Image Quality Factors Research Articles

Analysis of the Effect of Ct Angiography Combined with Three Tests, Mmp-9 and Paf, On the Assessment of Postoperative Vascular Stenosis in Acute Coronary Syndrome

Background: Acute Coronary Syndrome (ACS) is a very common cardiovascular disease in clinical practice with a very high risk of death. In this study, we observed the effectiveness of CT angiography (CTA), Matrix metalloproteinase-9 (MMP-9) amd Platelet activating factor (PAF) in the assessment of ACS. Materials and Methods: A total of 124 patients with ACS admitted to our hospital from June 2022 to March 2024 were enrolled as study subjects. All study subjects were examined by Coronary angiography (CAG) and CTA. To compare the detection rate of ACS by CAG and CTA, and the difference in effectiveness in assessing coronary plaque stenosis and plaque calcification. In addition, the levels of MMP-9 and PAF were detected in the patients to analyze their relationship with the degree of stenosis and plaque grade.Subsequently, the effect of the three combined tests, CTA, MMP-9 and PAF, was analyzed for the assessment of postoperative vessel restenosis. Finally, we analyzed the factors associated with poor CTA image quality. Results: There was no difference in the effectiveness of CTA in detecting ACS, assessing coronary plaque stenosis and plaque calcification compared with CAG (P>0.05). There was a positive correlation between MMP-9, PAF and the degree of coronary stenosis and plaque severity (P<0.05).The diagnostic accuracy of the combination of CTA, MMP-9, and PAF in the diagnosis of stenosis in postoperative ACS was 97.58% (Kappa=0.946). It was determined that 124 patients 21 patients had poor CTA images, and logistic regression analysis showed that shorter breath-hold time and arrhythmia were independent risk factors for poor CTA image quality. Conclusions: CTA, MMP-9 and PAF are excellent for the diagnosis of stenosis after ACS and shorter breath-hold time and arrhythmia are all independent risk factors for poor CTA image quality.

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.

Pengaruh Influencer, Brand Image, dan Product Quality, Terhadap Keputusan Pembelian Sepatu Compass di Purwokerto

The purpose of this research is to analyze the influence of influencers on Instagram social media, brand image, and product quality on purchasing decisions for Compass shoes among the people of Purwokerto. This research is a type of quantitative research with a research sample of 146 respondents who live in Purwokerto. The sampling method used is. Purposive Sampling, namely selecting respondents who are willing and meet the specified sample criteria. For data analysis, the Structural Equation Modeling (SEM) method was used with the Smart PLS version 4.0 tool. The tests carried out include the outer model test to assess the validity and reliability of the construct, the inner model test to evaluate the relationship between latent variables, and hypothesis testing to test the significance of the influence of each independent variable on the dependent variable. The research results show that partially the brand image and product quality are proven to have a positive and significant influence on the decision to purchase Compass shoes. On the other hand, influencers do not have a significant influence on the decision to purchase Compass shoes. These findings show that brand image and product quality factors are more dominant in influencing purchasing decisions than the influence of influencers on Instagram.

A phantom study on the applicability of a detectability index in ultrasound imaging

The assessment of clinical image quality on ultrasound is currently often subjective. While image quality factors such as contrast response or depth of penetration can be evaluated semi-automatically, the evaluation of high contrast resolution requires test objects with specific inserts. The aim of this study was to evaluate the applicability of image quality metrics which were derived from Linear System Theory in the field of medical ultrasound imaging. Modular Transfer Function (MTF) and noise power spectrum (NPS) were determined on four phantoms. Image quality was assessed using a detectability index for different diameters. One phantom contained a cylinder filled with water, which appears as a circle in the US images. The other three phantoms were homogeneous and consisted of three different materials all based on PVA (polyvinyl alcohol). The basic phantom material was a 10% PVA hydrogel. The two other materials included microplastic spheres and starch to increase echogeneity. NPS and the MTF were determined using MATLAB routines. Two linear US transducers with bandwidths of 2.4–10 and 4–15 MHz were used to show the dependence of the index on the principal frequency of the US wave. The results show that for all phantom materials and object sizes (1–10 mm diameter), the detectability indices decreased with increasing penetration depth (from 6 to 10 cm). In addition, all indices of the higher frequency transducer were higher than those of the lower frequency transducer. When comparing the different phantom materials (PVA, PVA with starch and PVA with microspheres), different mean pixel value (MPV) were found, while the standard deviations for the materials were similar. This enabled us to evaluate the detectability index at different signal-to-noise ratios (SNR). Measures of image homogeneity (coefficient of the variance and variation) showed no significant difference to a commercial phantom (p-values ranging from 0.16 to 1, average p-value 0.5). These results suggest that the concept of a detectability index can also be applied to US imaging.

