Document image segmentation and quality improvement by moiré pattern analysis

Reproduction couleur par trames irrégulières et semi-régulières

In the paper the features of protocols and the basic methods of organization of multimedia information transfer in telecommunication networks and distributed corporate safety networks are represented. The analysis of formats of basic elements of signals used in standards of multimedia information transfer is given.

Summary This case study summarizes the data reprocessing effort that has been applied to seismic data located in the Algerian on-shore over a structurally complex, low porosity, low permeability reservoir and the improvements in final image quality that have been achieved. Subsequent to processing, the seismic data were inverted to rock properties. The final results show seismic data with significant improvements in image quality and structural definition, whilst the inversion to rock properties has enabled lithology and porosity characterization with increased confidence. Furthermore, borehole seismic surveys were acquired and integrated in the 3D volume to design the trajectory of horizontal and side-track wells.

Optoacoustic (photoacoustic) imaging has already showcased the capacity to offer high-resolution small animal visualization in vivo in a variety of cancer, cardiovascular, or neuroimaging applications. In particular, multispectral optoacoustic tomography (MSOT) has shown imaging along the spectral and the time dimensions, enabling sensing of multiple molecules over time and, more recently, in real time. Furthermore, cross-sectional imaging of at least 20 mm diameter has been showcased in vivo in animals and humans using 64-element curved transducers placed along a single curved line. Herein, we investigated the imaging improvements gained by utilizing a larger number of detectors and inquired whether more detectors will result in measurable image quality improvements. For this reason, we implemented MSOT using 64-, 128-, and 256-element transducers and imaged the same phantoms and animals under similar conditions. Further, corroborated by numerical simulation analysis, our findings quantify the improvements in resolution and overall image quality for the increasing number of detectors used pointing to significant improvements in image quality for the 256 detector array, over 64 or 128 detectors.

In this paper, the issues of controlling and improving the image quality of a high-temperature vision system are considered. The image quality improvement is needed to measure the surface properties of metals and alloys. Two levels of image quality control and improvement are defined in the system. The first level in hardware aims at adjusting the system configuration to obtain the highest contrast and weakest aura images. When optimal configuration is obtained, the second level in software is applied. In this stage, image enhancement algorithms are applied which have been developed with consideration of distortions arising from the vision system components and specificity of images acquired during the measurement process. The developed algorithms have been applied in the vision system to images. The influence on the accuracy of wetting angles and surface tension determination are considered.

The purpose of this study was to investigate the efficacy of a retrospective adaptive motion correction technique known as autocorrection for reducing motion-induced artifacts in high-resolution three-dimensional time-of-flight MR angiography of the circle of Willis. Ten consecutive volunteers were imaged with an unenhanced gradient-recalled echo three-dimensional time-of-flight MR angiography sequence of the circle of Willis. Each volunteer was asked to rotate approximately 2 degrees after completion of one third and one half of the acquisition in the axial, sagittal, and oblique planes (45 degrees to the axial and sagittal planes). A single static data set was also acquired for each volunteer. Unprocessed and autocorrected maximum-intensity-projection images were reviewed as blinded image pairs by six radiologists and were compared on a five-point image quality scale. Mean improvement in image quality after autocorrection was 1.4 (p < 0.0001), 1.1 (p < 0.0001), and 0.2 (p = 0.003) observer points (maximum value, 2.0), respectively, for examinations corrupted by motion in the axial, oblique, and sagittal planes. All three axes had statistically significant improvement in image quality compared with the uncorrected images. Changes in image quality after the application of the autocorrection algorithm to static angiogram data were not statistically significant (mean change in score = -0.13 points; p = 0.29). Autocorrection can reduce artifacts in motion-corrupted MR angiography of the circle of Willis without distorting motion-free examinations.

