Improving Image Quality by Deconvolution Recovery Filter in Ultrasound Imaging.

Chirp coded excitation is an effective method to improve the signal-to-noise ratio (SNR) and penetration depth of high-frequency endoscopic ultrasound (EUS) imaging. In coded excitation, pulse compression is applied to compress the elongated coded signals into a short pulse, which determines the final imaging performance, including spatial resolution and SNR. However, with the current pulse compression methods, it is hard to get high performance in the peak sidelobe level (PSL), image contrast, and axial resolution at the same time. To solve this problem, in this article, a new method named compressed pulse weighting method (CPWM) was proposed based on the combination of two kinds of pulse compression signals. A brief theoretical derivation proved the feasibility of method. The proposed method was evaluated by the simulation and phantom experiments. Compared with traditional method, the results showed that the proposed adaptive weighting method can provide increases of 32.42% in the penetration depth, 9.48 dB in the SNR, 5.60 dB in the contrast ratio (CR), 5.46 in the contrast-to-noise ratio (CNR), and 0.13 mm in the axial imaging resolution for 12-MHz EUS. Therefore, this method can effectively improve the ultrasound penetration depth and imaging quality, which made it have good potential for high-frequency ultrasound imaging.

Toward Real-Time GPU Implementation of Diverging Beam With Synthetic Aperture Technique With Non-linear Beamforming for a Curvilinear Array.

Beamforming (BF) in ultrasound (US) imaging has significant impact on the quality of the final image, controlling its resolution and contrast. Despite its low spatial resolution and contrast, delay-and-sum (DAS) is still extensively used nowadays in clinical applications, due to its real-time capabilities. The most common alternatives are minimum variance (MV) method and its variants, which overcome the drawbacks of DAS, at the cost of higher computational complexity that limits its utilization in real-time applications. In this paper, we propose to perform BF in US imaging through a regularized inverse problem based on a linear model relating the reflected echoes to the signal to be recovered. Our approach presents two major advantages: 1) its flexibility in the choice of statistical assumptions on the signal to be beamformed (Laplacian and Gaussian statistics are tested herein) and 2) its robustness to a reduced number of pulse emissions. The proposed framework is flexible and allows for choosing the right tradeoff between noise suppression and sharpness of the resulted image. We illustrate the performance of our approach on both simulated and experimental data, with in vivo examples of carotid and thyroid. Compared with DAS, MV, and two other recently published BF techniques, our method offers better spatial resolution, respectively contrast, when using Laplacian and Gaussian priors.

Delay-and-sum (DAS) beamformers, when applied to photoacoustic (PA) image reconstruction, produce strong sidelobes due to the absence of transmit focusing. Consequently, DAS PA images are often severely degraded by strong off-axis clutter. For preclinical in vivo cardiac PA imaging, the presence of these noise artifacts hampers the detectability and interpretation of PA signals from the myocardial wall, crucial for studying blood-dominated cardiac pathological information and to complement functional information derived from ultrasound imaging. In this article, we present PA subaperture processing (PSAP), an adaptive beamforming method, to mitigate these image degrading effects. In PSAP, a pair of DAS reconstructed images is formed by splitting the received channel data into two complementary nonoverlapping subapertures. Then, a weighting matrix is derived by analyzing the correlation between subaperture beamformed images and multiplied with the full-aperture DAS PA image to reduce sidelobes and incoherent clutter. We validated PSAP using numerical simulation studies using point target, diffuse inclusion and microvasculature imaging, and in vivo feasibility studies on five healthy murine models. Qualitative and quantitative analysis demonstrate improvements in PAI image quality with PSAP compared to DAS and coherence factor weighted DAS (DAS CF ). PSAP demonstrated improved target detectability with a higher generalized contrast-to-noise (gCNR) ratio in vasculature simulations where PSAP produces 19.61% and 19.53% higher gCNRs than DAS and DAS CF , respectively. Furthermore, PSAP provided higher image contrast quantified using contrast ratio (CR) (e.g., PSAP produces 89.26% and 11.90% higher CR than DAS and DAS CF in vasculature simulations) and improved clutter suppression.

