Towards a biomechanical breast model to simulate and investigate breast compression and its effects in mammography and tomosynthesis.

Objective. In mammography, breast compression forms an essential part of the examination and is achieved by lowering a compression paddle on the breast. Compression force is mainly used as parameter to estimate the degree of compression. As the force does not consider variations of breast size or tissue composition, over- and undercompression are a frequent result. This causes a highly varying perception of discomfort or even pain in the case of overcompression during the procedure. To develop a holistic, patient specific workflow, as a first step, breast compression needs to be thoroughly understood. The aim is to develop a biomechanical finite element breast model that accurately replicates breast compression in mammography and tomosynthesis and allows in-depth investigation. The current work focuses thereby, as a first step, to replicate especially the correct breast thickness under compression.Approach. A dedicated method for acquiring ground truth data of uncompressed and compressed breasts within magnetic resonance (MR) imaging is introduced and transferred to the compression within x-ray mammography. Additionally, we created a simulation framework where individual breast models were generated based on MR images.Main results. By fitting the finite element model to the results of the ground truth images, a universal set of material parameters for fat and fibroglandular tissue could be determined. Overall, the breast models showed high agreement in compression thickness with a deviation of less than ten percent from the ground truth.Significance. The introduced breast models show a huge potential for a better understanding of the breast compression process.

DDQR (dynamic DNA QR coding): An efficient algorithm to represent DNA barcode sequences.

A DNA barcode is a short piece of standard DNA sequence used for species determination and discrimination. Representation of DNA barcodes is essential for DNA barcodes' applications in the transportation and recognition of biological materials. Previously, we have compared different strategies for representing the DNA barcodes. In the present study, we have developed a compression algorithm based on binary coding or Huffman coding scheme, followed by converting the binary digits into Base64 digits. The combination of this compression algorithm and the QR representation leads to the dynamic DNA QR coding algorithm (DDQR). We tested the DDQR algorithm on simulated data and real DNA barcode sequences from the commonly used plant and animal DNA barcode markers: rbcL, matK, trnH-psbA, ITS2, and COI. We compared the compression efficiency of DDQR and another state-of-the-art DNA compression algorithm GeCo3 for sequences with various base compositions and lengths. We found that DDQR had a higher compression rate than GeCo3 for DNA sequences shorter than 800 bp, which is the typical size range for DNA barcodes. We also upgraded a web server (http://www.1kmpg.cn/ddqr) that provides three functions: retrieval of DNA barcode sequences, encoding DNA barcode sequences to DDQR codes, and decoding DDQR codes to DNA barcode sequences. The DDQR algorithm and the webserver will be invaluable to applying DNA barcode technology in the food and traditional medicine industries.

Temporal Prediction Model-Based Fast Inter CU Partition for Versatile Video Coding.

Versatile video coding (VVC) adopts an advanced quad-tree plus multi-type tree (QTMT) coding structure to obtain higher compression efficiency, but it comes at the cost of a considerable increase in coding complexity. To effectively reduce the coding complexity of the QTMT-based coding unit (CU) partition, we propose a fast inter CU partition method based on a temporal prediction model, which includes early termination QTMT partition and early skipping multi-type tree (MT) partition. Firstly, according to the position of the current CU, we extract the optimal CU partition information of the position corresponding to the previously coded frames. We then establish a temporal prediction model based on temporal CU partition information to predict the current CU partition. Finally, to reduce the cumulative of errors of the temporal prediction model, we further extract the motion vector difference (MVD) of the CU to determine whether the QTMT partition can be terminated early. The experimental results show that the proposed method can reduce the inter coding complexity of VVC by 23.19% on average, while the Bjontegaard delta bit rate (BDBR) is only increased by 0.97% on average under the Random Access (RA) configuration.

Visual saliency guided perceptual adaptive quantization based on HEVC intra-coding for planetary images.

