Anand Ramkumar

Bio: Anand Ramkumar is an academic researcher from Indian Institute of Technology Madras. The author has contributed to research in topics: Compressed sensing & Image quality. The author has an hindex of 2, co-authored 3 publications receiving 12 citations.

Strategic Undersampling and Recovery Using Compressed Sensing for Enhancing Ultrasound Image Quality

Abstract: In conventional focused beamforming (CFB), there is a known tradeoff between the active aperture size of the ultrasound transducer array and the resulting image quality. Increasing the size of the active aperture leads to an increase in the image quality of the ultrasound system at the expense of increased system cost. An alternate approach is to get rid of the requirement of having consecutive active receive elements and instead place them in a random order in a larger aperture. This, in turn, creates an undersampled situation where there are only $M$ active elements placed in a larger aperture, which can accommodate $N$ consecutive receive elements (with $M ). It is possible to formulate and solve the above-mentioned undersampling situation using a compressed sensing (CS) approach. In our previous work, we had proposed Gaussian undersampling strategy for reducing the number of active receive elements. In this work, we introduce a novel framework, namely Gaussian undersampling-based CS framework (GAUCS) with wave atoms as a sparsifying basis for CFB imaging method. The performance of the proposed method is validated using simulation and in vitro phantom data. Without an increase in the active elements, it is found that the proposed GAUCS framework improved the lateral resolution (LR) and image contrast by 27% and 1.5 times, respectively, while using 16 active elements and by 39% and 1.1 times, respectively, while using 32 active elements. Thus, the GAUCS framework can play a significant role in improving the performance, especially, of affordable point-of-care ultrasound systems.

Compressed Sensing Approach for Reducing the Number of Receive Elements in Synthetic Transmit Aperture Imaging

Abstract: Recently, researchers have shown an increased interest in ultrasound imaging methods alternate to conventional focused beamforming (CFB). One such approach is based on the synthetic aperture (SA) scheme; more popular are the ones based on synthetic transmit aperture (STA) schemes with a single-element transmit or multielement STA (MSTA). However, one of the main challenges in translating such methods to low-cost ultrasound systems is the tradeoffs among image quality, frame rate, and complexity of the system. These schemes use all the transducer elements during receive, which dictates a corresponding number of parallel receive channels, thus increasing the complexity of the system. A considerable amount of literature has been published on compressed sensing (CS) for SA imaging. Such studies are aimed at reducing the number of transmissions in SA but still recover images of acceptable quality at high frame rate and fail to address the complexity due to full-aperture receive. In this work, we adopt a CS framework to MSTA, with a motivation to reduce the number of receive elements and data. The CS recovery performance was assessed for the simulation data, tissue-mimicking phantom data, and an example in vivo biceps data. It was found that in spite of using 50% receive elements and overall using only 12.5% of the data, the images recovered using CS were comparable to those of reference full-aperture case in terms of estimated lateral resolution, contrast-to-noise ratio, and structural similarity indices. Thus, the proposed CS framework provides some fresh insights into translating the MSTA imaging method to affordable ultrasound scanners.

Novel POES Method for Raw RF Signal Recovery in Sparse Synthetic Aperture Ultrasound Acquisition: Preliminary Performance Analysis

Abstract: Reduction in number of active receive channels, or using sparse arrays, in synthetic aperture-based schemes is one of the attractive approaches for reducing the ultrasound system cost and complexity. However, reconstructed images obtained from such schemes suffer from poorer image quality compared to that obtained using data from full-aperture. Several methods of recovering the missing data from sparse data acquisition have been demonstrated to contribute towards improved image quality. However, most of the reported random sparse recovery strategies such as compressed sensing (CS) involve optimization, and thus have increased computational complexity. Recently, a novel time delay difference-based approach called Projection Onto Elliptical Sets (POES) was developed to recover missing frame data in sparse transmit and limited receive aperture acquisition. In this study, the recovery performance of the novel POES method is evaluated on data acquired using typical sparse receive array configuration in diverging beam synthetic aperture transmit (DBSAT) scheme. Two random sparse distribution based on uniform and Gaussian were evaluated for 50%, 75% and 87.5% undersampling. The results suggested that POES method resulted in better CNR and lower NRMSE values in comparison to CS. The recovery time taken by POES method was 0.0723 ± 0.0377 seconds as opposed to 5–6 hours required for CS. Thus, POES method is demonstrated to be a practical, computationally more efficient alternate approach of recovering missing channel information for sparse random receive DBSAT data.