Image reconstruction based on inverse problems has the potential for providing good image quality in ultrasound imaging. In this paper, two Inverse Problems-Based (IPB) methods, i.e., Regularized Least Squares (RLS) and the iterative Conjugated Gradient (CG), are evaluated in comparison to the traditional Delay-And-Sum (DAS) Beamrfoming method. It is shown an improvement from 0:36mm to 0:2mm for the axial resolution when comparing the traditional DAS method with the IPB methods, showing the high potential of the method for improving image quality. Similarly, an improvement from 0:5mm to 0:2mm in lateral resolution was found. Further evaluation need to be carried out using data from actual ultrasound systems when noise and speckle is expected.

Recently, adaptive beamforming methods have been successfully applied to medical ultrasound imaging, resulting in significant improvement in image quality compared to non-adaptive delay-and-sum (DAS) beamformer. Most of the adaptive beamformers presented in the ultrasound imaging literature are based on the minimum variance (MV) beamformer which can improve the imaging resolution while retaining the contrast. The main objective of this research is to present a beamformer which could improve the imaging resolution and contrast, at the same time. In this paper, we have applied the eigenspace-based MV (EIBMV) beamformer to medical ultrasound imaging and shown the simultaneous improvement in imaging resolution and contrast. EIBMV beamformer utilizes the eigenstructure of the covariance matrix to enhance the performance of the MV beamformer. The weight vector of the EIBMV is found by projecting the obtained MV weight vector onto a vector subspace constructed from the eigenstructure of the covariance matrix. EIBMV beamformer shows a significant reduction in the sidelobe levels while representing the same mainlobe width as the MV. Specifically, in the point targets phantom, the proposed method decreases the sidelobes about 22 dB and 29 dB compared to MV and DAS, respectively. Furthermore, the proposed method increases the contrast of simulated cyst phantom about 8.6 dB and 14.1 dB and also improves the contrast-to-noise ratio about 31% and 61%, compared to MV and DAS, respectively.

MRCP is essential for noninvasive evaluation of the biliary and pancreatic ductal systems in children but can suffer from suboptimal image quality due to inconsistent protocols and technical factors. We performed a quality improvement project focused on enhancing the image quality of 3D MRCP at a tertiary children’s hospital. The project identified key contributors to poor image quality, including the inconsistent use of respiratory-triggered techniques and the absence of standardized protocols across multiple MRI units. Interventions were implemented, including updating protocols, technologist education, and improving communication between radiologists and technologists. We set a goal of achieving 90% of MRCP exams with acceptable image quality by September 2024. Our efforts increased the success rate from 68 to 77%. Although the target was not fully reached by the end of the set project timeline, the effort highlights the importance of multidisciplinary collaboration, continuous education, and ongoing auditing in driving improvements in imaging quality.

Ultrasound imaging is a widely used and safe medical diagnostic technique, due to its noninvasive nature, low cost, capability of forming real time imaging, and the continuing improvements in image quality. However; the usefulness of ultrasound imaging is degraded by the presence of signal dependant noise known as speckle. It is well-known that speckle is a multiplicative noise that degrades the visual evaluation in ultrasound imaging. In ultrasound (US) imaging, denoising is intended to improve quantitative image analysis techniques. In this paper, a new version of the Non Local (NL-) means filter adapted for US images is proposed based on Similarity function depend on specific characteristics of the variance speckle noise in ultrasound images. The proposed method has been compared with Median, Wavelet, Mean and variance local statistics, Geometric, Anisotropic diffusion filtering, and Non ' local means filter using quantitative parameters. From the visual results and image quality evaluation metrics obtained over real images we can conclude that the modified(NL-) means filter can be successfully used for ultrasound image denoising, and performs better results than all other methods while still retaining the structural details and retains the edges and textures very well while removing speckle noise.

Ultrasound (US) imaging application in medicine and other fields is enormous. It has several advantages over other medical imaging modalities. The use of US in diagnosis is well established because of its noninvasive nature, portable, accurate, low-cost imaging modality, capable of forming real-time imaging and continuing improvement in image quality. Medical images and satellite images are usually degraded by noise during image acquisition and transmission process. It is estimated that 1 out of every form medical diagnostic image studies in the world involves ultrasonic techniques. The objectives of this article are to give an overview of the types of speckle reduction techniques in US imaging, present a new technique of speckle reduction, and carry out a comparative evaluation of despeckle filtering based on image quality metrics. A new speckle suppression method and coherence enhancement of medical US image are proposed: 9 despeckled filtering techniques and 5 edge detection operators, with the result evaluated by image quality metrics. The best edge detection hybrid with best filter, quality evaluation metrics has been found that the proposed method performance better than all other methods, while the structural details and result preserving of small and important image feature that contain diagnostic information in a better way than other despeckling filter.

