Improved Coherent Plane-Wave Compounding Using Sign Coherence Factor Weighting for Frequency-Domain Beamforming.

A sparse and uniform multiple-input–multiple-output array is generally utilized in through-wall radar to implement real-time imaging of moving targets. However, the array sparsity with the interelement spacing much bigger than half a wavelength gives rise to grating lobe interference smearing the images. In order to enhance the signal-to-interference ratio, this letter introduces the sign coherence factor (SCF) to weigh through-wall images to suppress the grating lobes. The SCF is first proposed in medical ultrasound imaging and directly reflects the coherence of sign bits of all the transmit–receive channels in each pixel. Since the SCF calculated only by the sign bits, it has the smallest amount of computations compared with two other weighing factors, namely, the coherence factor (CF) and the phase coherence factor (PCF), which have been applied to through-wall imaging already. Moreover, the SCF has comparable performance in suppressing grating lobes with the PCF, much better than the CF. These two advantages make the SCF the most suitable for real-time imaging of moving targets. The experimental results with a two-transmitting eight-receiving stepped-frequency continuous-wave through-wall radar verifies the excellent performance of the SCF.

Ultrafast ultrasound imaging allows total focusing on every pixel, but it uses a non-focusing transmission of the ultrasonic wave into the tissue. Based on plane wave or diverging wave imaging, additional clutter reduces the imaging quality especially in echo-free regions like cysts. Clutter reducing techniques like adaptive beamforming processing compute weighting factors based on measured channel data used for each sample reconstruction. This increases signal and image quality again but adds computational complexity and may result in techniques that are not real-time capable to be implemented in an ultrasound scanner. A fast and accurate phase estimator for real-time applications is needed. Extending the concept of sign coherence factor (SCF) computations using bi-phase estimations, we propose a fast quad phase coherence (QPC) estimator for more accurate phase detection to reduce incoherent clutter. Instead of only using the sign coherence of each of the single channel data, the QPC estimator also includes the monotony of the signal to differentiate into the four classes “positive sign & increasing monotony”, “positive sign & decreasing monotony ”, “negative sign & decreasing monotony” and “negative sign & increasing monotony”. The dominant class of these four is selected and the adaptive weighting is computed as ratio of the absolute frequency of this class to the sum of all other class occurrences. The calculation of the monotony introduces only a small computational overhead compared to other adaptive beamforming techniques. The performance of QPC usage is evaluated using our ultrafast ultrasound research systems at phantom measurements (using “DiPhAS” system with 128 parallel channels) and in-vivo bladder measurements (using “MoUsE” system with 32 parallel channels, 3 MHz phased array transducer). The beamforming reconstruction is computed on the performance-limited integrated PC hardware as OpenCL kernel in real time. Imaging performance is increased compared to sign coherence beamforming (SNR by >3 dB, contrast by 4,4 dB, spatial resolution PSF FWHM by 19 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> ) while computation time still allows real-time usage.

Ultrasound imaging has been widely used in clinical diagnosis because of the characteristics of non-radiation, cost effective and real-time interaction. Hand-held ultrasound device has shown great potential in community health care and telemedicine. However, the image quality and frame rate are compromised due to simplification of hand-held hardware which severely limit its applications. The coherence plane-wave compounding imaging (CPWC) technology is usually used to generate high-quality ultrasound images, but compounding dozens of plane waves (PW) with different incident angles will dramatically reduce the frame rate. To get high-quality image while maintaining the frame rate for hand-held ultrasound imaging, in this paper we propose a LG_Unet model, which combines local detail information and global structure information through residual learning. Additionally, the natural image-based transfer learning with cascade training is adopted to alleviate the learning failure and overfitting issues. The experimental results demonstrated that our model can provide high-quality ultrasound images through only 3 PWs, in which the contrast and lateral resolution of the reconstructed images are better than those of the CPWC results from 31 PWs. The proposed method shows the feasibility of generating high-quality images for hand-held ultrasound device using plane wave while maintaining a promising balance between the image quality and frame rate.

Blind super-resolution for handheld ultrasound image: Two-stage degradation based unpaired deep learning.

