Constrained least squares filtering algorithm for ultrasound image deconvolution

Enhancing scanning electron microscopy imaging quality of weakly conductive samples through unsupervised learning

Scanning electron microscopy (SEM) is a crucial tool for analyzing submicron-scale structures. However, the attainment of high-quality SEM images is contingent upon the high conductivity of the material due to constraints imposed by its imaging principles. For weakly conductive materials or structures induced by intrinsic properties or organic doping, the SEM imaging quality is significantly compromised, thereby impeding the accuracy of subsequent structure-related analyses. Moreover, the unavailability of paired high-low quality images in this context renders the supervised-based image processing methods ineffective in addressing this challenge. Here, an unsupervised method based on Cycle-consistent Generative Adversarial Network (CycleGAN) was proposed to enhance the quality of SEM images for weakly conductive samples. The unsupervised model can perform end-to-end learning using unpaired blurred and clear SEM images from weakly and well-conductive samples, respectively. To address the requirements of material structure analysis, an edge loss function was further introduced to recover finer details in the network-generated images. Various quantitative evaluations substantiate the efficacy of the proposed method in SEM image quality improvement with better performance than the traditional methods. Our framework broadens the application of artificial intelligence in materials analysis, holding significant implications in fields such as materials science and image restoration.

Ultrasound technology assisted colloidal nanocrystal synthesis and biomedical applications

Non-invasive and high spatiotemporal resolution mythologies for the diagnosis and treatment of disease in clinical medicine promote the development of modern medicine. Ultrasound (US) technology provides a non-invasive, real-time, and cost-effective clinical imaging modality, which plays a significant role in chemical synthesis and clinical translation, especially in in vivo imaging and cancer therapy. On the one hand, the US treatment is usually accompanied by cavitation, leading to high temperature and pressure, so-called "hot spot", playing a significant role in sonochemical-based colloidal synthesis. Compared with the classical nucleation synthetic method, the sonochemical synthesis strategy presents high efficiency for the fabrication of colloidal nanocrystals due to its fast nucleation and growth procedure. On the other hand, the US is attractive for in vivo and medical treatment, with applications increasing with the development of novel contrast agents, such as the micro and nano bubbles, which are widely used in neuromodulation, with which the US can breach the blood-brain barrier temporarily and safely, opening a new door to neuromodulation and therapy. In terms of cancer treatment, sonodynamic therapy and US-assisted synergetic therapy show great effects against cancer and sonodynamic immunotherapy present unparalleled potentiality compared with other synergetic therapies. Further development of ultrasound technology can revolutionize both chemical synthesis and clinical translation by improving efficiency, precision, and accessibility while reducing environmental impact and enhancing patient care. In this paper, we review the US-assisted sonochemical synthesis and biological applications, to promote the next generation US technology-assisted applications.

Deconvolution based on sparsity and continuity improves the quality of ultrasound image

The application of ultrasound (US) image has been limited by its limited resolution, inherent speckle noise, and the impact of clutter and artifacts, especially in the miniaturized devices with restricted hardware conditions. In order to solve these problems, many researchers have explored a number of hardware modifications as well as algorithmic improvements, but further improvements in resolution, signal-to-noise ratio (SNR) and contrast are still needed. In this paper, a deconvolution algorithm based on sparsity and continuity (DBSC) is proposed to obtain the higher resolution, SNR, and, contrast. The algorithm begins with a relatively bold Wiener filtering for initial enhancement of image resolution in preprocessing, but it also introduces ringing noise and compromises the SNR. In further processing, the noise is suppressed based on the characteristic that the adjacent pixels of the US image are continuous as long as Nyquist sampling criterion is met, and the extraction of high-frequency information is balanced by using relatively sparse. Subsequently, the theory and experiments demonstrate that relative sparsity and continuity are general properties of US images. DBSC is compared with other deconvolution strategies through simulations and experiments, and US imaging under different transmission channels is also investigated. The final results show that the proposed method can greatly improve the resolution, as well as provide significant advantages in terms of contrast and SNR, and is also feasible in applications to devices with limited hardware.

