Image Post-processing Techniques Research Articles

Factors influencing diffusion tensor imaging of knee cartilage in children ages 6-12years: a prospective study.

Magnetic resonance diffusion tensor imaging (DTI) has recently been used to evaluate the developing cartilage of children, but the influencing factors have not been well studied. The objective of this study was to investigate the influence of the diffusion gradient strength (b value), diffusion gradient direction, age and sex on knee cartilage DTI in healthy children aged 6-12years. A total of 30 healthy child volunteers, with an average age of 8.9 ± 1.6 (mean ± standard deviation) years, were enrolled in this study. They were categorized into three groups according to their age range: 6-8years, 8-10years and 10-12years, ensuring equal sex distribution in each group (5 boys and 5 girls). These volunteers underwent routine left knee joint magnetic resonance imaging (MRI) and serial DTI scans. DTI parameters were altered as follows: when b value = 600s/mm2, diffusion gradient direction was set to 6, 15, 25, 35 and 45; and when diffusion gradient direction = 25, b value was set to 300, 600, 900 and 1200s/mm2. The values of fractional anisotropy (FA) and apparent diffusion coefficient (ADC) were separately acquired using image post-processing techniques. The correlation between various b values, diffusion gradient directions, age and sex on the one hand and FA and ADC values on the other, was investigated. (1) When diffusion gradient direction was fixed and the b value was varied, both FA and ADC exhibited a decreasing trend as the b value increased (P < 0.001). (2) When the b value was fixed and diffusion gradient direction was varied, the FA of knee cartilage showed a decreasing trend with increasing diffusion gradient direction (P < 0.001). (3) The FA value increased with age (P < 0.05). The b value, diffusion gradient direction value and age exert a significant impact on both FA and ADC values in MR DTI of knee cartilage in children aged 6-12years. In order to obtain a stable DTI, it is recommended to select a b value ≥ 600s/mm2 and a diffusion gradient direction ≥ 25 during scanning.

Advanced deep learning-based image reconstruction in lumbar spine MRI at 0.55 T – Effects on image quality and acquisition time in comparison to conventional deep learning-based reconstruction

ObjectivesTo evaluate an optimized deep leaning-based image post-processing technique in lumbar spine MRI at 0.55 T in terms of image quality and image acquisition time. Materials and methodsLumbar spine imaging was conducted on 18 patients using a 0.55 T MRI scanner, employing conventional (CDLR) and advanced (ADLR) deep learning-based post-processing techniques. Two musculoskeletal radiologists visually evaluated the images using a 5-point Likert scale to assess image quality and resolution. Quantitative assessment in terms of signal intensities (SI) and contrast ratios was performed by region of interest measurements in different body-tissues (vertebral bone, intervertebral disc, spinal cord, cerebrospinal fluid and autochthonous back muscles) to investigate differences between CDLR and ADLR sequences. ResultsThe images processed with the advanced technique (ADLR) were rated superior to the conventional technique (CDLR) in terms of signal/contrast, resolution, and assessability of the spinal canal and neural foramen. The interrater agreement was moderate for signal/contrast (ICC = 0.68) and good for resolution (ICC = 0.77), but moderate for spinal canal and neuroforaminal assessability (ICC = 0.55). Quantitative assessment showed a higher contrast ratio for fluid-sensitive sequences in the ADLR images. The use of ADLR reduced image acquisition time by 44.4%, from 14:22 min to 07:59 min. ConclusionsAdvanced deep learning-based image reconstruction algorithms improve the visually perceived image quality in lumbar spine imaging at 0.55 T while simultaneously allowing to substantially decrease image acquisition times. Clinical relevanceAdvanced deep learning-based image post-processing techniques (ADLR) in lumbar spine MRI at 0.55 T significantly improves image quality while reducing image acquisition time.

Image quality evaluation in deep-learning-based CT noise reduction using virtual imaging trial methods: Contrast-dependent spatial resolution.

