameter combinations. The model demonstrated high predictive accuracy, achieving an R2 value of 0.84. This confirms the effectiveness and feasibility of our model in accurately predicting point cloud errors using the nine parameters.

Errors in single pixel laser imaging emerging from spatial size limits in the bucket detector

Errors in single pixel laser imaging emerging from spatial size limits in the bucket detector

Migration and catalytic viscosity reduction of biochar-based catalyst fluid in heavy oil reservoir pores

The method for reducing heavy oil viscosity through catalysts has remained experimental. Catalyst aggregation in reservoirs is a challenging issue. This study prepares catalyst fluids with both hydrophilic and oleophilic properties and uses dispersants to inhibit aggregation. Stability is assessed using direct observation and an ultraviolet-visible spectrophotometer, with results showing that a dispersant concentration of 0.05 wt. % stabilizes catalyst fluids. Micromodel experiments are conducted to investigate the catalytic performance and dispersion characteristics of catalyst fluids under various conditions. A post-processing method based on the hue, saturation, and value color space for image recognition and error calculation is proposed to analyze the migration and sweeping effects of catalyst fluids. This method involves identifying images captured by a digital camera, calculating area ratios, and determining recognition errors. The results show that catalyst fluids exhibit the best viscosity reduction ratio (80.0%) and the largest dispersion area ratio (55.7%) when the catalyst concentration is 4 wt. %, the injection velocity is 0.01 ml/min, the reaction temperature is 200 °C, and the reaction time is 24 h. With the increase in injection velocity, the viscosity reduction effect becomes worse. The viscosity reduction effect is improved with the increase in reaction temperature. With the growth of reaction time, the viscosity reduction effect increases and then becomes gentle. The combined mechanism of catalytic viscosity reduction and sweeping effects of catalyst fluids in porous media is proposed. This study provides a theoretical foundation for the large-scale development of heavy oil catalytic viscosity reduction.

Contrast-enhanced ultrasonography guidance avoids US-CT/MR fusion error for percutaneous radiofrequency ablation of hepatocellular carcinoma

BackgroundThis study evaluated the impact of contrast-enhanced ultrasonography (CEUS) combined with CT or MRI fusion imaging on percutaneous radiofrequency ablation (RFA) outcomes for hepatocellular carcinoma (HCC) inconspicuous on conventional ultrasonography (US).MethodsPatients were categorized into US-inconspicuous (USI) and US-conspicuous (USC) groups based on US imaging. The parameters of viable HCCs ⎯ including diameter, location, and RFA efficacy ⎯ were compared between USI and USC groups. Moreover, the breathing fusion imaging errors were measured. The differences in technical success, technical efficacy, local tumor progression, new tumor occurrence, and overall survival rate between USI and USC groups were analyzed.ResultsSixty-five patients with 106 lesions were included. CEUS showed high consistency with CT/MRI but revealed larger diameters (p < 0.001) and more feeding arteries (p = 0.019) than CT/MRI. Breathing fusion imaging errors averaged 17 ± 4 mm, significantly affecting lesions in segments II, III, V, and VI (p < 0.001). The USI group had more lesions ablated per patient in a single RFA procedure (p = 0.001) than the USC group. No significant differences were observed in technical success rate, technical efficacy rate, local tumor progression rate, and overall survival rate between the two groups.ConclusionsCEUS combined with fusion imaging provides detailed information on viable HCCs and their feeding arteries. CEUS-guided RFA avoids fusion imaging errors and achieves comparable efficacy in both US-conspicuous and US-inconspicuous HCCs.

A bootstrapping residuals approach to determine the error in quantitative functional lung imaging.

