Improving moisture profile retrieval from broadband infrared radiances with an optimized first-guess scheme

Variational retrieval of legacy atmospheric moisture profiles needs to begin with a first guess. An optimized first-guess scheme is developed for moisture profile retrieval from broadband infrared (IR) radiances. In this scheme, the non-exponential response of moisture mixing ratio to IR radiance at high temperatures (> 273 K) is considered. It is found that the first guess of low-level (below 550 hPa) moisture profiles is substantially improved after the new scheme. The data collected by Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the Meteosat Second Generation are used for validation. This scheme provides an important optimization method for the next generation of Geostationary Operational Environmental Satellite (GOES)-R legacy profile retrieval algorithm because the Advanced Baseline Imager (ABI) onboard the GOES-R has very similar configurations to SEVIRI.     

Error analysis of ultrasonic tissue doppler velocity estimation techniques for quantification of velocity and strain

Recent work in the field of Doppler tissue imaging has focused mainly on the quantification of results involving the use of techniques of strain and strain-rate imaging. These results are based on measuring a velocity gradient between two points, a known distance apart, in the region-of-interest. Although many recent publications have demonstrated the potential of this technique in clinical terms, the method still suffers from low repeatability. The work presented here demonstrates, through the use of a rotating phantom arrangement and a custom developed single element ultrasound system, that this is a consequence of the fundamental accuracy of the technique used to estimate the original velocities. Results are presented comparing the performance of the conventional Kasai autocorrelation velocity estimator with those obtained using time domain cross-correlation and the complex cross-correlation model based estimator. The results demonstrate that the complex cross-correlation model based technique is able to offer lower standard deviations of the velocity gradient estimations compared with the Kasai algorithm.     

Bootstrapped DEPICT for error estimation in PET functional imaging

Basis pursuit denoising is a new approach for data-driven estimation of parametric images from dynamic positron emission tomography (PET) data. At present, this kinetic modeling technique does not allow for the estimation of the errors on the parameters. These estimates are useful when performing subsequent statistical analysis, such as, inference across a group of subjects or when applying partial volume correction algorithms. The difficulty with calculating the error estimates is a consequence of using an overcomplete dictionary of kinetic basis functions. In this paper, a bootstrap approach for the estimation of parameter errors from dynamic PET data is presented. This paper shows that the bootstrap can be used successfully to compute parameter errors on a region of interest or parametric image basis. Validation studies evaluate the methods performance on simulated and measured PET data ([(11)C]Diprenorphine-opiate receptor and [(11)C]Raclopride-dopamine D(2) receptor). The method is presented in the context of PET neuroreceptor binding studies, however, it has general applicability to a wide range of PET/SPET radiotracers in neurology, oncology and cardiology.     

Use of refractive direct ophthalmoscopy for estimation of refractive error

The purpose of this study was to find if direct ophthalmoscopy, a simple technique, could be used to give an approximate value of the refractive correction for a patient. This would shorten the time and lessen the effort to be expended during the following retinoscopic examination done for finding the patient's refractive correction. The use of direct ophthalmoscopy for this specific purpose is especially desirable where retinoscopic examination is quite tedious, e.g. uncooperative patients like children, bed-ridden patients and mentally retarded subjects, in patients with a large central corneal opacity and in patients having a large refractive error. The study was divided into two phases. In phase I, refractive direct ophthalmoscopy followed by classical retinoscopy was done for 92 subjects (184 eyes) in the age group of 11-35 years. The method of regression analysis was used to find a regression equation relating the readings to refractive error determined by the two above techniques. In phase II of study, the refractive correction needed for 50 other subjects in the similar age group was estimated using this regression equation by inserting their respective direct ophthalmoscopy readings. Then, these estimated values and classical retinoscopic examination values were compared. The refractive error determined after retinoscopy and that derived from regression equation (incorporating direct ophthalmoscopy readings) was statistically comparable (t = 0.52, p = 0.60). The correlation coefficient (r value) between the two methods was 0.37. Direct ophthalmoscopic lens reading can be used to give a fairly accurate estimate of refractive error in a patient's eye by using a linear regression equation, which relates these two examination techniques. The magnitude of astigmatic error, however, cannot be obtained.     

