Beamforming Through Regularized Inverse Problems in Ultrasound Medical Imaging.

Applying the Capon adaptive beamformer in medical ultrasound imaging results in enhanced resolution by improving the interference-suppressing capabilities of the array. This improvement comes at the expense of an increased computational complexity. We have investigated the application of a beamspace adaptive beamformer for medical ultrasound imaging, which can be used to achieve reduced computational complexity with performance comparable to that of the Capon beamformer. The idea behind beamspace beamforming is that, instead of using the spatial statistics of the elements in the array to differentiate between signals and interference, we use the spatial statistics of a set of orthogonal beams, which are formed in different directions. This represents a shift from element space to beamspace. Because the majority of interference in medical ultrasound imaging is constrained to a limited spatial interval due to the focused transmit beam, this latter space can be reduced to a dimension that is lower than that of element space. We show, using simulations and experimental data, that this dimension can be selected as low as 3 while still achieving performance comparable to its element space counterpart.

In this paper, the adaptive, minimum variance (MV) beam-former is applied to medical ultrasound imaging. The significant resolution and contrast gain provided by the adaptive, minimum variance (MV) beamformer, introduces the possibility of plane wave (PW) ultrasound imaging. Data is obtained using Field II and a 7 MHz, 128-elements, linear array transducer with lambda/2-spacing. MV is compared to the conventional delay-and-sum (DS) beamformer with Boxcar and Hanning weights. Furthermore, the PW images are compared to the a conventional ultrasound image, obtained from a linear scan sequence. The four approaches, {Linear Scan, DS Boxcar, DS Hanning, MV}, have full width at half maximum of {0.82, 0.71, 1.28, 0.12} mm and peak side-lobe levels of {-40.1,-16.8,-34.4,-57.0} dB.

In medical ultrasound B-mode imaging, by increasing the image contrast, spatial and temporal resolution, it will help to improve the diagnostic and decision making process. Previously the unsharp masking (UM) techniques have been successfully implemented for digital image processing as edge enhancement techniques. However, the outcomes of the method are limited and influenced by the image formats conversion and lower dynamic range. Moreover the conventional UM technique sensitive to noise due to the presence of linear high pass filter which cannot discriminate signal from noise. Furthermore, the technique only enhances the image in darker compared to lighter regions. To overcome those issues, a new improved UM method compatible with ultrasound B-mode imaging was applied to compound plane wave imaging (CPWI). The received radio frequency (RF) signal was beamformed with delay and sum (DAS) and filtered delay and sum (FDMAS). Results show that the proposed techniques are not only able to improve the image contrast but also the spatial resolution. The UM technique manages to improve the B-mode image lateral resolution (LR) by 18.36% and 10.25% as well as reduced the peak side lobes (PSL) by 16.67 dB and 21.24 dB on DAS and FDMAS respectively when compounded with CPWI, N=13. For the same number of compounding and beamforming order, the image contrast ratio (CR) has been improved by 10.35 dB and 7.39 dB accordingly.

Photoacoustic tomography (PAT) offers structural and functional imaging of living biological tissue with highly sensitive optical absorption contrast and excellent spatial resolution comparable to medical ultrasound (US) imaging. We report the development of a fully integrated PAT and US dual-modality imaging system, which performs signal scanning, image reconstruction and display for both photoacoustic (PA) and US imaging all in a truly real-time manner. The backprojection (BP) algorithm for PA image reconstruction is optimized to reduce the computational cost and facilitate parallel computation on a state of the art graphics processing unit (GPU) card. For the first time, PAT and US imaging of the same object can be conducted simultaneously and continuously, at a real time frame rate, presently limited by the laser repetition rate of 10 Hz. Noninvasive PAT and US imaging of human peripheral joints in vivo were achieved, demonstrating the satisfactory image quality realized with this system. Another experiment, simultaneous PAT and US imaging of contrast agent flowing through an artificial vessel was conducted to verify the performance of this system for imaging fast biological events. The GPU based image reconstruction software code for this dual-modality system is open source and available for download from http://sourceforge.net/projects/pat realtime .

For phased-array ultrasound imaging, alternative beamforming techniques and their VLSI circuits are studied to form a fully digital receive front-end hardware. In order to increase the timing accuracy in beamforming, a computationally efficient interpolation scheme to increase the sampling rate is examined. For adaptive beamforming, a phase aberration correction method with very low computational complexity is described. Image quality performance of the method is examined by processing the non-aberrated and aberrated phased-array experimental data sets of an ultrasound resolution phantom. A digital beamforming scheme based on receive focusing at the raster focal points is examined. The sector images of the resolution phantom, reconstructed from the phased-array experimental data by beamforming at the radial and raster focal points, are presented for comparison of the image resolution performances of the two beamforming schemes. VLSI circuits and their implementations for the proposed techniques are presented.

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.

Plane-wave imaging (PWI) is a best way to improve the frame rate in ultrasound imaging. However, due to the lack of transmit focus in PWI, the image quality is poor. A novel coherent plane-wave compounding (CPWC) was proposed to handle this issue, but the increased plane waves (PWs) reduced the frame rate. Thus, we combine a novel Double-Stage Delay Multiply and Sum (DS-DMAS) beamformer, which is shown to be better to improve the image quality, with plane-wave compounding to analyze the performance in terms of resolution and contrast. The simulation tests are carried out to show that applying DS-DMAS beamformer can achieve better results even if the number of transmitted PWs are decreased.