Due to the limited storage space of spacecraft and downlink bandwidth in the data delivery during planetary exploration, an efficient way for image compression onboard is essential to reduce the volume of acquired data. Applicable for planetary images, this study proposes a perceptual adaptive quantization technique based on Convolutional Neural Network (CNN) and High Efficiency Video Coding (HEVC). This technique is used for bitrate reduction while maintaining the subjective visual quality. The proposed algorithm adaptively determines the Coding Tree Unit (CTU) level Quantization Parameter (QP) values in HEVC intra-coding using the high-level features extracted by CNN. A modified model based on the residual network is exploited to extract the saliency map for a given image automatically. Furthermore, based on the saliency map, a CTU level QP adjustment technique combining global saliency contrast and local saliency perception is exploited to realize a flexible and adaptive bit allocation. Several quantitative performance metrics that efficiently correlate with human perception are used for evaluating image quality. The experimental results reveal that the proposed algorithm achieves better visual quality along with a maximum of 7.17% reduction in the bitrate as compared to the standard HEVC coding.

Mapping of fatty acid composition with free-breathing MR spectroscopic imaging and compressed sensing.

Nonalcoholic fatty liver disease (NAFLD) is a growing health problem, and a major challenge in NAFLD management is identifying which patients are at risk of progression to more serious disease. Simple measurements of liver fat content are not strong predictors of clinical outcome, but biomarkers related to fatty acid composition (ie, saturated vs. unsaturated fat) may be more effective. MR spectroscopic imaging (MRSI) methods allow spatially resolved, whole-liver measurements of chemical composition but are traditionally limited by slow acquisition times. In this work we present an accelerated MRSI acquisition based on spin echo single point imaging (SE-SPI), which, using appropriate sampling and compressed sensing reconstruction, allows free-breathing acquisition in a mouse model of fatty liver disease. After validating the technique's performance in oil/water phantoms, we imaged mice that had received a normal diet or a methionine and choline deficient (MCD) diet, some of which also received supplemental injections of iron to mimic hepatic iron overload. SE-SPI was more resistant to the line-broadening effects of iron than single-voxel spectroscopy measurements, and was consistently able to measure the amplitudes of low-intensity spectral peaks that are important to characterizing fatty acid composition. In particular, in the mice receiving the MCD diet, SE-SPI showed a significant decrease in a metric associated with unsaturated fat, which is consistent with the literature. This or other related metrics may therefore offer more a specific biomarker of liver health than fat content alone. This preclinical study is an important precursor to clinical testing of the proposed method. MR-based quantification of fatty acid composition may allow for improved characterization of non-alcoholic fatty liver disease. A spectroscopic imaging method with appropriate sampling strategy allows whole-liver mapping of fat composition metrics in a free-breathing mouse model. Changes in metrics like the surrogate unsaturation index (UIs) are visible in mice receiving a diet which induces fat accumulation in the liver, as compared to a normal diet; such metrics may prove useful in future clinical studies of liver disease.

A Wide Dynamic Range Neural Data Acquisition System With High-Precision Delta-Sigma ADC and On-Chip EC-PC Spike Processor.

A high-performance, wide dynamic range, fully-integrated neural interface is one key component for many advanced bidirectional neuromodulation technologies. In this paper, to complement the previously proposed frequency-shaping amplifier (FSA) and high-precision electrical microstimulator, we will present a proof-of-concept design of a neural data acquisition (DAQ) system that includes a 15-bit, low-power Delta-Sigma analog-to-digital converter (ADC) and a real-time spike processor based on one exponential component-polynomial component (EC-PC) algorithm. High-precision data conversion with low power consumption and small chip area is achieved by employing several techniques, such as opamp-sharing, multi-bit successive approximation (SAR) quantizer, two-step summation, and ultra-low distortion data weighted averaging (DWA). The on-chip EC-PC engine enables low latency, automatic detection, and extraction of spiking activities, thus supporting closed-loop control, real-time data compression and /or neural information decoding. The prototype chip was fabricated in a 0.13  µm CMOS process and verified in both bench-top and In-Vivo experiments. Bench-top measurement results indicate the designed ADC achieves a peak signal-to-noise and distortion ratio (SNDR) of 91.8 dB and a dynamic range of 93.0 dB over a 10 kHz bandwidth, where the total power consumption of the modulator is only 20  µW at 1.0 V supply, corresponding to a figure-of-merit (FOM) of 31.4fJ /conversion-step. In In-Vivo experiments, the proposed DAQ system has been demonstrated to obtain high-quality neural activities from a rat's motor cortex and also greatly reduce recovery time from system saturation due to electrical microstimulation.