To implement and validate an algorithm to determine the statistical errors in self-gated non-contrast-enhanced functional lung imaging. A bootstrapping residuals approach to determine the error in quantitative functional lung imaging is proposed. Precision and accuracy of the median error over the lungs, as well as reproducibility of the approach were investigated in 7 volunteers. The algorithm was additionally applied to data acquired in a patient with cystic fibrosis. The obtained bootstrapping error maps appear comparable to the error maps determined from repeated measurements, and median absolute error values for both methods show comparable median errors when reducing the number of averages. In a volunteer in whom 10 consecutive measurements were carried out, the median functional parameters were ventilation = 0.22 mL gas/mL lung tissue, perfusion amplitude = 0.028, perfusion timing = -82 ms, whereas precision and accuracy of the median error were below 3.2 × 10-3 mL gas/mL lung for ventilation tissue, 4.4 × 10-4 for perfusion amplitude, and 11 ms for perfusion timing. In the measurement of the patient, low errors in areas with reduced ventilation support the assessment as real defects. Using a bootstrapping residuals method, the error of functional lung MRI could be determined without the need for repeated measurements. The error values can be determined reproducibly and can be used as a future means of quality control for functional lung MRI.

Misdiagnosis in breast imaging: a statement paper from European Society Breast Imaging (EUSOBI)-Part 2: Main causes of errors in breast imaging and recommendations from European Society of Breast Imaging to limit misdiagnosis.

Breast cancer is one of the leading causes of negligence claims in radiology. The objective of this document is to describe the specific main causes of errors in breast imaging and provide European Society of Breast Imaging (EUSOBI) recommendations to try to minimize these. Technical failures represent 17% of all mammographic diagnostic negligence claims. Mammography quality control protocol and dedicated training for technologists and radiologists are essential. Lack of consideration of the clinical context is a second critical issue, as a clinical abnormality is found in 80% of malpractice claims. EUSOBI emphasizes the importance of communication and clinical examination before the diagnostic investigation. Detection errors or misapplications of the lexicon or Breast Imaging Reporting Data System (BI-RADS) score account for 5% of malpractice claims and should be reduced by limiting radiologists' distraction or fatigue, and being aware of satisfaction of search errors and the importance of a personal systematic review. Errors related to pathological concordance and MDT review can be limited by the use of markers after biopsy and the use of standardized reports, which can aid communication with other specialities. Finally, errors related to tumor or patient factors should be discussed, considering the use of contrast-enhanced mammography and magnetic resonance imaging. Several factors are responsible for misdiagnosis in breast cancer, including errors in the practice of the technician and/or radiologist (technical failures, lack of consideration of the clinical context, incorrect application of the BI-RADS score, false reassurances), lack of communication with other specialists or with the patient, and the type of tumor and breast parenchyma. Question What factors most contribute to and what implications stem from misdiagnosis in breast imaging? Findings Ongoing training and education for radiologists and other healthcare providers, as well as interdisciplinary collaboration and communication is paramount. Clinical relevance Misdiagnosis in breast imaging can have significant implications for patients, healthcare providers, and the entire healthcare system.

Estimation of soil loss and sediment yield by using the modified RUSLE model in the Indus River basin, including the quantification of error and uncertainty in remote-sensing images

Context Indus River is the cradle of Pakistani lifeline, and its lower reaches are prone to soil loss owing to bank erosion. Aims The aim was to investigate the sediment yield in the Lower Indus River Basin (LIRB), while addressing challenges related to error or uncertainty in remote-sensing data. Methods We employed a modified revised universal soil loss equation (RUSLE) model, integrating high-resolution digital elevation model (DEM) and calibrated Climate Hazards Group InfraRed Precipitation with station data (CHIRPS). Additional data layers, including land use, soil and cropping data, were also utilised. Key results The extent of actual soil erosion ranges from minimum to maximum erosion; 38.9% area lies in the range &gt;50 Mg ha‒1 year‒1, whereas 23.2% area lies in the range of 0–10 Mg ha‒1 year‒1, and 18.1% area lies in the range of 10–20 Mg ha‒1 year‒1. Conclusions The study identifies critical erosion areas and tackles uncertainties in remote-sensing data. The spatial analysis showed that higher distribution sediment erosion along the channel flow direction from the northern part of LIRB to the Arabian Sea. Implications The findings have provided critical information for policymakers and water managers to implement effective measures to reduce erosion, maintain soil integrity and promote the sustainability of the Indus River system.