Effects of Phase Aberration and Noise on Extended High Frame Rate Imaging

BACKGROUND: The coherent plane wave compounding (CPWC) is a promising technique to enhance the imaging quality while maintaining the high frame rate in the plane wave ultrasound imaging. Recently, the spatial-coherence-based method has been specially designed to process echo matrix required by the minimum variance (MV) method.OBJECTIVE: In this paper, a novel beamforming method that integrates the submatrix-spatial-coherence-based MV with the sign coherence factor (SCF) is proposed to further improve the imaging quality.METHOD: The submatrix smoothing technique is modified to smooth and de-correlate signals of the receiving array dimension. Then, the SCF is used to modify the input vector of the beamformer, which can reduce side lobe noises with almost no increase in the amount of calculation. Simulation, phantom, in vivo, and sound velocity error experiments have been performed to verify the superiority of the proposed beamformer.RESULTS: The imaging results show that the proposed approach performs better in the imaging resolution and contrast compared to the traditional CPWC method.CONCLUSION: The robustness of the proposed method is enhanced, and the over-suppression phenomenon can be alleviated, which is a phenomenon that occurs in the original spatial-coherence and SCF methods.

BackgroundThe aim of this study was to evaluate and compare PET image reconstruction algorithms on novel digital silicon photomultiplier PET/CT in patients with newly diagnosed and histopathologically confirmed lung cancer. A total of 45 patients undergoing 18F-FDG PET/CT for initial lung cancer staging were included. PET images were reconstructed using ordered subset expectation maximization (OSEM) with time-of-flight and point spread function modelling as well as Bayesian penalized likelihood reconstruction algorithm (BSREM) with different β-values yielding a total of 7 datasets per patient. Subjective and objective image assessment with all image datasets was carried out, including subgroup analyses for patients with high dose (> 2.0 MBq/kg) and low dose (≤ 2.0 MBq/kg) of 18F-FDG injection regimen.ResultsSubjective image quality ratings were significantly different among all different reconstruction algorithms as well as among BSREM using different β-values only (both p < 0.001). BSREM with a β-value of 600 was assigned the highest score for general image quality, image sharpness, and lesion conspicuity. BSREM reconstructions resulted in higher SUVmax of lung tumors compared to OSEM of up to + 28.0% (p < 0.001). BSREM reconstruction resulted in higher signal-/ and contrast-to-background ratios of lung tumor and higher signal-/ and contrast-to-noise ratio compared to OSEM up to a β-value of 800. Lower β-values (BSREM450) resulted in the best image quality for high dose 18F-FDG injections, whereas higher β-values (BSREM600) lead to the best image quality in low dose 18F-FDG PET/CT (p < 0.05).ConclusionsBSREM reconstruction algorithm used in digital detector PET leads to significant increases of lung tumor SUVmax, signal-to-background ratio, and signal-to-noise ratio, which translates into a higher image quality, tumor conspicuity, and image sharpness.

Photogrammetry depends critically on the quality of the images used to reconstruct accurate and detailed 3D models. Selection of high-quality images not only improves the accuracy and resolution of the resulting 3D models, but also contributes to the efficiency of the photogrammetric process by reducing data redundancy and computational demands. This study presents a novel approach to image quality evaluation tailored for photogrammetric applications that uses the key point descriptors typically encountered in image matching. Using a LightGBM ranker model, this research evaluates the effectiveness of key point descriptors such as SIFT, SURF, BRISK, ORB, KAZE, FREAK, and SuperPoint in predicting image quality. These descriptors are evaluated for their ability to indicate image quality based on the image patterns they capture. Experiments conducted on various publicly available image datasets show that descriptor-based methods outperform traditional no-reference image quality metrics such as BRISQUE, NIQE, PIQE, and BIQAA and a simple sharpness-based image quality evaluation method. The experimental results highlight the potential of using key-point-descriptor-based image quality evaluation methods to improve the photogrammetric workflow by selecting high-quality images for 3D modeling.