Lateralization of virtual sound sources with a binaural cochlear-implant sound coding strategy inspired by the medial olivocochlear reflex.

Many users of bilateral cochlear implants (BiCIs) localize sound sources less accurately than do people with normal hearing. This may be partly due to using two independently functioning CIs with fixed compression, which distorts and/or reduces interaural level differences (ILDs). Here, we investigate the potential benefits of using binaurally coupled, dynamic compression inspired by the medial olivocochlear reflex; an approach termed "the MOC strategy" (Lopez-Poveda et al., 2016, Ear Hear 37:e138-e148). Twelve BiCI users were asked to localize wideband (125-6000 Hz) noise tokens in a virtual horizontal plane. Stimuli were processed through a standard (STD) sound processing strategy (i.e., involving two independently functioning sound processors with fixed compression) and three different implementations of the MOC strategy: one with fast (MOC1) and two with slower contralateral control of compression (MOC2 and MOC3). The MOC1 and MOC2 strategies had effectively greater inhibition in the higher than in the lower frequency channels, while the MOC3 strategy had slightly greater inhibition in the lower than in the higher frequency channels. Localization was most accurate with the MOC1 strategy, presumably because it provided the largest and less ambiguous ILDs. The angle error improved slightly from 25.3° with the STD strategy to 22.7° with the MOC1 strategy. The improvement in localization ability over the STD strategy disappeared when the contralateral control of compression was made slower, presumably because stimuli were too short (200 ms) for the slower contralateral inhibition to enhance ILDs. Results suggest that some MOC implementations hold promise for improving not only speech-in-noise intelligibility, as shown elsewhere, but also sound source lateralization.

Efficient characterization of individual differences in compression ratio preference.

While wide dynamic range compression (WDRC) is a standard feature of modern hearing aids, it can be difficult to fit compression settings to individual hearing aid users. The goal of the current study was to develop a practical test to learn the preference of individual listeners for different compression ratio (CR) settings in different listening conditions (speech-in-quiet and speech-in-noise). While it is possible to exhaustively test different CR settings, such methods can take many hours to complete, making them impractical. Bayesian optimization methods were used to find CR preferences in individual listeners in a relatively short amount of time. Using this practical preference learning test, individual differences in CR preference were examined across a relatively wide range of CR settings in different listening conditions. In experiment 1, the accuracy of the preference learning test in normal hearing listeners was verified. In experiment 2, it is shown that individual hearing impaired listeners differ in their CR preferences, and listeners tended to prefer the CR setting identified by the preference learning test over both linear gain or the National Acoustics Lab--Nonlinear 2 CR prescription based on their audiograms.

Spiral waves characterization: Implications for an automated cardiodynamic tissue characterization.