Imaging error reduction in radial cine-MRI with deep learning-based intra-frame motion compensation

Objective.Radial cine-MRI allows for sliding window reconstruction at nearly arbitrary frame rate, promising high-speed imaging for intra-fractional motion monitoring in magnetic resonance guided radiotherapy. However, motion within the reconstruction window may determine the location of the reconstructed target to deviate from the true real-time position (target positioning errors), particularly in cases of fast breathing or for anatomical structures affected by the heartbeat. In this work, we present a proof-of-concept study aiming to enhance radial cine-MR imaging by implementing deep-learning-based intra-frame motion compensation techniques.Approach.A novel network (TransSin-UNet) was proposed to continuously estimate the final-position image of the target, corresponding to end of the frame acquisition. Within the radial k-space reconstruction window, the spatial-temporal dependencies among the sinogram representation of the spokes were modeled by a transformer encoder subnetwork, followed by a UNet subnetwork operating in the spatial domain for pixel-level fine-tuning. By simulating motion-dependent radial sampling with (tiny) golden angles, we generated datasets from 25 4D digital anthropomorphic lung cancer phantoms. The network was then trained and extensively evaluated across datasets characterized by varying azimuthal radial profile increments.Main Results.The method required additional 4.8 ms per frame over the conventional approach involving direct image reconstruction with motion-corrupted spokes. TransSin-UNet outperformed architectures relying solely on transformer encoders or UNets across all the comparative evaluations, leading to a noticeable enhancement in image quality and target positioning accuracy. The normalized root mean-squared error decreased by 50% from the initial value of 0.188 on average, whereas the mean Dice similarity coefficient of the gross tumor volume increased from 85.1% to 96.2% in the investigated cases. Furthermore, the final-positions of anatomical structures undergoing substantial intra-frame deformations were precisely derived.Significance.The proposed approach enables an effective intra-frame motion compensation, offering an opportunity to reduce errors in radial cine-MR imaging for real-time motion management.

Evaluating Text-to-Image Generated Photorealistic Images of Human Anatomy.

Generative artificial intelligence (AI) models that can produce photorealistic images from text descriptions have many applications in medicine, including medical education and the generation of synthetic data. However, it can be challenging to evaluate their heterogeneous outputs and to compare between different models. There is a need for a systematic approach enabling image and model comparisons. To address this gap, we developed an error classification system for annotating errors in AI-generated photorealistic images of humans and applied our method to a corpus of 240 images generated with three different models (DALL-E 3, Stable Diffusion XL, and Stable Cascade) using 10 prompts with eight images per prompt. The error classification system identifies five different error types with three different severities across five anatomical regions and specifies an associated quantitative scoring method based on aggregated proportions of errors per expected count of anatomical components for the generated image. We assessed inter-rater agreement by double-annotating 25% of the images and calculating Krippendorf's alpha and compared results across the three models and 10 prompts quantitatively using a cumulative score per image. The error classification system, accompanying training manual, generated image collection, annotations, and all associated scripts, is available from our GitHub repository at https://github.com/hastingslab-org/ai-human-images. Inter-rater agreement was relatively poor, reflecting the subjectivity of the error classification task. Model comparisons revealed that DALL-E 3 performed consistently better than Stable Diffusion; however, the latter generated images reflecting more diversity in personal attributes. Images with groups of people were more challenging for all the models than individuals or pairs; some prompts were challenging for all models. Our method enables systematic comparison of AI-generated photorealistic images of humans; our results can serve to catalyse improvements in these models for medical applications.

A Comparative Study of Mean Square Error, Dimensions, Signal to Noise Ratio of Colored and Non-Colored Clustered Original Images Along with Compressed Version After the Image Segmentation and Filtering Method

Primarily author has already done one fundamental paper work on image clustering and segmentation but here in this paper author has continued that same type of work on clustered and segmented images as a mode of comparative study for author has chosen three different parameters like mean square error, peak SNR and dimensions of images (length, width, height). The author has all three parametric methods on one particular to justify the comparison. So this paper is a cumulative case of a comparative study for which author has chosen the above mentioned parameters to justify the best results of the clustered and segmented images.