Phase aberration correction for ultrasound imaging guided extracorporeal shock wave therapy (ESWT): Feasibility study

This dissertation describes ultrasound algorithms developed for synthetic transmit aperture (STA) imaging during the transmission and the image reconstruction stages. Images generated using these algorithms demonstrate image quality enhancement both theoretically and experimentally. The advanced algorithms also improve the application of STA imaging. Due to the single element transmission pattern, the low signal-to-noise ratio is a major limitation for STA imaging. A delay-encoded transmission scheme (DE-STA) was designed in this dissertation to encode all the transmissions. The decoded RF signals were equivalent to the standard STA signals, but with a higher SNR. Improved image qualities were observed under DE-STA transmission in terms of lateral resolution (+28%), peak-signal-to-noise ratio (PSNR, +7 dB) and target contrast-to-noise ratio (CNR, +360%) compared to those acquired with the standard STA mode. The stability of DE-STA was analyzed and verified under various noise levels by the special distribution of the singular values of the encoding matrix through singular value decomposition (SVD) (i.e. all the singular values were the same except for the first one and the last one). A more efficient decoding process was also derived based on pseudo-inversion (PI) and the computation complexity was reduced by 2/3. Speckle and undesired sidelobe signals can reduce the lesion CNR and detectability in ultrasound images. Typically, the CNR can be increased by spatial compounding (SC) or frequency compounding (FC) during reconstruction. We proposed methods to implement a 2-dimentional (2-D) aperture domain filter in the SC/FC processes, referred to as filtered spatial compounding (FSC) and filtered frequency compounding (FFC), for synthetic transmit aperture (STA) imaging. Both techniques reduced the sidelobe interference and provided improved lesion CNR. Consequently, the lesion signal-to-noise ratio (lSNR) in FSC and FFC increased (up to +130%), compared to that in the standard delay-and-sum (DAS) method. This dissertation investigates all these proposed advanced ultrasound algorithms, with the end goal of implementing these methods in STA imaging to extend its application in clinic.

Orthogonal sparse dictionary based on Chirp echo for ultrasound imaging

In plane wave medical ultrasound imaging, the analog echo signals are received via the transducer, and sampled as digital radio frequency data which is further processed by the beamforming algorithm to obtain the output ultrasound image. The sampling frequency of radio frequency data has a great impact on the image quality. The radio frequency data with higher sampling frequency is closer to the true distribution of the analog echo signals, leading to better image quality. However, high-frequency sampling chips are usually expensive. Thus, improving the plane wave imaging quality for radio frequency data with low sampling frequency has important practical significance. The ultrasound imaging quality is mainly affected by shallow features such as texture and color. However, low-sampling-frequency data has poor shallow features while high-sampling-frequency data has better shallow features. In this work, we propose an adversarial learning method to replace the shallow features of low-frequency data with the shallow features of high-frequency data, but retaining the deep features of low-frequency data. The experiment results demonstrate that the proposed method improves the plane wave imaging quality for low-sampling-frequency data by fusing shallow and deep features.

In order to improve the efficiency of the eigenspace-based minimum variance (ESBMV) algorithm, a low-complexity minimum variance combined with eigenvalue decomposition (IBMV) algorithm is proposed. Firstly, the echo signals are transformed into beam domain by the discrete cosine transformation. Then, the eigenvalue decomposition of the sample covariance matrix is used to extract the signal subspace. The largest eigenvalue and its corresponding eigenvector can be extracted and other eigenvalues are taken the same value when the trace of sample covariance matrix remains unchanged. By these ways, the inverse operation of matrix can be simplified to the multiplication of vectors. In order to verify the validity of the proposed algorithm, Field II is introduced to test point targets and the cyst phantom. Experimental results indicate that the proposed IBMV with robustness to noises is obviously more efficient than ESBMV. Besides, the imaging quality of IBMV is better than delay and sum (DAS) method, minimum variance (MV) algorithm and beam domain minimum variance (BMV) method in terms of resolution and contrast ratio.

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