BACKGROUND AND OBJECTIVE: Spiral waves are phenomena observed in cardiac tissue especially during fibrillatory activities. Spiral waves are revealed through in-vivo and in-vitro studies using high density mapping that requires special experimental setup. Also, in-silico spiral wave analysis and classification is performed using membrane potentials from entire tissue. In this study, we report a characterization approach that identifies spiral wave behaviors using intracardiac electrogram (EGM) readings obtained with commonly used multipolar diagnostic catheters that perform localized but high-resolution readings. Specifically, the algorithm is designed to distinguish between stationary, meandering, and break-up rotors. METHODS: The clustering and classification algorithms are tested on simulated data produced using a phenomenological 2D model of cardiac propagation. For EGM measurements, unipolar-bipolar EGM readings from various locations on tissue using two catheter types are modeled. The distance measure between spiral behaviors are assessed using normalized compression distance (NCD), an information theoretical distance. NCD is a universal metric in the sense it is solely based on compressibility of dataset and not requiring feature extraction. We also introduce normalized FFT distance (NFFTD) where compressibility is replaced with a FFT parameter. RESULTS: Overall, outstanding clustering performance was achieved across varying EGM reading configurations. We found that effectiveness in distinguishing was superior in case of NCD than NFFTD. We demonstrated that distinct spiral activity identification on a behaviorally heterogeneous tissue is also possible. CONCLUSIONS: This report demonstrates a theoretical validation of clustering and classification approaches that provide an automated mapping from EGM signals to assessment of spiral wave behaviors and hence offers a potential mapping and analysis framework for cardiac tissue wavefront propagation patterns.

Considerations in applying compressed sensing to in vivo phosphorus MR spectroscopic imaging of human brain at 3T.

The purpose of this study was to apply compressed sensing method for accelerated phosphorus MR spectroscopic imaging (31P-MRSI) of human brain in vivo at 3T. Fast 31P-MRSI data of five volunteers were acquired on a 3T clinical MR scanner using pulse-acquire sequence with a pseudorandom undersampling pattern for a data reduction factor of 5.33 and were reconstructed using compressed sensing. Additionally, simulated 31P-MRSI human brain tumor datasets were created to analyze the effects of k-space sampling pattern, data matrix size, regularization parameters of the reconstruction, and noise on the compressed sensing accelerated 31P-MRSI data. The 31P metabolite peak ratios of the full and compressed sensing accelerated datasets of healthy volunteers in vivo were similar according to the results of a Bland-Altman test. The estimated effective spatial resolution increased with reduction factor and sampling more at the k-space center. A lower regularization parameter for both total variation and L1-norm penalties resulted in a better compressed sensing reconstruction of 31P-MRSI. Although the root-mean-square error increased with noise levels, the compressed sensing reconstruction was robust for up to a reduction factor of 10 for the simulated data that had sharply defined tumor borders. As a result, compressed sensing was successfully applied to accelerate 31P-MRSI of human brain in vivo at 3T.

Fast implementation for compressive recovery of highly accelerated cardiac cine MRI using the balanced sparse model.

PURPOSE: Sparsity-promoting regularizers can enable stable recovery of highly undersampled magnetic resonance imaging (MRI), promising to improve the clinical utility of challenging applications. However, lengthy computation time limits the clinical use of these methods, especially for dynamic MRI with its large corpus of spatiotemporal data. Here, we present a holistic framework that utilizes the balanced sparse model for compressive sensing and parallel computing to reduce the computation time of cardiac MRI recovery methods. THEORY AND METHODS: We propose a fast, iterative soft-thresholding method to solve the resulting ℓ1-regularized least squares problem. In addition, our approach utilizes a parallel computing environment that is fully integrated with the MRI acquisition software. The methodology is applied to two formulations of the multichannel MRI problem: image-based recovery and k-space-based recovery. RESULTS: Using measured MRI data, we show that, for a 224 × 144 image series with 48 frames, the proposed k-space-based approach achieves a mean reconstruction time of 2.35 min, a 24-fold improvement compared a reconstruction time of 55.5 min for the nonlinear conjugate gradient method, and the proposed image-based approach achieves a mean reconstruction time of 13.8 s. CONCLUSION: Our approach can be utilized to achieve fast reconstruction of large MRI datasets, thereby increasing the clinical utility of reconstruction techniques based on compressed sensing. Magn Reson Med 77:1505-1515, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Data dimensionality reduction and data fusion for fast characterization of green coffee samples using hyperspectral sensors.

