Theoretical limits to sensitivity and resolution in impedance imaging.

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.

Reduction of exposure dose and improvement in image quality can be expected to result from advances in the performance of imaging detectors. We propose a computerized method for determining optimized imaging conditions by use of simulated images. This study was performed to develop a prototype system for image noise and to ensure consistency between the resulting images and actual images. An RQA5 X-ray spectrum was used for determination of input-output characteristics of a flat-panel detector (FPD). The number of incident quantum to the detector per pixel (counts/pixel) was calculated according to the pixel size of the detector and the quantum number in RQA5 determined in IEC6220-1. The relationship among tube current-time product (mAs), exposure dose (C/kg) at the detector surface, the number of incident quanta (counts/pixel), and pixel values measured on the images was addressed, and a conversion function was then created. The images obtained by the FPD was converted into a map of incident quantum numbers and input into random-value generator to simulate image noise. In addition, graphic user interface was developed to observe images with changing image noise and exposure dose levels, which have trade-off relationship. Simulation images provided at different noise levels were compared with actual images obtained by the FPD system. The results indicated that image noise was simulated properly both in objective and subjective evaluation. The present system could allow us to determine necessary dose from image quality and also to estimate image quality from any exposure dose.

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.

Document image segmentation and quality improvement by moiré pattern analysis

ObjectivesA deep learning-based super-resolution for postcontrast volume-interpolated breath-hold examination (VIBE) of the chest was investigated in this study. Aim was to improve image quality, noise, artifacts and diagnostic confidence without change of acquisition parameters.Materials and methodsFifty patients who received VIBE postcontrast imaging of the chest at 1.5 T were included in this retrospective study. After acquisition of the standard VIBE (VIBES), a novel deep learning-based algorithm and a denoising algorithm were applied, resulting in enhanced images (VIBEDL). Two radiologists qualitatively evaluated both datasets independently, rating sharpness of soft tissue, vessels, bronchial structures, lymph nodes, artifacts, cardiac motion artifacts, noise levels and overall diagnostic confidence, using a Likert scale ranging from 1 to 4. In the presence of lung lesions, the largest lesion was rated regarding sharpness and diagnostic confidence using the same Likert scale as mentioned above. Additionally, the largest diameter of the lesion was measured.ResultsThe sharpness of soft tissue, vessels, bronchial structures and lymph nodes as well as the diagnostic confidence, the extent of artifacts, the extent of cardiac motion artifacts and noise levels were rated superior in VIBEDL (all P < 0.001).There was no significant difference in the diameter or the localization of the largest lung lesion in VIBEDL compared to VIBES. Lesion sharpness as well as detectability was rated significantly better by both readers with VIBEDL (both P < 0.001).ConclusionThe application of a novel deep learning-based super-resolution approach in T1-weighted VIBE postcontrast imaging resulted in an improvement in image quality, noise levels and diagnostic confidence as well as in a shortened acquisition time.

Deep learning (DL) reconstruction has been successful in realizing otherwise impracticable acceleration factors and improving image quality in conventional MRI field strengths; however, there has been limited application to ultra-high-field MRI. The objective of this study was to evaluate the performance of a prototype DL-based image reconstruction technique in 7T MRI of the brain utilizing magnetization-prepared 2 rapid acquisition gradient echoes (MP2RAGE) and sampling perfection with application-optimized contrasts using different flip angle evolutions (SPACE) acquisitions, in comparison with reconstructions in conventional compressed sensing and controlled aliasing in parallel imaging techniques. This retrospective study involved 60 patients who underwent 7T brain MRI between June 2024 and October 2024, comprising 30 patients with MP2RAGE data and 30 patients with SPACE FLAIR data. Each set of raw data was reconstructed with DL-based reconstruction and conventional reconstruction. Image quality was independently assessed by 2 neuroradiologists by using a 5-point Likert scale, which included overall image quality, artifacts, sharpness, structural conspicuity, and noise level. Interobserver agreement was determined by using top-box analysis. Contrast-to-noise ratio (CNR) and noise levels were quantitatively evaluated and compared by using the Wilcoxon signed-rank test. DL-based reconstruction resulted in a significant increase in overall image quality and a reduction in subjective noise level for both MP2RAGE and SPACE FLAIR data (all P < .001), with no significant differences in image artifacts (all P > .05). When compared with standard reconstruction, the implementation of DL-based reconstruction yielded an increase in CNR of 49.5% (95% CI, 33.0%-59.0%) for MP2RAGE data and 90.6% (95% CI, 73.2%-117.7%) for SPACE FLAIR data, along with a decrease in noise of 33.5% (95% CI, 23.0%-38.0%) for MP2RAGE data and 47.5% (95% CI, 41.9%-52.6%) for SPACE FLAIR data. DL-based reconstruction of 7T MRI significantly enhanced image quality compared with conventional reconstruction without introducing image artifacts. The achievable high acceleration factors have the potential to substantially improve image quality and resolution in 7T MRI.