Hierarchical Image Quality Improvement Based on Illumination, Resolution, and Noise Factors for Improving Object Detection

Object detection performance is significantly impacted by image quality factors such as illumination, resolution, and noise. This paper proposes a hierarchical image quality improvement process that dynamically prioritizes these factors based on severity, enhancing detection accuracy in diverse conditions. The process evaluates each factor—illumination, resolution, and noise—using discriminators that analyze brightness, edge strength, and noise levels. Improvements are applied iteratively with an adaptive weight update mechanism that adjusts factor importance based on improvement effectiveness. Following each improvement, a quality assessment is conducted, updating weights to fine-tune subsequent adjustments. This allows the process to learn optimal parameters for varying conditions, enhancing adaptability. The image improved through the proposed process shows improved quality through quality index (PSNR, SSIM) evaluation, and the object detection accuracy is significantly improved when the performance is measured using deep learning models called YOLOv8 and RT-DETR. The detection rate is improved by 7% for the ‘Bottle’ object in a high-light environment, and by 4% and 2.5% for the ‘Bicycle’ and ‘Car’ objects in a low-light environment, respectively. Additionally, segmentation accuracy saw a 9.45% gain, supporting the effectiveness of this method in real-world applications.

Patient centered HCC surveillance - complementary roles of ultrasound and CT/MRI.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality worldwide and is the fastest growing cause of cancer death in the United States (U.S.) In the U.S., current national clinical practice guidelines from the 2023 American Association for the Study of Liver Diseases (AASLD) Practice Guidance and the recently updated Liver Imaging Reporting & Data Systems (LI-RADS) Ultrasound (US) Surveillance v2024 core recommend semi-annual serum α-fetoprotein and US screening of patients deemed to be high risk for developing HCC. In this article, we will explore the transition to a patient-centered approach to HCC surveillance, including the role of the new LI-RADS US Surveillance v2024 core and the use of visualization score for determining ultrasound quality, the known risk factors for poor US image quality, and the potential options for alternative surveillance strategies when US may not be a viable option for certain patients, including multiphasic computed tomography (CT), magnetic resonance imaging (MRI), and several abbreviated MRI protocols.

The Assessment of Retinal Image Quality Using a Non-Mydriatic Fundus Camera in a Teleophthalmologic Platform.

This study assesses the quality of retinal images captured using a non-mydriatic fundus camera within a teleophthalmologic platform in Taiwan. The objective was to evaluate the effectiveness of non-mydriatic fundus cameras for remote retinal screening and identify factors impacting image quality. From June 2020 to August 2022, 629 patients from five rural infirmaries underwent ophthalmic examinations, with fundus images captured without pupil dilation. These images were reviewed by senior ophthalmologists and graded based on quality. The results indicated that approximately 70% of images were of satisfactory diagnostic quality. Risk factors for poor image quality included older age, the presence of cataracts, pseudophakia, and diabetes mellitus. This study demonstrates the feasibility of using non-mydriatic fundus cameras for teleophthalmology, highlighting the importance of identifying and addressing factors that affect image quality to enhance diagnostic accuracy in remote settings.

Perspectives on technology: Prostate Imaging-Reporting and Data System (PI-RADS) interobserver variability.