Deep-learning-based image reconstruction and noise reduction methods (DLIR) have been increasingly deployed in clinical CT. Accurate image quality assessment of these methods is challenging as the performance measured using physical phantoms may not represent the true performance of DLIR in patients since DLIR is trained mostly on patient images. In this work, we aim to develop a patient-data-based virtual imaging trial framework and, as a first application, use it to measure the spatial resolution properties of a DLIR method. The patient-data-based virtual imaging trial framework consists of five steps: (1) insertion of lesions into projection domain data using the acquisition geometry of the patient exam to simulate different lesion characteristics; (2) insertion of noise into projection domain data using a realistic photon statistical model of the CT system to simulate different dose levels; (3) creation of DLIR-processed images from projection or image data; (4) creation of ensembles of DLIR-processed patient images from a large number of noise and lesion realizations; and (5) evaluation of image quality using ensemble DLIR images. This framework was applied to measure the spatial resolution of a ResNet based deep convolutional neural network (DCNN) trained on patient images. Lesions in a cylindrical shape and different contrast levels (-500, -100, -50, -20, -10 HU) were inserted to the lower right lobe of the liver in a patient case. Multiple dose levels were simulated (50%, 25%, 12.5%). Each lesion and dose condition had 600 noise realizations. Multiple reconstruction and denoising methods were used on all the noise realizations, including the original filtered-backprojection (FBP), iterative reconstruction (IR), and the DCNN method with three different strength setting (DCNN-weak, DCNN-medium, and DCNN-strong). Mean lesion signal was calculated by performing ensemble averaging of all the noise realizations for each lesion and dose condition and then subtracting the lesion-present images from the lesion absent images. Modulation transfer functions (MTFs) both in-plane and along the z-axis were calculated based on the mean lesion signals. The standard deviations of MTFs at each condition were estimated with bootstrapping: randomly sampling (with replacement) all the DLIR/FBP/IR images from the ensemble data (600 samples) at each condition. The impact of varying lesion contrast, dose levels, and denoising strengths were evaluated. Statistical analysis with paired t-test was used to compare the z-axis and in-plane spatial resolution of five algorithms for five different contrasts and three dose levels. The in-plane and z-axis spatial resolution degradation of DCNN becomes more severe as the contrast or radiation dose decreased, or DCNN denoising strength increased. In comparison with FBP, a 59.5% and 4.1% reduction of in-plane and z-axis MTF (in terms of spatial frequencies at 50% MTF), respectively, was observed at low contrast (-10 HU) for DCNN with the highest denoising strength at 25% routine dose level. When the dose level reduces from 50% to 12.5% of routine dose, the in-plane and z-axis MTFs reduces from 92.1% to 76.3%, and from 98.9% to 95.5%, respectively, at contrast of -100 HU, using FBP as the reference. For most conditions of contrasts and dose levels, significant differences were found among the five algorithms, with the following relationship in both in-plane and cross-plane spatial resolution: FBP>DCNN-Weak>IR>DCNN-Medium>DCNN-Strong. The spatial resolution difference among algorithms decreases at higher contrast or dose levels. A patient-data-based virtual imaging trial framework was developed and applied to measuring the spatial resolution properties of a DCNN noise reduction method at different contrast and dose levels using real patient data. As with other non-linear image reconstruction and post-processing techniques, the evaluated DCNN method degraded the in-plane and z-axis spatial resolution at lower contrast levels, lower radiation dose, and higher denoising strength.

A microfluidic-based quantitative analysis system for the multiplexed genetic diagnosis of human viral infections using colorimetric loop-mediated isothermal amplification.