Hyperspectral sensors represent a powerful tool for chemical mapping of solid-state samples, since they provide spectral information localized in the image domain in very short times and without the need of sample pretreatment. However, due to the large data size of each hyperspectral image, data dimensionality reduction (DR) is necessary in order to develop hyperspectral sensors for real-time monitoring of large sets of samples with different characteristics. In particular, in this work, we focused on DR methods to convert the three-dimensional data array corresponding to each hyperspectral image into a one-dimensional signal (1D-DR), which retains spectral and/or spatial information. In this way, large datasets of hyperspectral images can be converted into matrices of signals, which in turn can be easily processed using suitable multivariate statistical methods. Obviously, different 1D-DR methods highlight different aspects of the hyperspectral image dataset. Therefore, in order to investigate their advantages and disadvantages, in this work, we compared three different 1D-DR methods: average spectrum (AS), single space hyperspectrogram (SSH) and common space hyperspectrogram (CSH). In particular, we have considered 370 NIR-hyperspectral images of a set of green coffee samples, and the three 1D-DR methods were tested for their effectiveness in sensor fault detection, data structure exploration and sample classification according to coffee variety and to coffee processing method. Principal component analysis and partial least squares-discriminant analysis were used to compare the three separate DR methods. Furthermore, low-level and mid-level data fusion was also employed to test the advantages of using AS, SSH and CSH altogether. Graphical Abstract Key steps in hyperspectral data dimenionality reduction.

Rapid 3D-imaging of phosphocreatine recovery kinetics in the human lower leg muscles with compressed sensing.

The rate of phosphocreatine (PCr) resynthesis following physical exercise is an accepted index of mitochondrial oxidative metabolism and has been studied extensively with unlocalized (31)P-MRS methods and small surface coils. Imaging experiments using volume coils that measure several muscles simultaneously can provide new insights into the variability of muscle function in healthy and diseased states. However, they are limited by long acquisition times relative to the dynamics of PCr recovery. This work focuses on the implementation of a compressed sensing technique to accelerate imaging of PCr resynthesis following physical exercise, using a modified three-dimensional turbo-spin-echo sequence and principal component analysis as sparsifying transform. The compressed sensing technique was initially validated using 2-fold retrospective undersampling of fully sampled data from four volunteers acquired on a 7T MRI system (voxel size: 1.6 mL, temporal resolution: 24 s), which led to an accurate estimation of the mono-exponential PCr resynthesis rate constant (mean error <6.4%). Acquisitions with prospective 2-fold acceleration (temporal resolution: 12 s) demonstrated that three-dimensional mapping of PCr resynthesis is possible at a temporal resolution that is sufficiently high for characterizing the recovery curve of several muscles in a single measurement.

EEG data compression to monitor DoA in telemedicine.

Data compression techniques have been widely used to process and transmit huge amount of EEG data in real-time and remote EEG signal processing systems. In this paper we propose a lossy compression technique, F-shift, to compress EEG signals for remote depth of Anaesthesia (DoA) monitoring. Compared with traditional wavelet compression techniques, our method not only preserves valuable clinical information with high compression ratios, but also reduces high frequency noises in EEG signals. Moreover, our method has negligible compression overheads (less than 0.1 seconds), which can greatly benefit real-time EEG signal monitoring systems. Our extensive experiments demonstrate the efficiency and effectiveness of the proposed compression method.

Digital reconstruction based on angular spectrum diffraction with the ridge of wavelet transform in holographic phase-contrast microscopy.

A numerical reconstruction technique of digital holography based on angular spectrum diffraction by means of the ridge of Gabor wavelet transform (GWT) is presented. Appling the GWT, the object wave can be reconstructed by calculating the wavelet coefficients of the hologram at the ridge of the GWT automatically even if the spectrum of the virtual image is disturbed by the other spectrum. It provides a way to eliminate the effect of the zero-order and the twin-image terms without the spatial filtering. In particular, based on the angular spectrum theory, GWT is applied to the digital holographic phase-contrast microscopy on biological specimens. The theory, the results of a simulation and an experiment of an onion specimen are shown.