Dynamically constructed network with error correction for accurate ventricle volume estimation

Automated ventricle volume estimation (AVVE) on cardiac magnetic resonance (CMR) images is very important for clinical cardiac disease diagnosis. However, current AVVE methods ignore the error correction for the estimated volume. This results in clinically intolerable ventricle volume estimation error and further leads to wrong ejection fraction (EF) assessment, which significantly limits the application potential of AVVE methods. The objective of this paper is to address this problem with AVVE and further make it more clinically applicable. We proposed a dynamically constructed network to achieve accurate AVVE. First, we introduced a novel dynamically constructed deep learning framework, that evolves a single model into a bi-model volume estimation network. In this way, the EF correlation can be built directly based on the bi-model network. Second, we proposed an error correction strategy using dynamically created residual nodes, which is based on stochastic configurations with an EF correlation constraint. Finally, we formulated the proposed method into an end-to-end joint optimization framework for accurate ventricle volume estimation with effective error correction. Experiments and comparisons on large-scale cardiac magnetic resonance datasets were carried out. Results show that the proposed method outperforms state-of-the-art methods, and has good potential for clinical application. Besides, the proposed method is the first work to achieve error correction for AVVE and also has the potential to be extended to other medical index estimation tasks.     

Self-supervised driven consistency training for annotation efficient histopathology image analysis

Training a neural network with a large labeled dataset is still a dominant paradigm in computational histopathology. However, obtaining such exhaustive manual annotations is often expensive, laborious, and prone to inter and intra-observer variability. While recent self-supervised and semi-supervised methods can alleviate this need by learning unsupervised feature representations, they still struggle to generalize well to downstream tasks when the number of labeled instances is small. In this work, we overcome this challenge by leveraging both task-agnostic and task-specific unlabeled data based on two novel strategies: (i) a self-supervised pretext task that harnesses the underlying multi-resolution contextual cues in histology whole-slide images to learn a powerful supervisory signal for unsupervised representation learning; (ii) a new teacher-student semi-supervised consistency paradigm that learns to effectively transfer the pretrained representations to downstream tasks based on prediction consistency with the task-specific unlabeled data.We carry out extensive validation experiments on three histopathology benchmark datasets across two classification and one regression based tasks, i.e., tumor metastasis detection, tissue type classification, and tumor cellularity quantification. Under limited-label data, the proposed method yields tangible improvements, which is close to or even outperforming other state-of-the-art self-supervised and supervised baselines. Furthermore, we empirically show that the idea of bootstrapping the self-supervised pretrained features is an effective way to improve the task-specific semi-supervised learning on standard benchmarks. Code and pretrained models are made available at: https://github.com/srinidhiPY/SSL_CR_Histo.     

Error estimation for reconstruction of neuronal spike firing from fast calcium imaging

Calcium imaging is becoming an increasingly popular technology to indirectly measure activity patterns in local neuronal networks. Calcium transients reflect neuronal spike patterns allowing for spike train reconstructed from calcium traces. The key to judging spiking train authenticity is error estimation. However, due to the lack of an appropriate mathematical model to adequately describe this spike-calcium relationship, little attention has been paid to quantifying error ranges of the reconstructed spike results. By turning attention to the data characteristics close to the reconstruction rather than to a complex mathematic model, we have provided an error estimation method for the reconstructed neuronal spiking from calcium imaging. Real false-negative and false-positive rates of 10 experimental Ca²⁺ traces were within the estimated error ranges and confirmed that this evaluation method was effective. Estimation performance of the reconstruction of spikes from calcium transients within a neuronal population demonstrated a reasonable evaluation of the reconstructed spikes without having real electrical signals. These results suggest that our method might be valuable for the quantification of research based on reconstructed neuronal activity, such as to affirm communication between different neurons.OCIS codes: (170.3010) Image reconstruction techniques, (170.2655) Functional monitoring and imaging, (100.3190) Inverse problems     

An error analysis perspective for patient alignment systems

Purpose

This paper analyses the effects of error sources which can be found in patient alignment systems. As an example, an ultrasound (US) repositioning system and its transformation chain are assessed. The findings of this concept can also be applied to any navigation system.

Methods and materials

In a first step, all error sources were identified and where applicable, corresponding target registration errors were computed. By applying error propagation calculations on these commonly used registration/calibration and tracking errors, we were able to analyse the components of the overall error. Furthermore, we defined a special situation where the whole registration chain reduces to the error caused by the tracking system. Additionally, we used a phantom to evaluate the errors arising from the image-to-image registration procedure, depending on the image metric used. We have also discussed how this analysis can be applied to other positioning systems such as Cone Beam CT–based systems or Brainlab’s ExacTrac.