To explore the topic of Prostate Imaging-Reporting and Data System (PI-RADS) interobserver variability, including a discussion of major sources, mitigation approaches, and future directions. A narrative review of PI-RADS interobserver variability. PI-RADS was developed in 2012 to set technical standards for prostate magnetic resonance imaging (MRI), reduce interobserver variability at interpretation, and improve diagnostic accuracy in the MRI-directed diagnostic pathway for detection of clinically significant prostate cancer. While PI-RADS has been validated in selected research cohorts with prostate cancer imaging experts, subsequent prospective studies in routine clinical practice demonstrate wide variability in diagnostic performance. Radiologist and biopsy operator experience are the most important contributing drivers of high-quality care among multiple interrelated factors including variability in MRI hardware and technique, image quality, and population and patient-specific factors such as prostate cancer disease prevalence. Iterative improvements in PI-RADS have helped flatten the curve for novice readers and reduce variability. Innovations in image quality reporting, administrative and organisational workflows, and artificial intelligence hold promise in improving variability even further. Continued research into PI-RADS is needed to facilitate benchmark creation, reader certification, and independent accreditation, which are systems-level interventions needed to uphold and maintain high-quality prostate MRI across entire populations.

Accelerated Diffusion-Weighted Magnetic Resonance Imaging of the Liver at 1.5 T With Deep Learning-Based Image Reconstruction: Impact on Image Quality and Lesion Detection.

To perform image quality comparison between deep learning-based multiband diffusion-weighted sequence (DL-mb-DWI), accelerated multiband diffusion-weighted sequence (accelerated mb-DWI), and conventional multiband diffusion-weighted sequence (conventional mb-DWI) in patients undergoing clinical liver magnetic resonance imaging (MRI). Fifty consecutive patients who underwent clinical MRI of the liver at a 1.5-T scanner, between September 1, 2021, and January 31, 2022, were included in this study. Three radiologists independently reviewed images using a 5-point Likert scale for artifacts and image quality factors, in addition to assessing the presence of liver lesions and lesion conspicuity. DL-mb-DWI acquisition time was 65.0 ± 2.4 seconds, significantly ( P < 0.001) shorter than conventional mb-DWI (147.5 ± 19.2 seconds) and accelerated mb-DWI (94.3 ± 1.8 seconds). DL-mb-DWI received significantly higher scores than conventional mb-DWI for conspicuity of the left lobe ( P < 0.001), sharpness of intrahepatic vessel margin ( P < 0.001), sharpness of the pancreatic contour ( P < 0.001), in-plane motion artifact ( P = 0.002), and overall image quality ( P = 0.005) by reader 2. DL-mb-DWI received significantly higher scores for conspicuity of the left lobe ( P = 0.006), sharpness of the pancreatic contour ( P = 0.020), and in-plane motion artifact ( P = 0.042) by reader 3. DL-mb-DWI received significantly higher scores for strength of fat suppression ( P = 0.004) and sharpness of the pancreatic contour ( P = 0.038) by reader 1. The remaining quality parameters did not reach statistical significance for reader 1. Novel diffusion-weighted MRI sequence with deep learning-based image reconstruction demonstrated significantly decreased acquisition times compared with conventional and accelerated mb-DWI sequences, while maintaining or improving image quality for routine abdominal MRI. DL-mb-DWI offers a potential alternative to conventional mb-DWI in routine clinical liver MRI.

Accelerating acquisition of readout-segmented echo planar imaging (rs-EPI) with a simultaneous multislice (SMS) technique for diffusion-weighted (DW) breast MRI: Evaluation of image quality factors

PurposeThis study aims to compare the image quality, apparent diffusion coefficient (ADC), signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) values, and scan time between readout-segmented echo planar imaging (rs-EPI) and simultaneous multislice (SMS) rs-EPI sequences. MethodsA total of 80 consecutive women who underwent breast diffusion-weighted imaging (DWI) were included, and two rs-EPI DWI sequences with and without SMS were acquired and compared. Qualitative analysis involved three radiologists independently scoring image quality and radiologist preference. For quantitative comparison, the radiologists independently measured the ADC values in patients, while SNR, CNR, and ADC values were measured on a phantom. ResultsThe acquisition time was 5:47 min for rs-EPI and 3:20 min for SMS rs-EPI. In terms of image quality, scores were similar between rs-EPI and SMS rs-EPI sequences in the pooled data set, with the exception of skin-line distinction (p = 0.001) and background noise (p < 0.001). All radiologists considered SMS rs-EPI as equal or superior to rs-EPI in more than 70 % of cases. SMS rs-EPI demonstrated significantly higher ADC values than rs-EPI by all radiologists (p ≤ 0.002). For the phantom measurement, ADC (SMS: 1.26 ± 0.68 and RS: 1.26 ± 0.68, p = 0.198), SNR (SMS: 540.6 ± 342.1 and RS: 558.8 ± 523.2, p = 0.927), and CNR (SMS: 235.5 ± 38.9 and RS: 252.8 ± 108.0, p = 0.784) values did not significantly differ between the two sequences. ConclusionSMS rs-EPI exhibited comparable image quality and similar ADC, SNR, and CNR values to rs-EPI while reducing the scan time by 42%.