This study aims to evaluate optimized breast implant surface-structure analysis by comparing high-end ultrasound technology with a new high frequency technique. This comparative study used new breast implants with different surfaces in an in vitro setting. Nine idle silicon or polyurethane (PU) breast implants were examined by two investigators in an experimental in vitro study using two high-end ultrasound devices with multi-frequency transducers (6-15 MHz, 9-16 MHz, 12.5-33 MHz).The ultrasound B-Mode was optimized using tissue harmonic imaging (THI), speckle reduction imaging (SRI, level 0-5), cross beam (high, medium, low) and photopic.Using a standardized ultrasound protocol, the implants were examined in the middle (point of highest projection) and lateral, by two independent examiners.Image evaluation was performed on anonymized digital images in the PACS. The aim was to achieve an artifact-free recording of the surface structure, the surface coating, the total image structures and, as far as possible, an artifact-free internal representation of the implants.For independent surface evaluation a score was used (0 = undetectability of surface structures, rich in artifacts, 5 = best possible, artifact free image quality). The quality of ultrasound imaging of breast implant surfaces after the optimization of B-Scan differed significantly comparing high-end ultrasound technology with modern high-frequency ultrasound technology (p < 0,05).The following setting has been found to be the best setting with the highest image quality:B-Mode, SRI value 3, Crossbeam high level with color coded imaging for B- mode. In the total examined frequency range of 6-33 MHz, the highest image quality was found in the average frequency range of 12.5-33 MHz at both measured points. For both devices, device 1 (high-end) and device 2 (high frequency) ultrasound, the image quality was in the12.5-33 MHz frequency range with an average image quality of 3.236. It was significantly higher, than in the lower frequency ranges and the same frequency range with THI. (p < 0,05). The image quality of the high-end sonography device was superior to the conventional high-frequency ultrasound device in all frequency ranges. High-end ultrasound imaging technology was superior in the quality of implant surface evaluation in comparison to high-frequency ultrasound sonography. The gained knowledge can serve as a basis for further multicenter clinical application and studies with the aim to develop an objective, precise tool to evaluate the implant and the surrounding tissue with ultrasound.

Automated three-dimensional breast ultrasound (ABUS) is a valuable adjunct to x-ray mammography for breast cancer screening of women with dense breasts. High image quality is essential for proper diagnostics and computer-aided detection. We propose an automated image quality assessment system for ABUS images that detects artifacts at the time of acquisition. Therefore, we study three aspects that can corrupt ABUS images: the nipple position relative to the rest of the breast, the shadow caused by the nipple, and the shape of the breast contour on the image. Image processing and machine learning algorithms are combined to detect these artifacts based on 368 clinical ABUS images that have been rated manually by two experienced clinicians. At a specificity of 0.99, 55% of the images that were rated as low quality are detected by the proposed algorithms. The areas under the ROC curves of the single classifiers are 0.99 for the nipple position, 0.84 for the nipple shadow, and 0.89 for the breast contour shape. The proposed algorithms work fast and reliably, which makes them adequate for online evaluation of image quality during acquisition. The presented concept may be extended to further image modalities and quality aspects.

While the recent advancements of computed tomography (CT) technology have contributed in reducing radiation dose and image noise, an objective evaluation of image quality in patient scans has not yet been established. In this study, we present a patient-specific CT image quality evaluation method that includes fully automated measurements of noise level, structure sharpness, and alteration of structure. This study used the CT images of 120 patients from four different CT scanners reconstructed with three types of algorithm: filtered back projection (FBP), vendor-specific iterative reconstruction (IR), and a vendor-agnostic deep learning model (DLM, ClariCT.AI, ClariPi Inc.). The structure coherence feature (SCF) was used to divide an image into the homogeneous (RH) and structure edge (RS) regions, which in turn were used to localize the regions of interests (ROIs) for subsequent analysis of image quality indices. The noise level was calculated by averaging the standard deviations from five randomly selected ROIs on RH, and the mean SCFs on RS was used to estimate the structure sharpness. The structure alteration was defined by the standard deviation ratio between RS and RH on the subtraction image between FBP and IR or DLM, in which lower structure alterations indicate successful noise reduction without degradation of structure details. The estimated structure sharpness showed a high correlation of 0.793 with manually measured edge slopes. Compared to FBP, IR and DLM showed 34.38% and 51.30% noise reduction, 2.87% and 0.59% lower structure sharpness, and 2.20% and -12.03% structure alteration, respectively, on an average. DLM showed statistically superior performance to IR in all three image quality metrics. This study is expected to contribute to enhance the CT protocol optimization process by allowing a high throughput and quantitative image quality evaluation during the introduction or adjustment of lower-dose CT protocol into routine practice.