25 Years of 3D ultrasound in prenatal diagnosis (1989-2014).

The purpose was to evaluate the potential of the multidimensional adaptive filtering (MAF) technique by investigating its effects on image noise and image quality in multislice spiral CT (MSCT) examinations of the head and neck region. Fifty patients with head and neck tumors were examined using MSCT with a high resolution protocol. Reconstructions were performed using dedicated reconstruction software with a standard algorithm both without and with MAF using different modification. In all reconstructions, we measured the noise in seven different anatomical structures. The image quality and image noise were rated on a five-point scale. There was a significant (P<0.05) reduction in mean pixel noise in the reconstructions using MAF in comparison to the standard reconstructions, but there was no significant difference between the different modification fractions. With MAF the mean reduction in noise level was 60%, depending upon body shape and anatomical region. Independently from the used modification fraction, MAF led to a significant (P<0.05) improvement of image quality. In direct comparison of the different filter strength, the optimal image quality was achieved in the investigations with 15% MAF. The use of MAF facilitates the distinction of anatomical and pathological structures from artifacts in the supraclavicular fossae and the upper mediastinum, whereas the image quality of the upper portions of the neck remained unchanged. MAF improved image quality by reducing the noise level and removing noise structures without loss of image sharpness. This technique offers new perspectives to reduce the patient dose.

In any practical impedance imaging system it is important to be able to predict the image quality which can be expected from particular measurements. It is of interest both to establish the smallest object that can be detected for a certain noise level and to determine the maximum resolution for a certain number of electrodes. In impedance imaging this is not straightforward. The reason is that the resolution and the accuracy of an image which represents a conductive region are related to the number of electrodes and to the noise on the measurements. They also vary with position in the image and depend on the particular distribution of conductivity itself. It is therefore not possible, in general, to make quantitative statements about the resolution and accuracy. It is of course possible to make qualitative statements, but they are not of much use in any particular situation. Formulations are presented here which do allow quantitative assessment of the resolution and accuracy in a certain class of conductive regions. The regions to which they apply are two-dimensional and have a circular boundary shape. The details of the approach are included, both mathematically and descriptively. The quantitative improvement in image quality which can be obtained by reducing the noise, is shown both in terms of accuracy and resolution. The limit to the improvement in quality which can be obtained by taking unlimited independent measurements (i.e. using an unlimited number of electrodes) is calculated. It is shown how to predict the smallest sized object that can just be detected by measurements with a known level of noise.

Introduction The accuracy of echocardiography interpretations is crucial. However, echocardiography has varying degrees of image quality, which can impact diagnostic decision-making. Automated chamber quantification of the left ventricle (LV) and left atrium (LA) has been shown to be feasible using machine learning (ML) systems. However, the effect of image quality on the performance of these ML systems has not been investigated. Purpose To assess the effect of different echocardiographic image quality on the performance of left-sided heart quantification using an ML system. Methods A multi-center study enrolled 240 patients who underwent stress echocardiography across five centers. The acquired rest phase images were anonymized for subsequent analysis. Expert cardiologists reviewed the studies, selecting apical four-chamber (4Ch) and two-chamber (2Ch) view images. The cardiologists identified end-systolic (ES) and end-diastolic (ED) frames. They traced the endocardial borders for the LA in ES and the LV in ED and ES. The same ED and ES frames were then processed by an ML algorithm trained on a separate set of images. Subsequently, an expert cardiologist, blinded to the previous measurements, assessed the image quality of LA and LV. The image quality for each chamber in the ES or ED frame was categorized into one of four grades: poor, suboptimal, fair, and optimal. Agreement analysis between human and AI measurements was performed within each image quality group. Yield was defined as the percentage of studies where the ML system predicted an echocardiographic parameter compared to a cardiologist. Results The yield of study parameters varied significantly with image quality. The lowest yield was 45%, observed for LV end-diastolic volume in the 2Ch view when image quality was poor. In contrast, the yield of 100% was seen for LV end-diastolic volumes in both the 4Ch and 2Ch views for optimal quality images. The correlation between automated and expert cardiologist measurements of left heart volumes varied depending on image quality. For poor or suboptimal images, the Pearson correlation coefficient ranged from 0.66 to 0.91 and improved to 0.85-0.92 in fair and optimal-quality images. Additionally, there was an inverse relation between agreement metrics (bias, limits of agreement, and coefficient of variation) and improvements in image quality. However, this tendency was less pronounced or nonexistent when comparing fair and optimal-quality images. (Table 1). Conclusions Image quality affects the yield of automated analysis and the agreement between cardiologists and automated measurements in echocardiography, highlighting the need for high-quality image acquisition in use with ML systems. Table 1