Semantic Segmentation Model-Based Boundary Line Recognition Method for Wheat Harvesting

The wheat harvesting boundary line is vital reference information for the path tracking of an autonomously driving combine harvester. However, unfavorable factors, such as a complex light environment, tree shade, weeds, and wheat stubble color interference in the field, make it challenging to identify the wheat harvest boundary line accurately and quickly. Therefore, this paper proposes a harvest boundary line recognition model for wheat harvesting based on the MV3_DeepLabV3+ network framework, which can quickly and accurately complete the identification in complex environments. The model uses the lightweight MobileNetV3_Large as the backbone network and the LeakyReLU activation function to avoid the neural death problem. Depth-separable convolution is introduced into Atrous Spatial Pyramid Pooling (ASPP) to reduce the complexity of network parameters. The cubic B-spline curve-fitting method extracts the wheat harvesting boundary line. A prototype harvester for wheat harvesting boundary recognition was built, and field tests were conducted. The test results show that the wheat harvest boundary line recognition model proposed in this paper achieves a segmentation accuracy of 98.04% for unharvested wheat regions in complex environments, with an IoU of 95.02%. When the combine harvester travels at 0~1.5 m/s, the normal speed for operation, the average processing time and pixel error for a single image are 0.15 s and 7.3 pixels, respectively. This method could achieve high recognition accuracy and fast recognition speed. This paper provides a practical reference for the autonomous harvesting operation of a combine harvester.

Quantitative phase imaging verification in large field-of-view lensless holographic microscopy via two-photon 3D printing

Large field-of-view (FOV) microscopic imaging (over 100 mm2) with high lateral resolution (1–2 μm) plays a pivotal role in biomedicine and biophotonics, especially within the label-free regime. Lensless digital holographic microscopy (LDHM) is promising in this context but ensuring accurate quantitative phase imaging (QPI) in large FOV LDHM is challenging. While phantoms, 3D printed by two-photon polymerization (TPP), have facilitated testing small FOV lens-based QPI systems, an equivalent evaluation for lensless techniques remains elusive, compounded by issues such as twin-image and beam distortions, particularly towards the detector’s edges. Here, we propose an application of TPP over large area to examine phase consistency in LDHM. Our research involves fabricating widefield phase test targets with galvo and piezo scanning, scrutinizing them under single-shot twin-image corrupted conditions and multi-frame iterative twin-image minimization scenarios. By measuring the structures near the detector’s edges, we verified LDHM phase imaging errors across the entire FOV, with less than 12% phase value difference between areas. Our findings indicate that TPP, followed by LDHM and Linnik interferometry cross-verification, requires new design considerations for precise large-area photonic manufacturing. This research paves the way for quantitative benchmarking of large FOV lensless phase imaging, enhancing understanding and further development of LDHM technique.

Analysis of the Grid Quantization for the Microwave Radar Coincidence Imaging Based on Basic Correlation Algorithm

In Microwave Radar Coincidence Imaging (MRCI), the imaging region is typically discretized into a fine grid. In other words, it assumes that the equivalent scatterers of the target are precisely located at the centers of these pre-discretized grids. However, this approach usually encounters the off-grid problem, which can significantly degrade the imaging performance. In this paper, to establish a criterion for grid quantization, the performance of the MRCI system related to the grid size and the distribution of imaging points is investigated. First, the discretization of the imaging scene is regarded as a random sampling problem, and the off-grid imaging model for MRCI is established. Then, the probability distribution function (PDF) of the imaging amplitude for a single point target is analyzed, and the mean first-order imaging error (MFE) for multiple point targets is derived based on the Basic Correlation Algorithm (BCA). Finally, the relationship between the grid quantization of the imaging area and the performance of the MRCI system is analyzed, providing a theoretical guidance for enhancing the performance of MRCI. The validity of the analyses is verified through simulation experiments.

The extremely strong non-neutralized electric currents of the unique solar active region NOAA 13664