Comparison of multichannel signal deconvolution algorithms in airborne LiDAR bathymetry based on wavelet transform

Airborne LiDAR bathymetry offers low cost and high mobility, making it an ideal option for shallow-water measurements. However, due to differences in the measurement environment and the laser emission channel, the received waveform is difficult to extract using a single algorithm. The choice of a suitable waveform processing method is thus of extreme importance to guarantee the accuracy of the bathymetric retrieval. In this study, we use a wavelet-denoising method to denoise the received waveform and subsequently test four algorithms for denoised-waveform processing, namely, the Richardson–Lucy deconvolution (RLD), blind deconvolution (BD), Wiener filter deconvolution (WFD), and constrained least-squares filter deconvolution (RFD). The simulation and measured multichannel databases are used to evaluate the algorithms, with focus on improving their performance after data-denoising and their capability of extracting water depth. Results show that applying wavelet denoising before deconvolution improves the extraction accuracy. The four algorithms perform better for the shallow-water orthogonal polarization channel (PMT2) than for the shallow horizontal row polarization channel (PMT1). Of the four algorithms, RLD provides the best signal-detection rate, and RFD is the most robust; BD has low computational efficiency, and WFD performs poorly in deep water (< 25 m).

Scaled reassigned spectrograms applied to linear transducer signals

This study evaluates the applicability of scaled reassigned spectrograms (ReSTS) on ultrasound radio frequency data obtained with a clinical linear array ultrasound transducer. The ReSTS's ability to resolve axially closely spaced objects in a phantom is compared to the classical cross-correlation method with respect to the ability to resolve closely spaced objects as individual reflectors using ultrasound pulses with different lengths. The results show that the axial resolution achieved with the ReSTS was superior to the cross-correlation method when the reflected pulses from two objects overlap. A novel B-mode imaging method, facilitating higher image resolution for distinct reflectors, is proposed.

Acoustical structured illumination for super-resolution ultrasound imaging

Structured illumination microscopy is an optical method to increase the spatial resolution of wide-field fluorescence imaging beyond the diffraction limit by applying a spatially structured illumination light. Here, we extend this concept to facilitate super-resolution ultrasound imaging by manipulating the transmitted sound field to encode the high spatial frequencies into the observed image through aliasing. Post processing is applied to precisely shift the spectral components to their proper positions in k-space and effectively double the spatial resolution of the reconstructed image compared to one-way focusing. The method has broad application, including the detection of small lesions for early cancer diagnosis, improving the detection of the borders of organs and tumors, and enhancing visualization of vascular features. The method can be implemented with conventional ultrasound systems, without the need for additional components. The resulting image enhancement is demonstrated with both test objects and ex vivo rat metacarpals and phalanges.

Enhancing effect of phase coherence factor for improvement of spatial resolution in ultrasonic imaging

PURPOSE

Spatial resolution is one of the important factors that determines ultrasound image quality. In the present study, methods using the phase variance of ultrasonic echoes received by individual transducer elements have been examined for improvement of spatial resolution.

METHOD

An imaging method, i.e., phase coherence imaging, which uses the phase coherence factor (PCF) obtained from the phase variance of received ultrasonic echoes, was recently proposed. Spatial resolution is improved by weighting ultrasonic RF signals obtained by delay-and-sum (DAS) beam forming using PCF. In the present study, alternative PCFs, i.e., exponential PCF, harmonic PCF, and Gaussian PCF, have been proposed and examined for further improvement of spatial resolution.

RESULT

Spatial resolutions realized by the proposed PCFs were evaluated by an experiment using a phantom. The full widths at half maxima of the lateral profiles of an echo from a string phantom were 2.61 mm (DAS only), 1.46 mm (conventional PCF), and 0.48-0.62 mm (proposed PCFs).

CONCLUSION

The PCFs newly proposed in the present study showed better spatial resolutions than the conventional PCF. The proposed PCFs also realized better visualization of echoes from a diffuse scattering medium than the conventional PCF.

Twofold processing for denoising ultrasound medical images

Ultrasound medical (US) imaging non-invasively pictures inside of a human body for disease diagnostics. Speckle noise attacks ultrasound images degrading their visual quality. A twofold processing algorithm is proposed in this work to reduce this multiplicative speckle noise. First fold used block based thresholding, both hard (BHT) and soft (BST), on pixels in wavelet domain with 8, 16, 32 and 64 non-overlapping block sizes. This first fold process is a better denoising method for reducing speckle and also inducing object of interest blurring. The second fold process initiates to restore object boundaries and texture with adaptive wavelet fusion. The degraded object restoration in block thresholded US image is carried through wavelet coefficient fusion of object in original US mage and block thresholded US image. Fusion rules and wavelet decomposition levels are made adaptive for each block using gradient histograms with normalized differential mean (NDF) to introduce highest level of contrast between the denoised pixels and the object pixels in the resultant image. Thus the proposed twofold methods are named as adaptive NDF block fusion with hard and soft thresholding (ANBF-HT and ANBF-ST). The results indicate visual quality improvement to an interesting level with the proposed twofold processing, where the first fold removes noise and second fold restores object properties. Peak signal to noise ratio (PSNR), normalized cross correlation coefficient (NCC), edge strength (ES), image quality Index (IQI) and structural similarity index (SSIM), measure the quantitative quality of the twofold processing technique. Validation of the proposed method is done by comparing with anisotropic diffusion (AD), total variational filtering (TVF) and empirical mode decomposition (EMD) for enhancement of US images. The US images are provided by AMMA hospital radiology labs at Vijayawada, India.