A new method for grating and side lobes suppression in ultrasound images is presented. It is based on an analysis of the phase diversity at the aperture data. Two coherence factors, namely the phase coherence factor (PCF) and the sign coherence factor (SCF), are proposed to weight the coherent sum output. Different from other approaches, phase rather than amplitude information is used to perform the correction action. Besides achieving the main goal, the method obtains improvements in lateral resolution and SNR. Implementation of the SCF technique is quite straightforward, operating in realtime, and can be added to any virtually existing beamformer to improve the resolution, contrast, SNR, and dynamic range of the images. A programmable parameter allows adjusting the sensitivity of the method to out-of-phase signals, from zero to a strict coherence criterion. The theoretical basis for the 2 methods are given and their performances evaluated by simulation. Then, experiments are conducted to provide results that are in good agreement with those expected from theory and simulation.

BackgroundTo study the impact of image quality on quantitative measurements and the frequency of segmentation error with optical coherence tomography angiography (OCTA).MethodsSeventeen eyes of 10 healthy individuals were included in this study. OCTA was performed using a swept-source device (Triton, Topcon). Each subject underwent three scanning sessions 1–2 min apart; the first two scans were obtained under standard conditions and for the third session, the image quality index was reduced using application of a topical ointment. En face OCTA images of the retinal vasculature were generated using the default segmentation for the superficial and deep retinal layer (SRL, DRL). Intraclass correlation coefficient (ICC) was used as a measure for repeatability. The frequency of segmentation error, motion artifact, banding artifact and projection artifact was also compared among the three sessions.ResultsThe frequency of segmentation error, and motion artifact was statistically similar between high and low image quality sessions (P = 0.707, and P = 1 respectively). However, the frequency of projection and banding artifact was higher with a lower image quality. The vessel density in the SRL was highly repeatable in the high image quality sessions (ICC = 0.8), however, the repeatability was low, comparing the high and low image quality measurements (ICC = 0.3). In the DRL, the repeatability of the vessel density measurements was fair in the high quality sessions (ICC = 0.6 and ICC = 0.5, with and without automatic artifact removal, respectively) and poor comparing high and low image quality sessions (ICC = 0.3 and ICC = 0.06, with and without automatic artifact removal, respectively).ConclusionsThe frequency of artifacts is higher and the repeatability of the measurements is lower with lower image quality. The impact of image quality index should be always considered in OCTA based quantitative measurements.

To evaluate the image quality improvement and noise reduction in routine dose, non-enhanced chest CT imaging by using a new generation adaptive statistical iterative reconstruction (ASIR-V) in comparison with ASIR algorithm. 30 patients who underwent routine dose, non-enhanced chest CT using GE Discovery CT750HU (GE Healthcare, Waukesha, WI) were included. The scan parameters included tube voltage of 120 kVp, automatic tube current modulation to obtain a noise index of 14HU, rotation speed of 0.6 s, pitch of 1.375:1 and slice thickness of 5 mm. After scanning, all scans were reconstructed with the recommended level of 40%ASIR for comparison purpose and different percentages of ASIR-V from 10% to 100% in a 10% increment. The CT attenuation values and SD of the subcutaneous fat, back muscle and descending aorta were measured at the level of tracheal carina of all reconstructed images. The signal-to-noise ratio (SNR) was calculated with SD representing image noise. The subjective image quality was independently evaluated by two experienced radiologists. For all ASIR-V images, the objective image noise (SD) of fat, muscle and aorta decreased and SNR increased along with increasing ASIR-V percentage. The SD of 30% ASIR-V to 100% ASIR-V was significantly lower than that of 40% ASIR (p < 0.05). In terms of subjective image evaluation, all ASIR-V reconstructions had good diagnostic acceptability. However, the 50% ASIR-V to 70% ASIR-V series showed significantly superior visibility of small structures when compared with the 40% ASIR and ASIR-V of other percentages (p < 0.05), and 60% ASIR-V was the best series of all ASIR-V images, with a highest subjective image quality. The image sharpness was significantly decreased in images reconstructed by 80% ASIR-V and higher. In routine dose, non-enhanced chest CT, ASIR-V shows greater potential in reducing image noise and artefacts and maintaining image sharpness when compared to the recommended level of 40%ASIR algorithm. Combining both the objective and subjective evaluation of images, non-enhanced chest CT images reconstructed with 60% ASIR-V have the highest image quality. Advances in knowledge: This is the first clinical study to evaluate the clinical value of ASIR-V in the same patients using the same CT scanner in the non-enhanced chest CT scans. It suggests that ASIR-V provides the better image quality and higher diagnostic confidence in comparison with ASIR algorithm.

Image quality improvement in single plane-wave imaging using deep learning