Cardiovascular instrumentation for spaceflight

The observation mechanisms dealing with pressure, flow, morphology, temperature, etc. are discussed. The approach taken in the performance of this study was to (1) review ground and space-flight data on cardiovascular function, including earlier related ground-based and space-flight animal studies, Mercury, Gemini, Apollo, Skylab, and recent bed-rest studies, (2) review cardiovascular measurement parameters required to assess individual performance and physiological alternations during space flight, (3) perform an instrumentation survey including a literature search as well as personal contact with the applicable investigators, (4) assess instrumentation applicability with respect to the established criteria, and (5) recommend future research and development activity. It is concluded that, for the most part, the required instrumentation technology is available but that mission-peculiar criteria will require modifications to adapt the applicable instrumentation to a space-flight configuration

Compressed Sensing for Elastography in Portable Ultrasound

Bonghun Shin, Soo Jeon, Jeongwon Ryu and Hyock Ju Kwon, “Compressed Sensing for Elastography in Portable Ultrasound,” Ultrasonic Imaging, 39(6), pp. 393-413, Copyright © The Author(s) 2017. Reprinted by permission of SAGE Publications. https://doi.org/10.1177/0161734617716938Portable wireless ultrasound has many advantages such as high portability, easy connectivity, strong individuality, as well as on-site diagnostic ability in real-time. Some of the modern portable ultrasound devices offer high image quality and multiple ultrasound modes comparable to console style ultrasound, however, none of them provides ultrasound elastography function that enables the diagnosis of malignant legions using elastic properties. This is mainly due to the limitations of hardware performance and wireless data transfer speed for processing the large amount of data for elastography. Therefore, reduction of the data transfer size is one of the feasible solutions to overcome these limitations. Recently compressive sensing (CS) theory has been rigorously studied as a means to break the conventional Nyquist sampling rate and thus can significantly decrease the amount of measurement signals without sacrificing signal quality. In this research, we implemented various CS reconstruction frameworks and comparatively evaluated their reconstruction performance for realizing ultrasound elastography function on portable ultrasound. Combinations of three most common model bases (FT, DCT, and WA) and two reconstruction algorithms (l_1 minimization and BSBL) were considered for CS frameworks. Two kinds of numerical phantoms, echoic and elastography phantoms, were developed to evaluate performance of CS on B-mode images and elastograms, respectively. To assess the reconstruction quality, mean absolute error (MAE), signal-to-noise (SNRe) and contrast-to-noise (CNRe) were measured on the B-mode images and elastograms from CS reconstructions. Results suggest that CS reconstruction adopting BSBL algorithm with DCT model basis can yield the best results for all the measures tested, and the maximum data reduction rate for producing readily discernable elastograms is around 60%.Natural Sciences and Engineering Research Council || RGPIN-2015-05273, RGPIN-2015-04118, RGPAS-354703-201

ViPS: Visual processing system for medical imaging

Imaging has become an indispensable tool in modern medicine. Various powerful and expensive platforms to study medical imaging applications appear in recent years. In this article, we design and propose a Visual Processing System (ViPS) that processes medical imaging applications efficiently. ViPS provides a user-friendly programming environment and high-performance architecture to perform image analysis, features extraction and object recognition for complex real-time images or videos. The data structure of image or video is described in the program memory using pattern descriptors; ViPS uses specialized 3D memory structure to handle complex images or videos and processes them on microprocessors or application specific hardware accelerators. The proposed system is highly reliable in terms of cost, performance, and power. ViPS based system is implemented and tested on a Xilinx Virtex-7 FPGA VC707 Evaluation Kit. The performance of ViPS is compared with the Intel i7 multi-core, GPU Jetson TK1 Embedded Development Kit with 192 CUDA cores based graphic systems. When compared with the Intel and GPU-based systems, the results show that ViPS performs real-time video reconstruction at 2x and 1.45x of higher frame rate, achieves 14.6x to 4.8x of speedup while executing different image processing applications and 20.3% and 12.6% of speedup for video processing algorithms respectively.Peer Reviewe

Efficient architectures of heterogeneous fpga-gpu for 3-d medical image compression