Deconvolution of vibroacoustic images using a simulation model based on a three dimensional point spread function

Vibro-acoustography (VA) is a medical imaging method based on the difference-frequency generation produced by the mixture of two focused ultrasound beams. VA has been applied to different problems in medical imaging such as imaging bones, microcalcifications in the breast, mass lesions, and calcified arteries. The obtained images may have a resolution of 0.7-0.8mm. Current VA systems based on confocal or linear array transducers generate C-scan images at the beam focal plane. Images on the axial plane are also possible, however the system resolution along depth worsens when compared to the lateral one. Typical axial resolution is about 1.0cm. Furthermore, the elevation resolution of linear array systems is larger than that in lateral direction. This asymmetry degrades C-scan images obtained using linear arrays. The purpose of this article is to study VA image restoration based on a 3D point spread function (PSF) using classical deconvolution algorithms: Wiener, constrained least-squares (CLSs), and geometric mean filters. To assess the filters' performance on the restored images, we use an image quality index that accounts for correlation loss, luminance and contrast distortion. Results for simulated VA images show that the quality index achieved with the Wiener filter is 0.9 (when the index is 1.0 this indicates perfect restoration). This filter yielded the best result in comparison with the other ones. Moreover, the deconvolution algorithms were applied to an experimental VA image of a phantom composed of three stretched 0.5mm wires. Experiments were performed using transducer driven at two frequencies, 3075kHz and 3125kHz, which resulted in the difference-frequency of 50kHz. Restorations with the theoretical line spread function (LSF) did not recover sufficient information to identify the wires in the images. However, using an estimated LSF the obtained results displayed enough information to spot the wires in the images. It is demonstrated that the phase of the theoretical and the experimental PSFs are dissimilar. This fact prevents VA image restoration with the current theoretical PSF. This study is a preliminary step towards understanding the restoration of VA images through the application of deconvolution filters.

Ultrasound despeckling for contrast enhancement

Images produced by ultrasound systems are adversely hampered by a stochastic process known as speckle. A despeckling method based upon removing outlier is proposed. The method is developed to contrast enhance B-mode ultrasound images. The contrast enhancement is with respect to decreasing pixel variations in homogeneous regions while maintaining or improving differences in mean values of distinct regions. A comparison of the proposed despeckling filter is compared with the other well known despeckling filters. The evaluations of despeckling performance are based upon improvements to contrast enhancement, structural similarity, and segmentation results on a Field II simulated image and actual B-mode cardiac ultrasound images captured in vivo.

An ultrasonic imaging speckle-suppression and contrast-enhancement technique by means of frequency compounding and coded excitation

A method for improving the contrast resolution of B-mode images is proposed by combining the speckle-reduction technique of frequency compounding (FC) and the coded excitation and pulse-compression technique called resolution enhancement compression (REC). FC suppresses speckle but at the expense of a reduction in axial resolution. Using REC, the axial resolution and bandwidth of the imaging system was doubled. Therefore, by combining REC with FC (REC-FC), the tradeoff between axial resolution and contrast enhancement was extended significantly. Simulations and experimental measurements were conducted with a single-element transducer (f/2.66) having a center frequency of 2.25 MHz and a -3-dB bandwidth of 50%. Simulations and measurements of hyperechoic (+6 dB) tissue-mimicking targets were imaged. Four FC cases were evaluated: full-, half-, third-, and fourth-width of the true impulse response bandwidth. The image quality metrics used to compare REC-FC to conventional pulsing (CP) and CP-FC were contrast-to-noise ratio (CNR), speckle signal-to-noise ratio, histogram pixel intensity, and lesion signal-to-noise ratio. Increases in CNR of 121%, 231%, 302%, and 391% were obtained in experiments when comparing REC-FC for the full-, half-, third-, and fourth-width cases to CP. Furthermore, smaller increases in CNR of 112%, 233%, and 309% were obtained in experiments when comparing CP-FC for the half-, third-, and fourth-width cases to CP. Improved lesion detectability was observed by using REC-FC.