Results

The estimates found by our error propagation analysis are in good agreement with the numbers found in the phantom study but significantly smaller than results from patient evaluations. We probably underestimated human influences such as the US scan head positioning by the operator and tissue deformation. Rotational errors of the tracking system can multiply these errors, depending on the relative position of tracker and probe.

Conclusions

We were able to analyse the components of the overall error of a typical patient positioning system. We consider this to be a contribution to the optimization of the positioning accuracy for computer guidance systems.     

Diffuse reflectance technique for infrared analysis of urinary calculi

We investigated the application of diffuse reflectance infrared Fourier transform spectroscopy to analysis of urinary calculi and compared its operation with that of older sampling techniques (pellet, mull, and attenuated total reflectance). The new method requires shorter sample preparation time and less sample (30 micrograms). Quantitative measurements are easier. Because of the additivity of the Kubelka-Munk functions, a quasi-exhaustive collection of infrared spectra of mixtures of possible components of stones can be compiled, facilitating a computerized intrepretation of infrared spectra of urinary calculi.     

Within-subject template estimation for unbiased longitudinal image analysis

Longitudinal image analysis has become increasingly important in clinical studies of normal aging and neurodegenerative disorders. Furthermore, there is a growing appreciation of the potential utility of longitudinally acquired structural images and reliable image processing to evaluate disease modifying therapies. Challenges have been related to the variability that is inherent in the available cross-sectional processing tools, to the introduction of bias in longitudinal processing and to potential over-regularization. In this paper we introduce a novel longitudinal image processing framework, based on unbiased, robust, within-subject template creation, for automatic surface reconstruction and segmentation of brain MRI of arbitrarily many time points. We demonstrate that it is essential to treat all input images exactly the same as removing only interpolation asymmetries is not sufficient to remove processing bias. We successfully reduce variability and avoid over-regularization by initializing the processing in each time point with common information from the subject template. The presented results show a significant increase in precision and discrimination power while preserving the ability to detect large anatomical deviations; as such they hold great potential in clinical applications, e.g. allowing for smaller sample sizes or shorter trials to establish disease specific biomarkers or to quantify drug effects.     

A novel walking speed estimation scheme and its application to treadmill control for gait rehabilitation

ABSTRACT: BACKGROUND: Virtual reality (VR) technology along with treadmill training (TT) can effectively provide goal-oriented practice and promote improved motor learning in patients with neurological disorders. Moreover, the VR + TT scheme may enhance cognitive engagement for more effective gait rehabilitation and greater transfer to over ground walking. For this purpose, we developed an individualized treadmill controller with a novel speed estimation scheme using swing foot velocity, which can enable user-driven treadmill walking (UDW) to more closely simulate over ground walking (OGW) during treadmill training. OGW involves a cyclic acceleration-deceleration profile of pelvic velocity that contrasts with typical treadmill-driven walking (TDW), which constrains a person to walk at a preset constant speed. In this study, we investigated the effects of the proposed speed adaptation controller by analyzing the gait kinematics of UDW and TDW, which were compared to those of OGW at three pre-determined velocities. METHODS: Ten healthy subjects were asked to walk in each mode (TDW, UDW, and OGW) at three pre-determined speeds (0.5 m/s, 1.0 m/s, and 1.5 m/s) with real time feedback provided through visual displays. Temporal-spatial gait data and 3D pelvic kinematics were analyzed and comparisons were made between UDW on a treadmill, TDW, and OGW. RESULTS: The observed step length, cadence, and walk ratio defined as the ratio of stride length to cadence were not significantly different between UDW and TDW. Additionally, the average magnitude of pelvic acceleration peak values along the anterior-posterior direction for each step and the associated standard deviations (variability) were not significantly different between the two modalities. The differences between OGW and UDW and TDW were mainly in swing time and cadence, as have been reported previously (Lee and Hidler, and Wass et. al.). Also, step lengths between OGW and TDW were different for 0.5 m/s and 1.5 m/s gait velocities, and walk ratio between OGS and UDW was different for 1.0 m/s gait velocities. CONCLUSIONS: Our treadmill control scheme implements similar gait biomechanics of TDW, which has been used for repetitive gait training in a small and constrained space as well as controlled and safe environments. These results reveal that users can walk as stably during UDW as TDW and employ similar strategies to maintain walking speed in both UDW and TDW. Furthermore, since UDW can allow a user to actively participate in the virtual reality (VR) applications with variable walking velocity, it can induce more cognitive activities during the training with VR, which may enhance motor learning effects.     