Compressed Sensing Approach for Reducing the Number of Receive Elements in Synthetic Transmit Aperture Imaging

Abstract: Recently, researchers have shown an increased interest in ultrasound imaging methods alternate to conventional focused beamforming (CFB). One such approach is based on the synthetic aperture (SA) scheme; more popular are the ones based on synthetic transmit aperture (STA) schemes with a single-element transmit or multielement STA (MSTA). However, one of the main challenges in translating such methods to low-cost ultrasound systems is the tradeoffs among image quality, frame rate, and complexity of the system. These schemes use all the transducer elements during receive, which dictates a corresponding number of parallel receive channels, thus increasing the complexity of the system. A considerable amount of literature has been published on compressed sensing (CS) for SA imaging. Such studies are aimed at reducing the number of transmissions in SA but still recover images of acceptable quality at high frame rate and fail to address the complexity due to full-aperture receive. In this work, we adopt a CS framework to MSTA, with a motivation to reduce the number of receive elements and data. The CS recovery performance was assessed for the simulation data, tissue-mimicking phantom data, and an example in vivo biceps data. It was found that in spite of using 50% receive elements and overall using only 12.5% of the data, the images recovered using CS were comparable to those of reference full-aperture case in terms of estimated lateral resolution, contrast-to-noise ratio, and structural similarity indices. Thus, the proposed CS framework provides some fresh insights into translating the MSTA imaging method to affordable ultrasound scanners.

Subsampling Approaches for Compressed Sensing with Ultrasound Arrays in Non-Destructive Testing.

Abstract: Full Matrix Capture is a multi-channel data acquisition method which enables flexible, high resolution imaging using ultrasound arrays. However, the measurement time and data volume are increased considerably. Both of these costs can be circumvented via compressed sensing, which exploits prior knowledge of the underlying model and its sparsity to reduce the amount of data needed to produce a high resolution image. In order to design compression matrices that are physically realizable without sophisticated hardware constraints, structured subsampling patterns are designed and evaluated in this work. The design is based on the analysis of the Cramer–Rao Bound of a single scatterer in a homogeneous, isotropic medium. A numerical comparison of the point spread functions obtained with different compression matrices and the Fast Iterative Shrinkage/Thresholding Algorithm shows that the best performance is achieved when each transmit event can use a different subset of receiving elements and each receiving element uses a different section of the echo signal spectrum. Such a design has the advantage of outperforming other structured patterns to the extent that suboptimal selection matrices provide a good performance and can be efficiently computed with greedy approaches.

Acceleration of reconstruction for compressed sensing based synthetic transmit aperture imaging by using in-phase/quadrature data.