Nadi Tarangini: a pulse based diagnostic system.

Ayurveda is a traditional medicine and natural healing system in India. Nadi-Nidan (pulse-based diagnosis) is a prominent method in Ayurveda, and is known to dictate all the salient features of a human body. In this paper, we provide details of our procedure for obtaining the complete spectrum of the nadi pulses as a time series. The system Nadi Tarangini1 contains a diaphragm element equipped with strain gauge, a transmitter cum amplifier, and a digitizer for quantifying analog signal. The system acquires the data with 16-bit accuracy with practically no external electronic or interfering noise. Prior systems for obtaining the nadi pulses have been few and far between, when compared to systems such as ECG. The waveforms obtained with our system have been compared with these other similar equipment developed earlier, and is shown to contain more details. The pulse waveform is also shown to have the desirable variations with respect to age of patients, and the pressure applied at the sensing element. The system is being evaluated by Ayurvedic practitioners as a computer-aided diagnostic tool.

A novel ultrasonic thermotherapy instrument.

An structure is proposed for fabricating a portable prototype apparatus, using the embedded control technique based on ARM core, which has a simple structure and small size. The hardware is easily updated and maintained due to its modular structure, the software has a visualized interface and hierarchical design mode, so the apparatus has a good peculiarity like compact structure and control. Moreover, the build-in PID algorithm for temperature control and the power driver element for ultrasonic output power have been optimized the experiment results demonstrate that using this structure and optimization method. Due to the speed of data collecting by parallel interface is much quick than the speed of data collecting by serial interface, so a reliable performance of this apparatus can be achieved, which can meet with the need of clinical application.

Implantable stimulator failures: causes, outcomes, and solutions.

Current designs of implantable stimulators for neuroprostheses exploit trade-offs in power consumption, functionality, volume, selectivity and ease of use. However, no stimulator design should compromise reliability or safety. In this paper, we consider the common failures in implantable stimulators, their causes, outcomes and solutions. A new coding scheme, Manchester non-return-to-zero code, is proposed to format the cable signal in order to avoid prolonged DC current between cable exposures. Thus, in the event of cable failure, the bidirectional charge-balanced current between cable exposures maintains the nearby tissue in safe condition.

On the mathematical reconstruction of two dimensional plants.

A set of 10, chosen medicinal plants (some of them with a reputation as remedies for tuberculosis) has been investigated through Partitioned Iterated Function Systems-Semi Fractals with Angle (PIFS-SFA) coding, Lempel, Ziv, Welch with quantization and noise (LZW-QN) compression, and surface density statistics (f(alpha)-SDS) discrimination techniques. The final outcomes of this quantitative analysis were, firstly: the linear ordering of the plants in question accompanied by the hope that it reflects their medical significance, secondly: the mathematical representation of each of the plants, and thirdly: the impressive compression achieved, leading to remarkable computer memory saving, and still permitting successful pattern recognition i.e., proper identification of the plant from the compressed image.

An adaptive multiple-input multiple-output analog-to-digital converter for high density neuroprosthetic electrode arrays.

On chip signal compression is one of the key technologies driving development of energy efficient biotelemetry devices. In this paper, we describe a novel architecture for analog-to-digital (A/D) conversion that combines sigma delta conversion with the spatial data compression in a single module. The architecture called multiple-input multiple-output (MIMO) sigma-delta is based on a min-max gradient descent optimization of a regularized cost function that naturally leads to an A/D formulation. Experimental results with simulated and recorded multichannel data demonstrate the effectiveness of the proposed architecture to eliminate cross-channel redundancy in high density microelectrode data, thus superceding the performance of parallel independent data converters in terms of its energy efficiency.