Utilizing deep learning techniques to improve image quality and noise reduction in preclinical low-dose PET images in the sinogram domain.

Low-dose positron emission tomography (LD-PET) imaging is commonly employed in preclinical research to minimize radiation exposure to animal subjects. However, LD-PET images often exhibit poor quality and high noise levels due to the low signal-to-noise ratio. Deep learning (DL) techniques such as generative adversarial networks (GANs) and convolutional neural network (CNN) have the capability to enhance the quality of images derived from noisy or low-quality PET data, which encodes critical information about radioactivity distribution in the body. Our objective was to optimize the image quality and reduce noise in preclinical PET images by utilizing the sinogram domain as input for DL models, resulting in improved image quality as compared to LD-PET images. A GAN and CNN model were utilized to predict high-dose (HD) preclinical PET sinograms from the corresponding LD preclinical PET sinograms. In order to generate the datasets, experiments were conducted on micro-phantoms, animal subjects (rats), and virtual simulations. The quality of DL-generated images was weighted by performing the following quantitative measures: structural similarity index measure (SSIM), root mean squared error (RMSE), peak signal-to-noise ratio (PSNR), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR). Additionally, DL input and output were both subjected to a spatial resolution calculation of full width half maximum (FWHM) and full width tenth maximum (FWTM). DL outcomes were then compared with the conventional denoising algorithms such as non-local means (NLM), block-matching, and 3D filtering (BM3D). The DL models effectively learned image features and produced high-quality images, as reflected in the quantitative metrics. Notably, the FWHM and FWTM values of DL PET images exhibited significantly improved accuracy compared to LD, NLM, and BM3D PET images, and just as precise as HD PET images. The MSE loss underscored the excellent performance of the models, indicating that the models performed well. To further improve the training, the generator loss (G loss) was increased to a value higher than the discriminator loss (D loss), thereby achieving convergence in the GAN model. The sinograms generated by the GAN network closely resembled real HD preclinical PET sinograms and were more realistic than LD. There was a noticeable improvement in image quality and noise factor in the predicted HD images. Importantly, DL networks did not fully compromise the spatial resolution of the images.

73‐2: Optimized WRGB OLED Reflection Properties to Enhance Visual Quality in Gaming Displays

Immersiveness is a crucial element in gaming, and the quality of visual display is essential to achieving it. Reflections on gaming displays can hinder immersion under various lighting conditions. Four gaming displays were compared though reflection analysis and visual assessment. In this paper, we found that Lambertian reflectance was a crucial factor for image quality attributes such as Blackness, Contrast, and Sharpness. By applying AGAR surface treatment to WRGB OLEDs, the distinctness of reflected images was effectively reduced while maintaining blackness, making it a more suitable for playing games in various ambient environments.

Petrochemical Equipment Tracking by Improved Yolov7 Network and Hybrid Matching in Moving Scenes.

Petrochemical equipment tracking is a fundamental and important technology in petrochemical industry security monitoring, equipment working risk analysis, and other applications. In complex scenes where the multiple pipelines present different directions and many kinds of equipment have huge scale and shape variation in seriously mutual occlusions captured by moving cameras, the accuracy and speed of petrochemical equipment tracking would be limited because of the false and missed tracking of equipment with extreme sizes and severe occlusion, due to image quality, equipment scale, light, and other factors. In this paper, a new multiple petrochemical equipment tracking method is proposed by combining an improved Yolov7 network with attention mechanism and small target perceive layer and a hybrid matching that incorporates deep feature and traditional texture and location feature. The model incorporates the advantages of channel and spatial attention module into the improved Yolov7 detector and Siamese neural network for similarity matching. The proposed model is validated on the self-built petrochemical equipment video data set and the experimental results show it achieves a competitive performance in comparison with the related state-of-the-art tracking algorithms.