In this study, a microfluidic-based system utilizing colorimetric loop-mediated isothermal amplification (LAMP) is introduced for the quantitative analysis of nucleic acid targets. This system offers a user-friendly and cost-effective platform for the multiplexed genetic diagnosis of various infectious diseases across multiple samples. It includes time-lapse imaging equipment for capturing images of the microfluidic device during the LAMP assay and a hue-based quantitative analysis software to analyze the LAMP reaction, streamlining diagnostic procedures. An electric pipette was used to simplify the loading of samples and LAMP reagents into the device, allowing easy operation even by untrained individuals. The hue-based analysis software employs efficient image processing and post-processing techniques to calculate DNA amplification curves based on color changes in multiple reaction chambers. This software automates several tasks, such as identifying reaction chamber areas from time-lapse images, quantifying color information within each chamber, correcting baselines of DNA amplification curves, fitting experimental data to theoretical curves, and determining the threshold time for each curve. To validate the developed system, conventional off-chip LAMP assays were conducted with a 25 μL reaction mixture in 0.2 mL polymerase chain reaction (PCR) tubes using a real-time turbidimeter. The results indicated that the threshold time obtained using the colorimetric LAMP assay in the developed system is comparable to that obtained with real-time turbidity measurements in PCR tubes, demonstrating the system's capability for quantitative analysis of target nucleic acids, including those from human herpesviruses.

Neuromuscular imaging in clinical practice: an ESNR survey of 30 centers.

We assessed the current clinical imaging practice in the primary evaluation of neuromuscular disorders (NMD), with respect to standardized imaging, evaluation and reporting through a European and extra-European-wide survey. An online questionnaire was emailed to all European Society of Neuroradiology (ESNR) members (n = 1662) who had expressed their interest in NMD. The questionnaire featured 40 individual items. Information was gathered on the context of the practices, available and preferred imaging modalities, applied imaging protocols and standards for interpretation, reporting and communication. A total of 30 unique entries from European and extra-European academic and non-academic institutions were received. Of these, 70% were neuroradiologists, 23% general radiologists and 7% musculoskeletal radiologists. Of the 30 responding institutes, 40% performed from 20 to 50 neuromuscular scans per year for suspected NMD. The principal modality used for a suspected myopathy was magnetic resonance imaging (MRI) (50%) or "mainly MRI" (47%). The primary imaging modality used for the evaluation of patients suspected of a neuropathy was MRI in 63% of all institutions and "mainly MRI" in 37%. For both muscle and nerve pathology, pelvic girdle and inferior limbs are the most scanned parts of the body (28%), followed by the thigh and leg (24%), whole body MR (24%), scapular girdle (16%), and the thigh in just 8% of institutions. Multiplanar acquisitions were performed in 50% of institutions. Convectional sequences used for muscle MRI included T2-STIR (88%), 2D T1weighted (w) (68%), T1 Dixon or equivalent (52%), T2 Dixon (40%), DWI (36%), 2D T2w (28%), T1 3D and T2 3D (20% respectively). For nerve MRI conventional sequences included T2-STIR (80%), DWI (56%), T2 3D (48%), 2D T2w (48%), T1 3D (44%), T1 Dixon or equivalent (44%), 2D T1 (36%), T2 Dixon (28%). Quantitative sequences were used regularly by 40% respondents. While only 28% of institutions utilized structured reports, a notable 88% of respondents expressed a desire for a standardized consensus structured report. Most of the respondents (93%) would be interested in a common MRI neuromuscular protocol and would like to be trained (87%) by the ESNR society with specific neuromuscular sessions in European annual meetings. Based on the survey findings, we can conclude that the current approach to neuromuscular imaging varies considerably among European and extra-European countries, both in terms of image acquisition and post-processing. Some of the challenges identified include the translation of research achievements (related to advanced imaging) into practical applications in a clinical setting, implementation of quantitative imaging post-processing techniques, adoption of structured reporting methods, and communication with referring physicians.

Efficient denoising of cold atom images using the optimized eigenface recognition algorithm.