<strong>Purpose:</strong> Accurate assessment of the size and location of osteolytic regions is essential in minimally invasive hip revision surgery. Moreover, image-guided robotic intervention for osteolysis treatment requires precise localization of implant components. However, high density metallic implants in proximity to the hip make assessment by either 2D or 3D x-ray imaging difficult. This paper details the initial implementation and evaluation of an advanced model-based conebeam CT (CBCT) reconstruction algorithm to improve guidance and assessment of hip osteolysis treatment. <strong>Method:</strong> A model-based reconstruction approach called Known Component Reconstruction (KCR) was employed to obtain high-quality reconstruction of regions neighboring metallic implants. KCR incorporates knowledge about the implant shape and material to precisely reconstruct surrounding anatomy while simultaneously estimating implant position. A simulation study involving a phantom generated from a CBCT scan of a cadaveric hip was performed. Registration accuracy in KCR iterations was evaluated as translational and rotational error from the true registration. Improvement in image quality was evaluated using normalized cross correlation (NCC) in two regions of interest (ROIs) about the femoral and acetabular components. <strong>Result:</strong> The study showed significant improvement in image quality over conventional filtered backprojection (FBP) and penalized-likelihood (PL) reconstruction. The NCC in the two ROIs improved from 0.74 and 0.81 (FBP) to 0.98 and 0.86 (PL) and &gt;0.99 for KCR. The registration error was 0.01 mm in translation (0.02&deg; in rotation) for the acetabular component and 0.01 mm (0.01&deg; rotation) for the femoral component. <strong>Conclusions:</strong> Application of KCR to imaging hip osteolysis in the presence of the implant offers a promising step toward quantitative assessment in minimally invasive image-guided osteolysis treatment. The method improves image quality (metal artifact reduction), yields a precise registration estimate of the implant, and offers a means for reducing radiation dose in intraoperative CBCT.

IntroductionLow back pain is a global health issue causing disability and missed work days. Commonly used MRI scans including T1-weighted and T2-weighted images provide detailed information of the spine and surrounding tissues. Artificial intelligence showed promise in improving image quality and simultaneously reducing scan time. This study evaluates the performance of deep learning (DL)-based T2 turbo spin-echo (TSE, T2DLR) and T1 TSE (T1DLR) in lumbar spine imaging regarding acquisition time, image quality, artifact resistance, and diagnostic confidence.Material and methodsThis retrospective monocentric study included 60 patients with lower back pain who underwent lumbar spinal MRI between February and April 2023. MRI parameters and DL reconstruction (DLR) techniques were utilized to acquire images. Two neuroradiologists independently evaluated image datasets based on various parameters using a 4-point Likert scale.ResultsAccelerated imaging showed significantly less image noise and artifacts, as well as better image sharpness, compared to standard imaging. Overall image quality and diagnostic confidence were higher in accelerated imaging. Relevant disk herniations and spinal fractures were detected in both DLR and conventional images. Both readers favored accelerated imaging in the majority of examinations. The lumbar spine examination time was cut by 61% in accelerated imaging compared to standard imaging.ConclusionIn conclusion, the utilization of deep learning-based image reconstruction techniques in lumbar spinal imaging resulted in significant time savings of up to 61% compared to standard imaging, while also improving image quality and diagnostic confidence. These findings highlight the potential of these techniques to enhance efficiency and accuracy in clinical practice for patients with lower back pain.