A novel walking speed estimation scheme and its application to treadmill control for gait rehabilitation

Vestibular prosthetics transmit angular velocities to the nervous system via electrical stimulation. Head-fixed gyroscopes measure angular motion, but the gyroscope coordinate system will not be coincident with the sensory organs the prosthetic replaces. Here we show a simple calibration method to align gyroscope measurements with the anatomical coordinate system. We benchmarked the method with simulated movements and obtain proof-of-concept with one healthy subject. The method was robust to misalignment, required little data, and minimal processing.     

An electronic system of infrared spectral data for toxicological analysis

A large number of non-volatile poisons have been analyzed by infrared spectroscopy and spectral indices have been stored as standards. Comparison of the spectra of unknown samples with the standard spectra allows identification of the unknown samples. This comparison is carried out by computer.     

Dual-channel cascade pose estimation network trained on infrared thermal image and groundtruth annotation for real-time gait measurement

Real-time spatiotemporal parameter measurement for gait analysis is challenging. Previous techniques for 3D motion analysis, such as inertial measurement units, marker based motion analysis or the use of depth cameras, require expensive equipment, highly skilled staff and limits feasibility for sustainable applications. In this paper a dual-channel cascaded network to perform contactless real-time 3D human pose estimation using a single infrared thermal video as an input is proposed. An algorithm to calculate gait spatiotemporal parameters is presented by tracking estimated joint locations. Additionally, a training dataset composed of infrared thermal images and groundtruth annotations has been developed. The annotation represents a set of 3D joint locations from infrared optical trackers, which is considered to be the gold standard in clinical applications. On the proposed dataset, our pose estimation framework achieved a 3D human pose mean error of below 21 mm and outperforms state-of-the-art methods. The results reveal that the proposed system achieves competitive skeleton tracking performance on par with the other motion capture devices and exhibited good agreement with a marker-based three-dimensional motion analysis system (3DMA) over a range of spatiotemporal parameters. Moreover, the process is shown to distinguish differences in over-ground gait parameters of older adults with and without Hemiplegia’s disease. We believe that the proposed approaches can measure selected spatiotemporal gait parameters and could be effectively used in clinical or home settings.     

A target localization error analysis method for bistatic SAR based on gradient theory

Bistatic range sum (BRS) equation and Doppler equation of bistatic synthetic aperture radar (BiSAR) can be used to achieve target localization in BiSAR images. Because of the double square roots existing in BiSAR range history, it is hard to analyze the target localization error of a BiSAR system by calculating the partial derivatives directly. In this letter, a method utilizing spatial gradient vectors of BRS and Doppler frequency is proposed to derive the analytic solution of localization error for BiSAR. The effectiveness of the method is validated by a numerical simulation experiment.     

Spatiotemporal activity estimation for multivoxel pattern analysis with rapid event-related designs

Despite growing interest in multi-voxel pattern analysis (MVPA) methods for fMRI, a major problem remains--that of generating estimates in rapid event-related (ER) designs, where the BOLD responses of temporally adjacent events will overlap. While this problem has been investigated for methods that reduce each event to a single parameter per voxel (Mumford et al., 2012), most of these methods make strong parametric assumptions about the shape of the hemodynamic response, and require exact knowledge of the temporal profile of the underlying neural activity. A second class of methods uses multiple parameters per event (per voxel) to capture temporal information more faithfully. In addition to enabling a more accurate estimate of ER responses, this allows for the extension of the standard classification paradigm into the temporal domain (e.g., Mour?o-Miranda et al., 2007). However, existing methods in this class were developed for use with block and slow ER data, and there has not yet been an exploration of how to adapt such methods to data collected using rapid ER designs. Here, we demonstrate that the use of multiple parameters preserves or improves classification accuracy, while additionally providing information on the evolution of class discrimination. Additionally, we explore an alternative to the method of Mour?o-Miranda et al. tailored to use in rapid ER designs that yields equivalent classification accuracies, but is better at unmixing responses to temporally adjacent events. The current work paves the way for wider adoption of spatiotemporal classification analyses, and greater use of MVPA with rapid ER designs.     

HST-N302血液分析流水线检测结果一致性探讨

目的探讨实现HST—N302血液分析流水线检测结果一致性的方法。方法运用仪器配套校准品、配套质控品及新鲜全血对该流水线定期进行校准,并参照美国临床和实验室标准化研究所（CLSI）H26-P2文件要求定期进行比对。结果校准后流水线的2台仪器间及同一仪器不同吸样模式间比对结果差异值均在允许范围内。结论运用上述方法可实现HST—N302血液分析流水线检测结果在保证准确性前提下的一致性。