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).

Introduction: One of the major problems in the development of nuclear medicine images is the presence of noise. The noise level in nuclear medicine images is usually reduced by the analysis of imaging data in a Fourier transform environment. The main drawback of this environment belongs to low signal to noise ratio in high frequencies because removing noise frequencies may remove data and times information as well. This problem becomes more serious when the most important signals are non-stationary. The aim of this research is to evaluate the effect of wavelet transform on nuclear medicine image. Materials and Methods: A brain phantom-like Hoffman phantom with 4 layers was used. Planar images were acquired from the phantom, with a total count of 107 (as reference image) and from each layer separately. The counts of planar images in each layer were increased from 100 to 700 kilo counts in seven steps. Different levels of white (Gaussian) noise was added to the reference images using MATLAB software. Simulation images using Simset software and NCAT phantom were also produced. Wavelet transform, Butterworth, Metz, Hanning(3*3) and Wiener filters were applied on all images. The Universal Image Quality Index (UIQI) was used to evaluate the image quality. Result: Wavelet transform can increase the value UIQI (UIQI = 0.7352). The effect of wavelet transform on the improvement of image quality is much better than Butterworth (UIQI = 0.3556) and Metz (UIQI = 0.3493) but almost the same as the effect of Wiener (UIQI = 0.7626) and Hanning (3*3) (UIQI = 0.8017). Discussion and Conclusion: Although wavelet transform is not the best method of reducing noise level, but its use can reduce the noise level like filters do. Assessment of commonly used thresholds as well as new design of a special threshold for wavelet transform in nuclear medicine images can improve the performance of this transform.

Despite its success in many biomedical applications, Positron Emission Tomography (PET) has the drawback of typically having lower spatial resolution and higher noise respect to other medical imaging techniques. The best achievable spatial resolution in PET scanners is limited by factors such as the positron range, non-collinearity and the size of the detector crystals. In this work, we present a novel method that uses series of image reconstructions (super-iterations) to go beyond the expected resolution-noise limits for a given PET acquisition. The image quality improvement is achieved using the projections of the previous image reconstruction to redistribute the measured counts of each line-of-response (LOR) into several subLORs, from which a new activity distribution with better quality is reconstructed. The method was evaluated with data from the preclinical scanner 4R-SuperArgus PET/CT, using the NEMA NU4-2008 image quality phantom, a cold Derenzo phantom, and an in-vivo FDG cardiac study on a rat. Resolution and recovery coefficient (RC) improvement of ~10% was achieved while keeping the same noise level. Qualitative results from the in-vivo study also confirm this improvement in image quality. The proposed method is able to achieve significantly better images at the expense of a modest increase of the computational time, and it could be also applied to other modalities, such as SPECT and Compton Cameras.

<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.

Electrical resistance tomography (ERT) has been developed for monitoring the conductivity distributions in industrial applications with the advantages of low cost, no radiation and non-invasive. The traditional ERT system uses a scheme with current injection and voltage measurement to detect the changes in conductivity distribution. But the quality of measurement may become very poor under the background of low conductivity, e.g. the distilled water. In addition, it is difficult to achieve a satisfied SNR while maintaining a high dynamic measurement range. This paper presents a voltage-driven ERT system, which is based on LCR meter and custom-built switch array. Moreover, the system can accomplish the multi-channel resistance measurements by incorporating LCR meter with a custom-built programmable analog switch array. The system SNR has been calculated, and the tomogram for image verification under the background of different conductivity water has been conducted. Preliminary results show that the sysstem has the advantages of measurement under the background of low conductivity. Compared with other ERT systems, this system can apply to a large range of conductivity from $5\times 10^{4}$ S/m to $5\times 10^{2}$ S/m.