Absorption imaging is a widely employed technique for detecting cold atom clouds and Bose-Einstein condensates (BECs). There are situations where such images may suffer from unwanted interference fringes, resulting in uncertainties in determining crucial parameters such as the atom number, temperatures, or even dynamics in small timescales. Reducing the acoustic vibrations and recording image frames synchronized with the source of such vibrations can largely reduce these fringes; however, some residual fringes still need to be taken care of for precision measurements. In this study, we propose an efficient image post-processing technique for noise reduction that effectively mitigates such interference patterns. Our approach makes use of the well-known eigenface recognition algorithm, combined with an optimized masking strategy applied to the image of the atomic cloud using a small number of basis sets. The use of a limited basis set ensures minimal computational time, allowing this method to be readily incorporated into every experimental run. Through the application of our technique, we successfully reduce interference fringes and improve the accuracy of parameter estimation by 50% in the absorption imaging of cold atoms. The temperature uncertainties of cold 87 R b atoms are reduced by more than 50% after the algorithm is applied. This approach holds significant promise for enhancing the reliability and precision of experimental measurements in diverse research fields where absorption imaging is employed.

The effect of ultrasound wave path estimation to defect characterization capability in half-skip total focusing method

The total focusing method (TFM) is a post-processing imaging technique applied on full matrix capture (FMC) ultrasonic inspection (UT) dataset. In TFM the ultrasonic wave energy is synthetically focused on every pixel in the image region of interest (ROI). In terms of half-skip TFM (HSTFM), wave mode conversion happens when the wave rebounds at interface, such as specimen backwall. This paper aims to propose and evaluate a method that involves Snell’s law to address accurate estimation of distanceof-flight (DOF) of wave propagation when wave mode conversion appears in HSTFM. This HSTFM algorithm is applied to both experimental and simulated FMC dataset that inspects a surface-breaking notch for notch image reconstruction. Comparisons between images with and without considering Snell’s law in wave mode conversion show visible difference that could lead to misinterpretations in characterizing the defect. The sensitivity of TFM to varying defect features such as defect tilt angle is also studied using simulated FMC datasets.

3D-shape formation of blood vessels based on computer aided design system

This paper proposes and tests a computerized approach for constructing a 3D model of blood vessels from angiogram images. The approach is divided into two steps, image features extraction and solid model formation. In the first step, image morphological operations and post-processing techniques are used for extracting geometrical entities from the angiogram image. These entities are the middle curve and outer edges of the blood vessel, which are then passed to a computer-aided graphical system for the second phase of processing. The system has embedded programming capabilities and pre-programmed libraries for automating a sequence of events that are exploited to create a solid model of the blood vessel. The gradient of the middle curve is adopted to steer the vessel’s direction, while the cross-sections of the blood vessel are formed as a sequence of circles lying in planes that are orthogonal to the gradients of the middle curves. The radii for the circles are estimated as a distance between the intersection points of the blood vessel edges with the orthogonal plane to the middle curve gradient. The system then uses these circles and the middle curve gradients to produce a solid volume that represents the 3D shape of the blood vessel. The method was tested and evaluated using different cases of angiogram images, and showed a reasonable agreement between the generated shapes and the tested images.

Ice Accretion: Image Post-Processing Measurement Techniques for 2D Ice Shapes

Ice accretion poses substantial safety hazards for the manned and unmanned aviation industries. Its study is essential for icing events risk assessment and for the development of efficient ice protection systems. The existing ice accretion measurement techniques—casting, molding, and laser-scanning—are time-consuming, sometimes cumbersome to use, and highly expensive, while hand tracing is inexpensive, but has lower accuracy and time-consuming post-processing. This work presents two low-cost, fast, and easy-to-use measurement techniques for 2D ice accretion profiles. Both employ algorithms of automatic ice shape detection, one based on unmediated image-processing, another based on the processing of manual ice tracings. The techniques are applied to ice accretion experiments conducted in an icing wind tunnel at low Reynolds numbers, and their results are validated against ice thickness caliper measurements. A comparison of the results shows that both techniques accurately measure the leading-edge ice thickness and the 2D shape of the ice accretion profiles. One technique is faster, with higher measurement accuracy, but produces interrupted-line 2D ice profiles and requires good lighting conditions, while the other generates continuous-line 2D profiles and has no application restriction, but it is slower, with lower accuracy. A discussion is conducted, aiming to help one determine the best applications for each ice accretion measurement technique presented.