The advent of development in three-dimensional (3-D) imaging modalities have generated a massive amount of volumetric data in 3-D images such as magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), and ultrasound (US). Existing survey reveals the presence of a huge gap for further research in exploiting reconfigurable computing for 3-D medical image compression. This research proposes an FPGA based co-processing solution to accelerate the mentioned medical imaging system. The HWT block implemented on the sbRIO-9632 FPGA board is Spartan 3 (XC3S2000) chip prototyping board. Analysis and performance evaluation of the 3-D images were been conducted. Furthermore, a novel architecture of context-based adaptive binary arithmetic coder (CABAC) is the advanced entropy coding tool employed by main and higher profiles of H.264/AVC. This research focuses on GPU implementation of CABAC and comparative study of discrete wavelet transform (DWT) and without DWT for 3-D medical image compression systems. Implementation results on MRI and CT images, showing GPU significantly outperforming single-threaded CPU implementation. Overall, CT and MRI modalities with DWT outperform in term of compression ratio, peak signal to noise ratio (PSNR) and latency compared with images without DWT process. For heterogeneous computing, MRI images with various sizes and format, such as JPEG and DICOM was implemented. Evaluation results are shown for each memory iteration, transfer sizes from GPU to CPU consuming more bandwidth or throughput. For size 786, 486 bytes JPEG format, both directions consumed bandwidth tend to balance. Bandwidth is relative to the transfer size, the larger sizing will take more latency and throughput. Next, OpenCL implementation for concurrent task via dedicated FPGA. Finding from implementation reveals, OpenCL on batch procession mode with AOC techniques offers substantial results where the amount of logic, area, register and memory increased proportionally to the number of batch. It is because of the kernel will copy the kernel block refer to batch number. Therefore memory bank increased periodically related to kernel block. It was found through comparative study that the tree balance and unroll loop architecture provides better achievement, in term of local memory, latency and throughput

An ultrasonic system for intravascular measurement and visualisation of anatomical structures and blood flow

Imperial Users onl

Development of a Feasible Elastography Framework for Portable Ultrasound

Portable wireless ultrasound is emerging as a new ultrasound device due to the advantages such as small size, lightweight and affordable price. Its high portability allows practitioners to make diagnostic and therapeutic decisions in real-time without having to take the patients out of their environment. Recent portable ultrasound devices are equipped with sophisticated processors and image processing algorithms providing high image quality. Some of them are able to deliver multiple ultrasound modes including color Doppler, echocardiography, and endovaginal examination. Nevertheless, they are still lack of elastography functions due to the limitations in computational performance and data transfer speed via wireless communication. In order to implement the elastography function in the wireless portable ultrasound devices, this thesis proposes a new strain estimation method to significantly reduce the computation time and a compressive sensing framework to minimize the data transfer size. Firstly, a robust phase-based strain estimator (RPSE) is developed to overcome the limited hardware performance of portable ultrasound. The RPSE is not only computationally efficient but also robust to variations of the speed of sound, sampling frequency and pulse repetition. The RPSE has been compared with other representative strain estimators including time-delay, displacement-gradient, and conventional phase-based strain estimators (TSE, DSE and PSE, respectively). It has been shown that the RPSE is superior in several elastographic image quality measures, including signal-to-noise (SNRe) and contrast-to-noise (CNRe), and the computational efficiency. The study indicates that the RPSE method can deliver the acceptable level of elastography and fast computational speed for the ultrasound echo data sets from the numerical and experimental phantoms. According to the results from the numerical phantom experiment, RPSE can achieve highest values of SNRe and CNRe (around 5.22 and 47.62 dB) among all strain estimators tested, and almost 100 times higher computational efficiency than TSE and DSE (around 0.06 vs. 5.76 seconds per frame for RPSE and TSE, respectively). Secondly, as a means to reduce the large amount of ultrasound measurement data that has to be transmitted via wireless communication, the compressive sensing (CS) framework has been applied to elastography. The performance of CS is highly dependent on the selection of model basis to represent the sparse expansion as well as the reconstruction algorithm to recover the original data from the compressed signal. Therefore, it is essential to compose the optimal combination of model basis and reconstruction algorithm for CS framework to achieve the best CS performance in terms of image quality and the maximum data reduction. In this thesis, three model bases, discrete Fourier transform (FT), discrete cosine transform (DCT), and wave atoms (WA), along with two reconstruction algorithms, L1 minimization (L1) and Block sparse Bayesian learning (BSBL) are tested. Using B-mode and elastogram images of simulated numerical phantoms, the quality of CS reconstruction is assessed in terms of three image quality measures, mean absolute error (MAE), SNRe, and CNRe, at varying data reduction (subsampling) rates. The results illustrate that BSBL based CS frameworks can generally deliver much higher image quality and subsampling rate compared with L1-based ones. In particular, the CS frameworks adopting DCT and BSBL offer the best CS performance. The results also suggests that the maximum subsampling rates without causing image degradation are 40% for L1-based framework and 60% for BSBL-based framework, respectively. The contributions of this thesis help realize elastography functionality in portable ultrasound, thereby significantly expanding its utility. For example, the diagnosis of malignant lesions, even when a patient cannot be moved to hospital immediately, is possible with the portable ultrasound. Furthermore, the SPSE method and the CS framework can be individually employed for the conventional ultrasound device as well as other telemedicine applications, to enhance computational efficiency and image quality

Toward quantitative limited-angle ultrasound reflection tomography to inform abdominal HIFU treatment planning