Pengaruh Brand Image, Brand Trust dan Product Quality terhadap Keputusan Pembelian Produk EcoKing di Kota Batam

The advancement of digital technology today makes the development of business strategies grow rapidly. So that every company can run its business well, the company continues to strive to develop innovation and creativity to compete in market competition. The purpose of this research is to determine the effect of brand image, brand trust and product quality on purchasing decisions for EcoKing products in Batam City. This study applies a quantitative method by determining a sample of 100 respondents to consumers who are in the city of Batam, specifically the sub-district of Batam, a city that is a user of Ecoking lamp products. Questionnaires are used as a technique in collecting data. With the help of SPSS 26, data analysis techniques include descriptive statistical tests, data quality tests, classical assumption tests, influence tests, and hypothesis testing. Based on the results of multiple linear regression, brand image (X1) has an effect of 0.231 or 23.1% on purchasing decisions, brand trust (X2) has an effect of 0.424 or 42.4% on purchasing decisions and product quality (X3) has an effect of 0.231 or 23 .1% on purchasing decisions. Brand image, brand trust and product quality factors influence the decision to purchase EcoKing products in Batam City by 67.4% based on the results of the coefficient of determination test. Based on the results of hypothesis testing, it was found that brand image, brand trust and product quality had a positive and significant effect partially or simultaneously on purchasing decisions for EcoKing products in Batam City.

Enhancing Privacy of information with Data Embedding in Medical MRI Images Based on Segmentation and HVS Model

The development of communications technology and the arrival of Medicine modern equipment in the health domain has caused the diagnosis and treatment methods to be considered from a distance and medical centers are equipped with telemedicine systems. Forensic medicine organizations with daily clients and outpatient examinations such as accident clients, Conflict returns as well as spousal abuse. Doctors and employees of the organization also serve as one of the powerful arms of the judiciary, following up on important cases in the medical, laboratory, and psychiatric commissions So that they can take steps to realize the rights of the people. Patient data security has become a serious concern for professionals and one of the methods is using data embedding to the protection against these risks. In this method, medical informatics, telemedicine, and forensic medicine organizations has played a pivotal role and any mistake in the reporting can be catastrophic. The main purpose of this research is to present data on EPRs with Enhancing data embedding Based on SSIM and HVS with the help of medical image segmentation and focus on brain MRI images. In this study, innovations include the addition of the HVS block based on the SSIM criterion to meet transparency and robustness conditions. Selection of the embedding coefficient K is considered adaptively depending on the degree of uniformity of the N - ROI region with the image quality factor. The coordinates of ROI areas in one of the DCT and DWT conversion blocks have been demonstrated to have better performance at Concealment EPRs. The choice of coefficients af,k which consists of the optimization frequency sensitivity function and spatial property is comparatively done to match the visual perception of the visual system. The present study aims to improve the effectiveness of the proposed method, improve the security level, and the confidentiality of patient information, and integrate the storage of patient information and image. The simulation results of the proposed method, considering the parameters of embedding and transparency in comparison with other methods, have been done using evaluation criteria including MSE PSNR, NC, SSIM, and BER.

Super-resolution method for SEM images based on pixelwise weighted loss function.

Scanning electron microscopy (SEM) has realized high-throughput defect monitoring of semiconductor devices. As miniaturization and complexification of semiconductor circuit patterns increase in recent years, so has the number of defects. There is thus a great need to further increase the throughput of SEM defect monitoring. Toward this end, we propose a deep learning-based super-resolution method that reproduces high-resolution (HR) images from corresponding low-resolution images. Image quality factors such as pattern contrast and sharpness are important in SEM HR images in order to evaluate the quality of printed circuit patterns. Our proposed method meets various image quality requirements by changing the loss calculation method pixelwise based on the pattern in the image. It realizes super-resolved images that compare favorably with actual HR images and can improve SEM throughput by 100% or more.

