Inertial Measuring System to Evaluate Gait Parameters and Dynamic Alignments for Lower-Limb Amputation Subjects.

The study aims to construct an inertial measuring system for the application of amputee subjects wearing a prosthesis. A new computation scheme to process inertial data by installing seven wireless inertial sensors on the lower limbs was implemented and validated by comparing it with an optical motion capture system. We applied this system to amputees to verify its performance for gait analysis. The gait parameters are evaluated to objectively assess the amputees' prosthesis-wearing status. The Madgwick algorithm was used in the study to correct the angular velocity deviation using acceleration data and convert it to quaternion. Further, the zero-velocity update method was applied to reconstruct patients' walking trajectories. The combination of computed walking trajectory with pelvic and lower limb joint motion enables sketching the details of motion via a stickman that helps visualize and animate the walk and gait of a test subject. Five participants with above-knee (n = 2) and below-knee (n = 3) amputations were recruited for gait analysis. Kinematic parameters were evaluated during a walking test to assess joint alignment and overall gait characteristics. Our findings support the feasibility of employing simple algorithms to achieve accurate and precise joint angle estimation and gait parameters based on wireless inertial sensor data.

Evaluation of lumbar and hip movement characterization and muscle activities during gait in patients with knee osteoarthritis.

Patients with knee osteoarthritis (KOA) might have gait deviations, but few previous studies have discussed gait compensatory movements of the proximal and distal parts of muscle groups related to KOA. The study aimed to measure lumbar and hip movements during gait test and collect muscle activities of the lower extremities. Thirty-four participants with KOA and 28 healthy participants aged over 50 years were recruited for this study. Lumbar and hip motions during walking test were measured using inertial measurement units. Four muscle groups of the lower extremity (erector spinae, gluteus maximus, quadriceps muscle, and gastrocnemius) activities in gait were collected using surface EMGs. KOA patients used an 2.12∘anterior inclined lumbar spine (p = 0.007) and 22.94∘ flexed hip (p = 0.001) in gait compared to healthy participants. The KOA patients had a small hip movement range 30.19∘(p = 0.001) and a higher asymmetric stance time ratio 0.39 (p = 0.006). Patients with KOA showed decreased erector spinae and gluteus maximus muscle activation and increased activation of the quadriceps and gastrocnemius muscles during gait. In conclusion, patients with KOA used a hyperlordotic lumbar and hip flexed strategy, which overactivates distal extensor muscles through the whole gait and might cause overstress on the lower extremity joints.

Optimizing diffuse optical imaging for breast tissues with a dual-encoder neural network to preserve small structural information and fine features.

Purpose: Various laboratory sources have recently achieved progress in implementing deep learning models on biomedical optical imaging of soft biological tissues. The highly scattered nature of tissues at specific optical wavelengths results in poor spatial resolution. This opens up opportunities for diffuse optical imaging to improve the spatial resolution of obtained optical properties suffering from artifacts. This study aims to investigate a dual-encoder deep learning model for successfully detecting tumors in different phantoms w.r.t tumor size on diffuse optical imaging. Approach: Our proposed dual-encoder network extends U-net by adding a parallel branch of signal data to get information directly from the base source. This allows the trained network to localize the inclusions without degrading or merging with the background. The signals from the forward model and the images from the inverse problem are combined in a single decoder, filling the gap between existing direct processing and post-processing. Results: Absorption and reduced scattering coefficients are well reconstructed in both simulation and phantom test datasets. The proposed and implemented dual-encoder networks characterize better optical-property images than the signal-encoder and image-encoder networks, and the contrast-and-size detail resolution of the dual-encoder networks outperforms the other two approaches. From the measures of performance evaluation, the structural similarity and peak signal-to-noise ratio of the reconstructed images obtained by the dual-encoder networks remain the highest values. Conclusions: In this study, we synthesized the advantages of boundary data direct reconstruction, namely the extracted signals and iterative methods, from the obtained images into a unified network architecture.

Periodic-net: an end-to-end data driven framework for diffuse optical imaging of breast cancer from noisy boundary data.

Significance: The machine learning (ML) approach plays a critical role in assessing biomedical imaging processes especially optical imaging (OI) including segmentation, classification, and reconstruction, intending to achieve higher accuracy efficiently. Aim: This research aims to develop an end-to-end deep learning framework for diffuse optical imaging (DOI) with multiple datasets to detect breast cancer and reconstruct its optical properties in the early stages. Approach: The proposed Periodic-net is a nondestructive deep learning (DL) algorithm for the reconstruction and evaluation of inhomogeneities in an inverse model with high accuracy, while boundary measurements are calculated by solving a forward problem with sources/detectors arranged uniformly around a circular domain in various combinations, including 16 × 15 , 20 × 19 , and 36 × 35 boundary measurement setups. Results: The results of image reconstruction on numerical and phantom datasets demonstrate that the proposed network provides higher-quality images with a greater amount of small details, superior immunity to noise, and sharper edges with a reduction in image artifacts than other state-of-the-art competitors. Conclusions: The network is highly effective at the simultaneous reconstruction of optical properties, i.e., absorption and reduced scattering coefficients, by optimizing the imaging time without degrading inclusions localization and image quality.