High-Intensity Focused Ultrasound (HIFU) is a treatment modality for solid cancers of the liver and pancreas which is non-invasive and free from many of the side-effects of radiotherapy and chemotherapy. The safety and efficacy of abdominal HIFU treatment is dependent on the ability to bring the therapeutic sound waves to a small focal ”lesion” of known and controllable location within the patient anatomy. To achieve this, pre-treatment planning typically includes a numerical simulation of the therapeutic ultrasound beam, in which anatomical compartment locations are derived from computed tomography or magnetic resonance images. In such planning simulations, acoustic properties such as density and speed-of-sound are assumed for the relevant tissues which are rarely, if ever, determined specifically for the patient. These properties are known to vary between patients and disease states of tissues, and to influence the intensity and location of the HIFU lesion. The subject of this thesis is the problem of non-invasive patient-specific measurement of acoustic tissue properties. The appropriate method, also, of establishing spatial correspondence between physical ultrasound transducers and modeled (imaged) anatomy via multimodal image reg-istration is also investigated; this is of relevance both to acoustic tissue property estimation and to the guidance of HIFU delivery itself. First, the principle of a method is demonstrated with which acoustic properties can be recovered for several tissues simultaneously using reflection ultrasound, given accurate knowledge of the physical locations of tissue compartments. Second, the method is developed to allow for some inaccuracy in this knowledge commensurate with the inaccuracy typical in abdominal multimodal image registration. Third, several current multimodal image registration techniques, and two novel modifications, are compared for accuracy and robustness. In conclusion, relevant acoustic tissue properties can, in principle, be estimated using reflected ultrasound data that could be acquired using diagnostic imaging transducers in a clinical setting

Elasticity mapping for breast cancer diagnosis using tactile imaging and auxiliary sensor fusion

Tactile Imaging (TI) is a technology utilising capacitive pressure sensors to image elasticity distributions within soft tissues such as the breast for cancer screening. TI aims to solve critical problems in the cancer screening pathway, particularly: low sensitivity of manual palpation, patient discomfort during X-ray mammography, and the poor quality of breast cancer referral forms between primary and secondary care facilities. TI is effective in identifying ‘non-palpable’, early-stage tumours, with basic differential ability that reduced unnecessary biopsies by 21% in repeated clinical studies. TI has its limitations, particularly: the measured hardness of a lesion is relative to the background hardness, and lesion location estimates are subjective and prone to operator error. TI can achieve more than simple visualisation of lesions and can act as an accurate differentiator and material analysis tool with further metric development and acknowledgement of error sensitivities when transferring from phantom to clinical trials. This thesis explores and develops two methods, specifically inertial measurement and IR vein imaging, for determining the breast background elasticity, and registering tactile maps for lesion localisation, based on fusion of tactile and auxiliary sensors. These sensors enhance the capabilities of TI, with background tissue elasticity determined with MAE < 4% over tissues in the range 9 kPa – 90 kPa and probe trajectory across the breast measured with an error ratio < 0.3%, independent of applied load, validated on silicone phantoms. A basic TI error model is also proposed, maintaining tactile sensor stability and accuracy with 1% settling times < 1.5s over a range of realistic operating conditions. These developments are designed to be easily implemented into commercial systems, through appropriate design, to maximise impact, providing a stable platform for accurate tissue measurements. This will allow clinical TI to further reduce benign referral rates in a cost-effective manner, by elasticity differentiation and lesion classification in future works.Tactile Imaging (TI) is a technology utilising capacitive pressure sensors to image elasticity distributions within soft tissues such as the breast for cancer screening. TI aims to solve critical problems in the cancer screening pathway, particularly: low sensitivity of manual palpation, patient discomfort during X-ray mammography, and the poor quality of breast cancer referral forms between primary and secondary care facilities. TI is effective in identifying ‘non-palpable’, early-stage tumours, with basic differential ability that reduced unnecessary biopsies by 21% in repeated clinical studies. TI has its limitations, particularly: the measured hardness of a lesion is relative to the background hardness, and lesion location estimates are subjective and prone to operator error. TI can achieve more than simple visualisation of lesions and can act as an accurate differentiator and material analysis tool with further metric development and acknowledgement of error sensitivities when transferring from phantom to clinical trials. This thesis explores and develops two methods, specifically inertial measurement and IR vein imaging, for determining the breast background elasticity, and registering tactile maps for lesion localisation, based on fusion of tactile and auxiliary sensors. These sensors enhance the capabilities of TI, with background tissue elasticity determined with MAE < 4% over tissues in the range 9 kPa – 90 kPa and probe trajectory across the breast measured with an error ratio < 0.3%, independent of applied load, validated on silicone phantoms. A basic TI error model is also proposed, maintaining tactile sensor stability and accuracy with 1% settling times < 1.5s over a range of realistic operating conditions. These developments are designed to be easily implemented into commercial systems, through appropriate design, to maximise impact, providing a stable platform for accurate tissue measurements. This will allow clinical TI to further reduce benign referral rates in a cost-effective manner, by elasticity differentiation and lesion classification in future works