Rigid registration of intraoperative ultrasound (US) and preoperative CT image is important for providing real-time guidance during operations. However, due to the low spatial and temporal resolutions and the dissimilarity between US and CT, accurate registration of CT and US images is still a challenging problem. The authors propose an adaptive-mask-based CT and US registration method. The registration is initialized by matching the image regions of CT and US with intensity distinctiveness. The registration is a multistage iterative process in which the US region mask is adaptively updated. Each stage is an interleaving process of optimizing a global similarity energy and updating the mask of US by selecting high saliency and local statistical dependency regions. Performances of their proposed method and mutual information (MI) based method are validated with simulated, in vitro phantom and real patient datasets. Results show that their method has larger capture range in all datasets. The estimated transformation parameters in their method are more accurate than the mutual information based method. By using an adaptively updated mask of the US image, regions with salient intensity information and high intensity correlation with CT are included in the registration. Regions which have low correlation with CT such as artifacts are excluded in the registration so that the robustness and accuracy of the intensity-based registration method are improved.

Ultrasound (US) imaging is considered as one of the most advanced diagnostic tools in medical use. However, a drawback of the medical US imaging is its poor quality of the image, which is affected by speckle noise. In this paper, a MultiLayer Back-Propagation Neural Networks (MLBPNNs) with the Epanechnikov fuzzy function is proposed to reduce the speckle, and while at the same time, enhance the lesion boundaries of the US image. The main goal of the proposed method is to improve the quality of US image so as to improve the quality of the humans interpretation and the computer systems autoedge detection. In order to automatically detect the lesion boundary by a computer system, an edge enhancement is required. Evaluating the simulation results by Peak Signal to Noise Ratio (PSNR), Normalized Mean Square Error (NMSE), Detail Variance (DV), and Background Variance (BV), the proposed method demonstrates an increased performance of reducing the speckle and enhancing the edge. The proposed method has higher PSNR than conventional methods and can remove the speckle sufficiently, so that tumor boundaries of real US breast tumor image could be preserved and detected.

Medical ultrasound imaging has transformed the disease identification in the human body in the last few decades. The major setback for ultrasound medical images is speckle noise. Speckle noise is created in ultrasound images due to numerous reflections of ultrasound signals from hard tissues of human body. Speckle noise corrupts the medical ultrasound images dropping the detectable quality of the image. An endeavor is made to recover the image quality of ultrasound medical images by using block based hard and soft thresholding of wavelet coefficients. Medical ultrasound image is transformed to wavelet domain using debauchee's mother wavelet. Divide the approximate and detailed coefficients into uniform blocks of size 8×8, 16×16, 32×32 and 64×64. Hard and soft thresholding on these blocks of approximate and detailed coefficients are applied. Inverse transformation to original spatial domain produces a noise reduced ultrasound image. Experiments were conducted on medical ultrasound images obtained from diagnostic centers in Vijayawada, India. Quality of improved images in measured using peak signal to noise ratio (PSNR), image quality index (IQI), structural similarity index (SSEVI).

Objective:Spinal fusion surgeries require accurate placement of pedicle screws in anatomic corridors without breaching bone boundaries. We are developing a combined ultrasound and photoacoustic image guidance system to avoid pedicle screw misplacement and accidental bone breaches, which can lead to nerve damage.Methods:Pedicle cannulation was performed on a human cadaver, with co-registered photoacoustic and ultrasound images acquired at various time points during the procedure. Bony landmarks obtained from coherence-based ultrasound images of lumbar vertebrae were registered to post-operative CT images. Registration methods were additionally tested on an ex vivo caprine vertebra.Results:Locally weighted short-lag spatial coherence (LW-SLSC) ultrasound imaging enhanced the visualization of bony structures with generalized contrast-to-noise ratios (gCNRs) of 0.99 and 0.98–1.00 in the caprine and human vertebrae, respectively. Short-lag spatial coherence (SLSC) and amplitude-based delay-and-sum (DAS) ultrasound imaging generally produced lower gCNRs of 0.98 and 0.84, respectively, in the caprine vertebra and 0.84–0.93 and 0.34–0.99, respectively, in the human vertebrae. The mean ± standard deviation of the area of −6 dB contours created from DAS photoacoustic images acquired with an optical fiber inserted in prepared pedicle holes (i.e., fiber surrounded by cancellous bone) and holes created after intentional breaches (i.e., fiber exposed to cortical bone) was 10.06±5.22 mm2 and 2.47±0.96 mm2, respectively (p < 0.01).Conclusions:Coherence-based LW-SLSC and SLSC beamforming improved visualization of bony anatomical landmarks for ultrasound-to-CT registration, while amplitude-based DAS beamforming successfully distinguished photoacoustic signals within the pedicle from less desirable signals characteristic of impending bone breaches.Significance:These results are promising to improve visual registration of ultrasound and photoacoustic images with CT images, as well as to assist surgeons with identifying and avoiding impending bone breaches during pedicle cannulation in spinal fusion surgeries.

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