Robust quantification of CT-ventilation biomarker techniques and repeatability in a porcine model.

Biomarkers estimating local lung ventilation have been derived from computed tomography (CT) imaging using various image acquisition and post-processing techniques. CT-ventilation biomarkers have potential clinical use in functional avoidance radiation therapy (RT), in which RT treatment plans are optimized to reduce dose delivered to highly ventilated lung. Widespread clinical implementation of CT-ventilation biomarkers necessitates understanding of biomarker repeatability. Performing imaging within a highly controlled experimental design enables quantification of error associated with remainingvariables. To characterize CT-ventilation biomarker repeatability and dependence on image acquisition and post-processing methodology in anesthetized and mechanically ventilatedpigs. Five mechanically ventilated Wisconsin Miniature Swine (WMS) received multiple consecutive four-dimensional CT (4DCT) and maximum inhale and exhale breath-hold CT (BH-CT) scans on five dates to generate CT-ventilation biomarkers. Breathing maneuvers were controlled with an average tidal volume difference <200 cc. As surrogates for ventilation, multiple local expansion ratios (LERs) were calculated from the acquired CT scans using Jacobian-based post-processing techniques. measured local expansion between an image pair using either inhale and exhale BH-CT images or two 4DCT breathing phase images. measured the maximum local expansion across the 4DCT breathing phase images. Breathing maneuver consistency, intra- and interday biomarker repeatability, image acquisition and post-processing technique dependence were quantitatively analyzed. Biomarkers showed strong agreement with voxel-wise Spearman correlation for intraday repeatability and for all other comparisons, including between image acquisition techniques. Intra- and interday repeatability were significantly different (p < 0.01). LER2 and LERN post-processing did not significantly affect intraday repeatability. 4DCT and BH-CT ventilation biomarkers derived from consecutive scans show strong agreement in controlled experiments with nonhumansubjects.

Efficient Reconstruction of Low Photon Count Images from a High Speed Camera

Challenging imaging applications requiring ultra-short exposure times or imaging in photon-starved environments can acquire extremely low numbers of photons per pixel, (&lt;1 photon per pixel). Such photon-sparse images can require post-processing techniques to improve the retrieved image quality as defined quantitatively by metrics including the Structural Similarity Index Measure (SSIM) and Mean Squared Error (MSE) with respect to the ground truth. Bayesian retrodiction methods have been shown to improve estimation of the number of photons detected and spatial distributions in single-photon imaging applications. In this work, we demonstrate that at high frame rates (&gt;1 MHz) and low incident photon flux (&lt;1 photon per pixel), image post processing can provide better grayscale information and spatial fidelity of reconstructed images than simple frame averaging, with improvements in SSIM up to a factor of 3. Various other image post-processing techniques are also explored and some of which result in a similar quality of image reconstruction to Bayesian retrodiction, with lower computational load. Image reconstructions using Bayesian Retrodiction or bilateral filtering are of comparable quality to frame averaging, as measured by the Structural Similarity Index Measure, when using less than 40% of the photon flux.

In-situ DIC measurement of material deformation through spatial–temporal kinematics analysis during orthogonal cutting Ti6Al4V

A strong relationship exists between material deformation and cutting conditions in machining process. The continuous material deformation behaviors temporally varying in shear zone are required to be in-situ measured during machining. The spatio-temporal kinematics for material deformation with in-situ DIC measurement during orthogonal cutting Ti6Al4V is proposed. The image processing, digital image correlation and high-speed photography techniques are presented to analyze spatial–temporal kinematics of material plastic deformation during orthogonal cutting Ti6Al4V. The post-processing imaging technique is developed to process raw image sequences. The possessed image quality is characterized by four indicators. The evolution of material deformation along different strain paths is captured and analyzed in adiabatic shear bands. The spatio-temporal kinematic fields within the primary shear deformation zone under different cutting conditions are investigated by experiments. The research results can help to more precisely understand the dynamic mechanism of material plastic deformation during the manufacturing process.