Multiocular disease detection using a generic framework based on handcrafted and deep learned feature analysis

Diabetic retinopathy (DR) and Glaucoma are two major ocular diseases that lead to vision impairment if not detected and treated promptly. Manual detection and diagnosis of these diseases is a laborious and time-consuming process. Computer aided diagnosis (CAD) systems can serve to assist physicians in manual diagnosis. This work aims to develop a generic multiocular CAD system for DR and glaucoma diagnosis, which could serve as a boon in a large scale screening scenario by reducing the time and manpower involved. To this end, a segmentation-independent approach is employed that eliminates the need for individual diagnostic systems each involving a set of disease-specific algorithms for localisation and analysis of regions of interest (ROIs). Further, the proposed approach alleviates the segmentation inaccuracies attributed to image quality and anatomical factors, which have a cascaded effect on the classification performance. Specifically, a machine learning (ML) model based on random forest (RF) classifier and a pool of non-linear features including higher order spectra (HOS), entropy and fractal features, was developed. An ensemble of this ML model and convolutional neural network (CNN)-based deep learning (DL) model was further constructed using the ‘sum rule’ for decision fusion. The proposed ensemble model resulted in accuracy, sensitivity and specificity as high as 98.08%, 98.37% and 99.07% respectively, for three-class classification to categorize samples as normal, DR or glaucoma.

VCX Version 2023 The latest transparent and objective mobile phone test scheme

VCX or Valued Camera eXperience is a nonprofit organization dedicated to the objective and transparent evaluation of mobile phone cameras. The members continuously work on the development of a test scheme that can provide an objective score for the camera performance. Every device is tested for a variety of image quality factors while these typically based on existing standards. This paper presents that latest development with the newly released version 2023 and the process behind it. New metric included are extended tests on video dynamics, AE and AWB, dedicated tests on ultra wide modules and adjustments to the metric system based on a large scale subjective study.

GraphIQA: Learning Distortion Graph Representations for Blind Image Quality Assessment

A good distortion representation is crucial for the success of deep blind image quality assessment (BIQA). However, most previous methods do not effectively model the relationship between distortions or the distribution of samples with same distortion type but different distortion levels. In this work, we start from the analysis of the relationship between perceptual image quality and distortion-related factors, such as distortion types and levels. Then, we propose a Distortion Graph Representation (DGR) learning framework for IQA, named GraphIQA, in which each distortion is represented as a graph, i.e., DGR. One can distinguish distortion types by learning the contrast relationship between these different DGRs, and infer the ranking distribution of samples from different levels in a DGR. Specifically, we develop two sub-networks to learn the DGRs: a) Type Discrimination Network (TDN) that aims to embed DGR into a compact code for better discriminating distortion types and learning the relationship between types; b) Fuzzy Prediction Network (FPN) that aims to extract the distributional characteristics of the samples in a DGR and predicts fuzzy degrees based on a Gaussian prior. Experiments show that our GraphIQA achieves the state-of-the-art performance on many benchmark datasets of both synthetic and authentic distortions. The code is available at http://staff.ustc.edu.cn/~chenzhibo/resources/2021/GraphIQA.html.

Design of X-ray Images Based on Quantitative Analysis: Predictive Model of Image Quality and Physical Quantity Factors by Multiple Regression Equation of Visual Evaluation (Scheffé's) and Physical Quantities (Dose and Noise)

The Scheffé's method obtains the difference between pair comparisons with that of the interval scale and can judge the superiority or inferiority of the sample to be compared with no restriction in the observation image by the statistical significant difference. However, the Scheffé's method cannot be judged as a single image quality indicator. Therefore, I examined a method that can evaluate the association of average degree of preference of Scheffé's method and the physical quantities that make up the image. This study focuses on the fact that the average degree of preference of the Scheffé's method is quantitative data on the interval scale and that multiple regression analysis is possible. In the multiple regression analysis, the average degree of preference by imaging simulated pulmonary adenocarcinoma with different exposure doses was used as the objective variable and the exposure doses and noise (standard deviation [SD]) were used as the explanatory variables. The Scheffé's method used the Nakaya's modified method. In the multiple regression analysis, SD was P=0.027. By substituting the threshold value of the intersection of the exposure doses and SD into the multiple regression equation (predictive model), the average degree of preference (Ŷ) was calculated. Ŷ(Scheffé;Gy,SD) was -0.147, which was about 1/2 of exposure doses (-0.150). The multiple regression analysis of Scheffé's (average degree of preference) and physical quantity factors (exposure doses and noise) has made it possible to design images that can reduce exposure doses while maintaining adequate image quality.