As a part of quality assurance program, film reject analysis was conducted in the three most occupied hospitals in Khartoum, Sudan. Initially, 959 films were evaluated for a period of 12 days in eight x-ray rooms. The film repeat causes were evaluated by radiographers and radiologists. Reasons for reject films were identified and corrective actions were accordingly applied; investigation was done once again by analyzing a total number of 1803 films for the same period. In one hospital a repeat rate was reduced from 16.64% to 9.75% due to radiography factors, while in another from 16.11% to 3.81% due to processor factor. The reject causes rates were ranged from 14.2 % to 32%, 3.6 to 16 and from 1.7% to 11.3% due to over/under exposure, processing problems and wrong positioning respectively. Significant improvement in image quality was observed. The results showed that improvements in image quality and dose reduction could be achieved using simple reject film analysis.

Control of the higher eigenmodes of a microcantilever: Applications in atomic force microscopy

Introduction CT coronary angiography (CTCA) is widely applied in coronary artery disease. It is therefore important that imaging protocols are optimised to obtain diagnostic images at the lowest possible radiation dose. We aimed to establish the feasibility of single heart-beat imaging in 320-multidetector CTCA, and assess variables that affect image quality. Methods Consecutive patients (n=249, 38% male) underwent contrast enhanced prospective electrocardiogram-gated single heart-beat CTCA. We assessed images before and after the introduction of sublingual glyceryl trinitrate to the CTCA protocol. Tube current and voltage were selected based on body-mass index (BMI) and acquisition window was selected based on heart rate (70–80% or 30–80% if the heart rate was below or above 65 beats/min respectively). Images were assessed by two trained observers and image quality was graded on a 4-point scale (1, excellent; 4, poor). Obstructive disease was defined as a stenosis of greater than 70% diameter. Results The mean heart rate was 60 beats/min (95% CI 59 to 62), BMI 29 kg/m2 (28, 30), age 58 years (56, 59) and dose-length product 283 mGy cm (266, 301). During scanning, 133(51%) received sublingual glyceryl trinitrate (GTN), 9(4%) had ectopic beats, and 12(5%) had atrial fibrillation. Diagnostic image quality was obtained in 99% with mean image quality 1.4 (1.3, 1.5). For patients with a BMI of 30 or above (n=111) diagnostic image quality was obtained in 109 scans (98%) and excellent image quality was obtained in 78 scans (70%). For patients with a heart rate above 65 bpm (n=58) diagnostic image quality was obtained in 98% (57 scans) and excellent image quality was obtained in 36% (21 scans). The mean dose-length product was 283 mGy cm (266, 301). An increase in heart rate led to an increase in radiation dose to 377 mGy cm (286, 268) for patients with a heart rate of greater than 80 beats/min (p&lt;0.001). A higher radiation dose was also associated with a BMI above 30 (DLP 231 (213, 248) vs 353 (330, 377), p&lt;0.001) and the presence of arrhythmia (DLP 275 (259, 290) vs 384 (301, 467), p 0.013). Age, sex, atrial fibrillation, ectopics, diabetes mellitus (12%) and the presence of obstructive coronary disease were not related to image quality. In multivariate regression analysis a lower heart rate and GTN were associated with improved image quality (β 0.439, p&lt;0.001 and β −0.199, p=0.001 respectively). Figure 1 The effect of heart rate, BMI and arrhythmia on the proportion of CTCA with diagnostic image quality. Conclusions Optimal image quality in single heart-beat 320-multidetector CTCA is achievable at low radiation doses in 99% of unselected patients. Image quality is further improved by lower heart rate and sublingual GTN. This study has established the use of half-segment reconstruction in all patients undergoing wide volume CTCA irrespective of heart rate, ectopic beats and the presence of atrial fibrillation. A widened window of acquisition can be used to improve image quality in patients with a heart rate greater than 65 beats per minute. In addition we have established that the use of GTN improves image quality in 320 multidetector CTCA.