Sensor-to-Image Based Neural Networks: A Reliable Reconstruction Method for Diffuse Optical Imaging of High-Scattering Media.

Imaging tasks today are being increasingly shifted toward deep learning-based solutions. Biomedical imaging problems are no exception toward this tendency. It is appealing to consider deep learning as an alternative to such a complex imaging task. Although research of deep learning-based solutions continues to thrive, challenges still remain that limits the availability of these solutions in clinical practice. Diffuse optical tomography is a particularly challenging field since the problem is both ill-posed and ill-conditioned. To get a reconstructed image, various regularization-based models and procedures have been developed in the last three decades. In this study, a sensor-to-image based neural network for diffuse optical imaging has been developed as an alternative to the existing Tikhonov regularization (TR) method. It also provides a different structure compared to previous neural network approaches. We focus on realizing a complete image reconstruction function approximation (from sensor to image) by combining multiple deep learning architectures known in imaging fields that gives more capability to learn than the fully connected neural networks (FCNN) and/or convolutional neural networks (CNN) architectures. We use the idea of transformation from sensor- to image-domain similarly with AUTOMAP, and use the concept of an encoder, which is to learn a compressed representation of the inputs. Further, a U-net with skip connections to extract features and obtain the contrast image, is proposed and implemented. We designed a branching-like structure of the network that fully supports the ring-scanning measurement system, which means it can deal with various types of experimental data. The output images are obtained by multiplying the contrast images with the background coefficients. Our network is capable of producing attainable performance in both simulation and experiment cases, and is proven to be reliable to reconstruct non-synthesized data. Its apparent superior performance was compared with the results of the TR method and FCNN models. The proposed and implemented model is feasible to localize the inclusions with various conditions. The strategy created in this paper can be a promising alternative solution for clinical breast tumor imaging applications.

In Vivo Validation of Diffuse Optical Imaging with a Dual-Direction Measuring Module of Parallel-Plate Architecture for Breast Tumor Detection.

We demonstrate a working prototype of an optical breast imaging system involving parallel-plate architecture and a dual-direction scanning scheme designed in combination with a mammography machine; this system was validated in a pilot study to demonstrate its application in imaging healthy and malignant breasts in a clinical environment. The components and modules of the self-developed imaging system are demonstrated and explained, including its measuring architecture, scanning mechanism, and system calibration, and the reconstruction algorithm is presented. Additionally, the evaluation of feature indices that succinctly demonstrate the corresponding transmission measurements may provide insight into the existence of malignant tissue. Moreover, five cases are presented including one subject without disease (a control measure), one benign case, one suspected case, one invasive ductal carcinoma, and one positive case without follow-up treatment. A region-of-interest analysis demonstrated significant differences in absorption between healthy and malignant breasts, revealing the average contrast between the abnormalities and background tissue to exceed 1.4. Except for ringing artifacts, the average scattering property of the structure densities was 0.65-0.85 mm-1.

Indocyanine Green-Camptothecin Co-Loaded Perfluorocarbon Double-Layer Nanocomposite: A Versatile Nanotheranostics for Photochemotherapy and FDOT Diagnosis of Breast Cancer.

Breast cancer remains the most frequently diagnosed cancer and is the leading cause of neoplastic disease burden for females worldwide, suggesting that effective therapeutic and/or diagnostic strategies are still urgently needed. In this study, a type of indocyanine green (ICG) and camptothecin (CPT) co-loaded perfluorocarbon double-layer nanocomposite named ICPNC was developed for detection and photochemotherapy of breast cancer. The ICPNCs were designed to be surface modifiable for on-demand cell targeting and can serve as contrast agents for fluorescence diffuse optical tomography (FDOT). Upon near infrared (NIR) irradiation, the ICPNCs can generate a significantly increased production of singlet oxygen compared to free ICG, and offer a comparable cytotoxicity with reduced chemo-drug dosage. Based on the results of animal study, we further demonstrated that the ICPNCs ([ICG]/[CPT] = 40-/7.5-µM) in association with 1-min NIR irradiation (808 nm, 6 W/cm2) can provide an exceptional anticancer effect to the MDA-MB-231 tumor-bearing mice whereby the tumor size was significantly reduced by 80% with neither organ damage nor systemic toxicity after a 21-day treatment. Given a number of aforementioned merits, we anticipate that the developed ICPNC is a versatile theranostic nanoagent which is highly promising to be used in the clinic.