Selective video enhancement in the Laguerre–Gauss domain

Traditional image and video enhancement techniques do not consider the subjective preferences of the user, who might be interested in modifying or highlighting specific elements of a scene. In fact, most image or video post-processing techniques are applied to the entire image or frame. In this work, a framework for selective video enhancement is presented. It is based on an adaptive multi-resolution edge enhancement technique performed in the Laguerre–Gauss complex wavelet domain. In the proposed scheme, edges are selectively enhanced or attenuated according to the inputs of the user, taking into account the characteristics of the content, the response of the human visual system, and the masking effects induced by textured background. Two scenarios have been implemented and tested: (i) an interactive video editing system, where the user selects the objects to enhance through a graphical interface and the system automatically propagates the selection through the following video frames belonging to the same shot, (ii) a perceptually-driven technique to improve the quality of RGB plus Depth videos, which uses depth maps as additional features to guide the enhancement.

Vorticity dynamics using PIV: Quantitative analysis of water perturbation by bubble rise

Bubble rise behavior and its disturbance of surrounding water are widespread in nuclear power plants and have a significant impact on the safety and efficiency of the installations. In this work, the dynamic parameters of six different sizes of bubbles and the perturbation of water are investigated, with the help of particle image velocimetry (PIV) and numerical image post-processing techniques. The morphology, trajectory, instantaneous velocity, velocity vector fields, and vorticity fields as well as their interactions are analyzed, by extracting data from the images. The results show that the shape of the rising bubbles is elliptical with small eccentricity most of the time, but not a standard round. In the initial stage of bubble generation, the eccentricity is large and the Weber number (We) of the bubble is small due to the surface tension. In addition, a standard annular flow field and two symmetric vorticities are found around the bubble in the initial stage, by calculating the velocity vector field and vorticity field of water. However, the equilibrium between the two vorticities can only be maintained for tens of milliseconds, and once the vorticity completely occupies the tail of the bubble, the bubble turns in the direction of the vorticity. This phenomenon occurs four to five times in the observed region and manifests itself macroscopically as a swing of the bubble trajectory. Further, the trajectory swing frequency and the bubble average velocity are found to decrease with the size of the bubble, which indicates that larger bubbles are more stable. However, the average velocity of the water increases with the increase of the bubble size, indicating that the water perturbation is more sensitive to the size of the bubble compared to the trajectory and morphology. Finally, a correlation is proposed for calculating the water velocity based on Reynolds number (Re), Eötvös number (Eo), and bubble velocity, which shows a good adaptation after comparing with the experimental data.

Increasing role of imaging in differentiating MS from non-MS and defining indeterminate borderline cases.

Multiple sclerosis (MS) is a heterogenous condition with differences between patients regarding disease presentation, imaging features, disease activity, prognosis and treatment responses. Following the discovery of new biomarkers, the concept of MS has evolved, with syndromes previously considered to be its variants now recognised as separate entities, including aquaporin-4 (AQP4)-antibody (Ab) neuromyelits optica spectrum disorders (NMOSD), and myelin oligodendrocyte glycoprotein (MOG)-Ab disease (MOGAD). In line with their distinct pathology, the newly emerging conditions have imaging characteristics which are dissimilar to typical MS. Progress in reclassifying such demyelinating CNS conditions has highlighted the challenge in meaningful categorisation of atypical presentations at the borders of MS, such as antibody-negative neuromyelitis optica-like syndromes, tumefactive demyelinating lesions, or Balo's concentric sclerosis. In this review, we discuss the increasing role of imaging in distinguishing MS from non-MS CNS inflammatory/demyelinating conditions and defining undetermined borderline cases. This progress relies both on better characterisation of imaging features of these conditions on conventional imaging in terms of their appearance and location, as well as on the implementation of novel image acquisition and/or post-processing techniques allowing for more in-depth lesion assessment, including the presence of a central vein sign or paramagnetic rim.