Abstract: Compressed sensing-based synthetic transmit aperture (CS-STA) was previously proposed to recover the full radio-frequency (RF) channel dataset of synthetic transmit aperture (STA) from that of a smaller number of randomly apodized plane wave (PW) transmissions. In this way, the imaging frame rate (FR) and contrast are improved with maintained spatial resolution, compared with those of STA. Because CS-STA reconstruction is repeated for all receive elements and RF samples (with a high sampling frequency), the recovery of STA dataset in RF domain is time-consuming. In the meantime, a large amount of RF data needs to be transferred and stored, resulting in an increase of system complexity and required memory space. In this study, CS-STA is extended to in-phase/quadrature (IQ) domain (with lower sampling frequency) for the recovery of baseband STA IQ dataset to accelerate the CS-STA reconstruction by reducing the amount of data to be processed. More importantly, CS-STA reconstruction using IQ data is of practical importance, as clinical ultrasound systems typically record baseband IQ signal instead of RF signal. Simulations, phantom and in vivo experiments verify the feasibility of CS-STA in IQ domain for the recovery of STA dataset. More specifically, CS-STA using IQ data achieves similar image quality and appreciably improves reconstruction speed (by ∼3 times) compared with that using RF data. These findings demonstrate that IQ-domain CS-STA is capable of relieving the computational and storage burdens, which may facilitate the implementation of CS-STA in practical ultrasound systems.

An efficient medical image compression technique for telemedicine systems

Abstract: The medical practitioners primarily used medical images to reveal abnormalities in the internal critical organs and structures of body covered by the bones and the skin. Main application of medical imaging is to perform medical diagnosis from the image features extracted. Processing these images are very much required for assessing the patient’s condition. However, long-term monitoring of the patient using certain medical imaging technologies produces enormous volumes of data everyday. There is a need to compress the data to reduce redundancies and speed up the acquisition process, making them suitable for efficient transmission and analysis. Recently Compressed Sensing (CS) has been widely used for image compression at high speed with fewer samples. High-quality reconstruction using conventional CS and Block based CS (BCS) is a matter of utmost concern as they follow the random selection of samples. This could be overcome by adaptively selecting samples from various image regions using Adaptive Block Compressed Sensing (ABCS). This paper proposes Coefficient Mixed Thresholding based ABCS (CMT-ABCS) for compressing different medical images with a high compression ratio. The experimental outcomes exhibit a noteworthy improvement in the proposed method’s performance metrics when compared to other state-of-the-art approaches. There is a increase in PSNR of 5–10 dB, SSIM of 0.1–0.2 with NCC values closer to 1 and NAE values closer to 0. At low sampling rate, the reconstruction was greatly enhanced with only around 10% measurements/samples. • Reconstructs entire image with only 10% of samples. • Coefficient mixed thresholding involves simple calculation procedures. • Achieves 40%–70% of compression. • Significant improvement in image quality measures like PSNR, SSIM, NCC and NAE. • Blocking artifacts and improper block reconstruction are eliminated completely. • Remarkable quality improvement is noticed even at low sampling rate.

Acceleration of reconstruction for compressed sensing based synthetic transmit aperture imaging by using in-phase/quadrature data

Abstract: Compressed sensing-based synthetic transmit aperture (CS-STA) was previously proposed to recover the full radio-frequency (RF) channel dataset of synthetic transmit aperture (STA) from that of a smaller number of randomly apodized plane wave (PW) transmissions. In this way, the imaging frame rate (FR) and contrast are improved with maintained spatial resolution, compared with those of STA. Because CS-STA reconstruction is repeated for all receive elements and RF samples (with a high sampling frequency), the recovery of STA dataset in RF domain is time-consuming. In the meantime, a large amount of RF data needs to be transferred and stored, resulting in an increase of system complexity and required memory space. In this study, CS-STA is extended to in-phase/quadrature (IQ) domain (with lower sampling frequency) for the recovery of baseband STA IQ dataset to accelerate the CS-STA reconstruction by reducing the amount of data to be processed. More importantly, CS-STA reconstruction using IQ data is of practical importance, as clinical ultrasound systems typically record baseband IQ signal instead of RF signal. Simulations, phantom and in vivo experiments verify the feasibility of CS-STA in IQ domain for the recovery of STA dataset. More specifically, CS-STA using IQ data achieves similar image quality and appreciably improves reconstruction speed (by ∼3 times) compared with that using RF data. These findings demonstrate that IQ-domain CS-STA is capable of relieving the computational and storage burdens, which may facilitate the implementation of CS-STA in practical ultrasound systems.