Context. In May 2024, the extremely complex active region National Oceanic and Atmospheric Administration (NOAA) 13664 produced the strongest geomagnetic storm since 2003. Aims.The aim of this study is to explore the development of the extreme magnetic complexity of NOAA 13664 in terms of its photospheric electric current. Methods. The non-neutralized electric current was derived from photospheric vector magnetograms, provided by the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory. The calculation method is based on image processing, thresholding, and error analysis. The spatial and temporal evolution of the non-neutralized electric current of the region as well as its constituent subregions was examined. For context, a comparison with other complex, flare-prolific active regions is provided. Results. Active region NOAA 13664 was formed by the emergence and interaction of three subregions, two of which were of notable individual complexity. It consisted of numerous persistent, current-carrying magnetic partitions that exhibited periods of conspicuous motions and strongly increasing electric current at many locations within the region. These periods were followed by intense and repeated flaring. The total unsigned non-neutralized electric currents and average injection rates reached 5.95 ⋅ 1013 A and 1.5 ⋅ 1013 A/day, and are the strongest observed so far, significantly surpassing other super-active regions of Solar Cycle 24 and 25. Conclusions. Active region NOAA 13664 presents a unique case of complexity. Further scrutiny of the spatial and temporal variation of the net electric currents during the emergence and development of super-active regions is paramount to understand the origin of complex regions and adverse space weather.

An Image Compensation-Based Range–Doppler Model for SAR High-Precision Positioning

The range–Doppler (R–D) model is extensively employed for the geometric processing of synthetic aperture radar (SAR) images. Refining the sensor motion state and imaging parameters is the most common method for achieving high-precision geometric processing using the R–D model, comprising a process that involves numerous parameters and complex computations. In order to reduce the specialization and complexity of parameter optimization in the classic R–D model, we introduced a novel approach called ICRD (image compensation-based range–Doppler) to improve the positioning accuracy of the R–D model, implementing a low-order polynomial to compensate for the original imaging errors without altering the initial positioning parameters. We also designed low-order polynomial compensation models with different parameters. The models were evaluated on various SAR images from different platforms and bands, including spaceborne TerraSAR-X and Gaofen3-C images, manned airborne SAR-X images, and unmanned aerial vehicle-mounted miniSAR-Ku images. Furthermore, image positioning experiments involving the use of different polynomial compensation models and various numbers and distributions of ground control points (GCPs) were conducted. The experimental results demonstrate that geometric processing accuracy comparable to that of the classical rigorous positioning method can be achieved, even when applying only an affine transformation model to the images. Compared to classical refinement models, however, the proposed image-compensated R–D model is much simpler and easy to implement. Thus, this study provides a convenient, robust, and widely applicable method for the geometric-positioning processing of SAR images, offering a potential approach for the joint-positioning processing of multi-source SAR images.

Feasibility study of YSO/SiPM based detectors for virtual monochromatic image synthesis.

The development of photon-counting CT systems has focused on semiconductor detectors like cadmium zinc telluride (CZT) and cadmium telluride (CdTe). However, these detectors face high costs and charge-sharing issues, distorting the energy spectrum. Indirect detection using Yttrium Orthosilicate (YSO) scintillators with silicon photomultiplier (SiPM) offers a cost-effective alternative with high detection efficiency, low dark count rate, and high sensor gain. This work aims to demonstrate the feasibility of the YSO/SiPM detector (DexScanner L103) based on the Multi-Voltage Threshold (MVT) sampling method as a photon-counting CT detector by evaluating the synthesis error of virtual monochromatic images. In this study, we developed a proof-of-concept benchtop photon-counting CT system, and employed a direct method for empirical virtual monochromatic image synthesis (EVMIS) by polynomial fitting under the principle of least square deviation without X-ray spectral information. The accuracy of the empirical energy calibration techniques was evaluated by comparing the reconstructed and actual attenuation coefficients of calibration and test materials using mean relative error (MRE) and mean square error (MSE). In dual-material imaging experiments, the overall average synthesis error for three monoenergetic images of distinct materials is 2.53% ±2.43%. Similarly, in K-edge imaging experiments encompassing four materials, the overall average synthesis error for three monoenergetic images is 4.04% ±2.63%. In rat biological soft-tissue imaging experiments, we further predicted the densities of various rat tissues as follows: bone density is 1.41±0.07 g/cm3, adipose tissue density is 0.91±0.06 g/cm3, heart tissue density is 1.09±0.04 g/cm3, and lung tissue density is 0.32±0.07 g/cm3. Those results showed that the reconstructed virtual monochromatic images had good conformance for each material. This study indicates the SiPM-based photon-counting detector could be used for monochromatic image synthesis and is a promising method for developing spectral computed tomography systems.