MR Enterography in Crohn's Disease: Comparison of Contrast Imaging with Diffusion-weighted Imaging and a special Form of Color Coding.

PurposeWe compared contrast-enhanced MR enterography with diffusion-weighted sequences to evaluate the validity of diffusion-weighted sequences for activity assessment in Crohnʼs disease compared to endoscopy. In addition, we investigated a new color-coded image post-processing technique in comparison with standard sequences and endoscopy.Materials and MethodsIncluded were 197 MR enterographies (2015–2017) performed by using standardized examination protocols. The intestine was divided into 7 segments, which were compared separately. The accuracy of the MR examinations with regard to disease activity was validated using the Seo and MaRIA score and endoscopy findings. In addition, the image data were post-processed using a color-coded evaluation method (DCE tool on OsiriX).ResultsThe comparison between contrast-enhanced and diffusion-weighted sequences showed a highly significant correlation for all bowel sections with a mean Spearman correlation coefficient of 0.876 (0.809–0.928). The color-coded image post-processing showed a sensitivity of 83.2 % and a specificity of 70.5 % in comparison with the MaRIA score. In comparison to endoscopy, a sensitivity of 81.3 % and a specificity of 70.5 %. In comparison with endoscopy, the MaRIA score showed a sensitivity of 80.2 % and a specificity of 84.0 % at a cut-off of 7. The visual score according to Seo showed a sensitivity of 85.7 % with a specificity of 77.0 % in the contrast-weighted examination and a sensitivity of 87.9 % and a specificity of 71.8 % for diffusion weighted images.ConclusionDiffusion-weighted sequences are as good as contrast-weighted sequences for assessing inflammatory activity in Crohnʼs disease. Contrast is often helpful for assessing complications, but this was not the purpose of this study. Visual imaging using color-coded data sets was similarly good at detecting inflammation.Key points:MR enterography is possible without contrast using diffusion-weighted imaging with comparable high informative value.Inflammatory activity in MR correlates sufficiently well with endoscopyThe MaRIA and Seo scores are comparable in their validity.The colored representation of inflamed bowel sections is a promising technique.Citation FormatJakob M, Backes M, Schaefer C et al. MR Enterography in Crohnʼs Disease: Comparison of Contrast Imaging with Diffusion-weighted Imaging and a special Form of Color Coding. Fortschr Röntgenstr 2022; 194: 1119 – 1131

P 71 Optical coherence tomography – a future imaging technique of peripheral nerves?

Object: Owing to their complex topography, their long courses and their small diameters peripheral nerves are challenging structures for radiological diagnostics. However, imaging techniques in the area of peripheral nerve diseases have undergone unexpected development in recent decades. They include magnetic-resonance imaging (MRI) and high-resolution sonography (HRS). Yet none of those imaging techniques reaches a resolution comparable to that of histological sections. Fascicles are the smallest discernable structure. Optical-coherence tomography (OCT) is the first imaging technique that is able to depict a nerve’s ultrastructure at micrometer resolution. Methods: OCT measurement was performed in 32 patients with different peripheral nerve pathologies, i.e. nerve compression syndromes. The nerves were examined during surgery after exposing. Only the sural nerve was twice examined ex vivo . The OCT systems Thorlabs Callisto and Thorlabs Ganymede were used. For intra-operative use, a hand probe was covered with a sterile foil. Different post-processing imaging techniques were applied and evaluated. Results: The intra-operative use of OCT is easy and intuitive. Image artifacts are mainly caused by motion and the sterile foil. If they are kept at a low level, the hyporeflecting bundles of nerve fascicles and their inner parts can be displayed. In the Haralick evaluation the second angular moment (SAM) is most suitable to depict the connective tissue. Conclusion: OCT is a new imaging technique which has shown to be promising in peripheral nerve surgery for particular questions. Its resolution exceeds that provided by recent radiological possibilities such as MRI and HRS. Since its field of view is relatively small, faster acquisition times would be highly desirable. Currently, the method resembles an optical biopsy and can be a supplement to intra-operative sonography, giving high-resolution insight into a suspect area which has been located by sonography in advance. This could be especially helpful in surgery of peripheral nerve lesions in continuity.

A phenomenological investigation on near-wall bubble behavior close to CHF in flow boiling

The insight information of subcooled flow boiling is very important for the development of boiling crisis mechanistic model. In this paper, visual experiment is carried out in a vertical square channel with a heating rod with diameter of 9.5 mm. Based on the near-wall bubble behavior captured by a high-speed camera, combined with advanced image post-processing technique developed in-house, the qualitative and quantitative analysis of flow boiling behavior near the heating wall are presented when approaching the CHF (Critical Heat Flux). According to the visual experimental study, a detailed discussion on the phenomena before CHF and during the CHF is presented. With high heat flux, periodic large vapor clot passes with relatively constant frequency. Then periodic wavy vapor layer and liquid film clearly appear in the near-wall region when the working condition is extremely close to the CHF. It is found that high thermal-hydraulic parameters, including system pressure, mass flux and inlet subcooling, prevent the formation of thick vapor layer, and this is a significant precursor to trigger boiling crisis. This implies that the possibility of triggering boiling crisis is reduced due to the difficulty in forming thicker vapor layer, and lead to higher CHF value. Additionally, a prediction of wavelength between two-phase layers is proposed by combining visual observations and instability theory. The predicted results show good agreement compared with experimental data. Those results can provide predictions of key interfacial feature parameters for developing CHF mechanistic model.

Noise estimation in 2D MRI using DWT coefficients and optimized neural network

Robust estimation of noise strength in Magnetic Resonance Images (MRIs) is a task of great importance due to its extensive applications in image post-processing techniques. Many noise estimation algorithms have been proposed in the past to retrieve noise characteristics of the magnitude image. These algorithms rely on either the spatial or transform domain information of the image. In this research article, we propose a noise estimation algorithm that utilizes a hybrid Discrete Wavelet Transform (DWT) and edge information suppression based algorithm to estimate the strength of noise in magnitude MR images. The wavelet coefficients corresponding to spatial domain edges are eliminated using a down-sampled complement of the Sobel edge map. The distribution of average noise energy in spatial and transform domain which follows Parseval’s theorem is utilized for calculating the initial noise estimate. Further, the robustness of the proposed algorithm is enhanced using Feedforward Neural Network. The proposed algorithm is observed to be computationally fast and accurate. Results on synthetic and clinical T1- and T2-w brain MRIs following Gaussian or Rician distribution show better performance than the existing benchmark algorithms over a wide range of input noise.

Lower extremity CT angiography in peripheral arterial disease: from the established approach to evolving technical developments.

With the advent of multidetector computed tomography (CT), CT angiography (CTA) has gained widespread popularity for noninvasive imaging of the arterial vasculature. Peripheral extremity CTA can nowadays be performed rapidly with high spatial resolution and a decreased amount of both intravenous contrast and radiation exposure. In patients with peripheral artery disease (PAD), this technique can be used to delineate the bilateral lower extremity arterial tree and to determine the amount of atherosclerotic disease while differentiating between acute and chronic changes. This article provides an overview of several imaging techniques for PAD, specifically discusses the use of peripheral extremity CTA in patients with PAD, clinical indications, established technical considerations and novel technical developments, and the effect of postprocessing imaging techniques and structured reporting.