Wavelength optimization using available laser diodes in spectral near-infrared optical tomography.

For employing optimized wavelengths, a near-infrared (NIR) tomographic imaging system with multiwavelengths in a continuous wave (CW) enables us to provide accurate information of chromophores. In this paper, we discuss wavelength optimization with a selection from commercial laser diodes. Through theoretical analysis, the residual norm (R) and the condition number (κ) represent the uniqueness of a matrix problem and the smooth singular-value distribution of each chromophore, respectively. The optimum wavelengths take place for large R and small κ. We considered a total of 38 wavelengths of laser diodes in the range of 633-980 nm commercially available to discover optimum sets for a broad range of chromophore combinations. In the 38 wavelengths, there exists 501,942 (C538), 2,760,681 (C638), and 12,620,256 (C738) combinations of five, six, and seven wavelength sets, respectively, for accurately estimating chromophores (HbO2, HbR, H2O, and lipids), water, lipids, and the scattering prefactor A. With the numerical calculation, the top 10 wavelength sets were selected based on the principle of large R and small κ. In the study, the chromophore concentration for young and elderly women are investigated; finally, choosing the laser diodes with a wavelength of 650, 690, 705, 730, 870/880, 915, and 937 nm is recommended either for young or elderly women to construct a spectral NIR tomographic imaging system in the CW domain. Simulated data were used to validate the claims.

Inertial Sensing Based Assessment Methods to Quantify the Effectiveness of Post-Stroke Rehabilitation.

In clinical settings, traditional stroke rehabilitation evaluation methods are subjectively scored by occupational therapists, and the assessment results vary individually. To address this issue, this study aims to develop a stroke rehabilitation assessment system by using inertial measurement units. The inertial signals from the upper extremities were acquired, from which three quantitative indicators were extracted to reflect rehabilitation performance during stroke patients' movement examination, i.e., shoulder flexion. Both healthy adults and stroke patients were recruited to correlate the proposed quantitative evaluation indices and traditional rehab assessment scales. Especially, as a unique feature of the study the weight for each of three evaluation indicators was estimated by the least squares method. The quantitative results demonstrate the proposed method accurately reflects patients' recovery from pre-rehabilitation, and confirm the feasibility of applying inertial signals to evaluate rehab performance through feature extraction. The implemented assessment scheme appears to have the potential to overcome some shortcomings of traditional assessment methods and indicates rehab performance correctly.

A noncontact resonance frequency detection technique for the assessment of the interfacial bone defect around a dental implant.

This study employed a noncontact resonance frequency (RF) detection technique that was developed by our group to evaluate the interfacial bone in in vitro implant-bone models. Based on our method, the implant-bone structure was excited by the acoustic energy of a loudspeaker, and its vibration response was acquired with a capacitance sensor. The spectral analysis was used to characterize the first RF value. Two types of in vitro defect models, Buccal-Lingual (BL) and Mesial-Distal (MD), were constructed for the verification. The measurements of the RF for a defect model clamped at four different heights (9, 10, 11, and 12 mm) were performed in two sensing directions (BL and MD). Moreover, each model was also analyzed using an Osstell Mentor. The obtained two parameters, RF and ISQ (Implant Stability Quotient), were statistically analyzed through one-way analysis of variance (ANOVA) and linear regression analysis for comparisons. The RF and the ISQ values obtained for all of the defect models at the four clamp heights decreased significantly (p < 0.05) with an increase in the severity of the defect. The two parameters for each imperfection increase significantly (p < 0.05) with an increase in the clamp height. Additionally, the RFs of all of the defect models are linearly correlated with their corresponding ISQs for the four clamp heights and the two measuring orientations. Therefore, our developed technique is feasible for the assessment of the postoperative healing around a dental implant.

Flexible near-infrared diffuse optical tomography with varied weighting functions of edge-preserving regularization.

In this paper, a flexible edge-preserving regularization algorithm based on the finite element method is proposed to reconstruct the optical-property images of near-infrared diffuse optical tomography. This regularization algorithm can easily incorporate with varied weighting functions, such as a generalized Lorentzian function, an exponential function, or a generalized total variation function. To evaluate the performance, results obtained from Tikhonov or edge-preserving regularization are compared with each other. As found, the edge-preserving regularization with the generalized Lorentzian function is more attractive than that with other functions for the estimation of absorption-coefficient images concerning functional tomographic images to discover functional information of tested phantoms/tissues.

Implementation of edge-preserving regularization for frequency-domain diffuse optical tomography.

In this study, we first propose the use of edge-preserving regularization in optimizing an ill-conditioned problem in the reconstruction procedure for diffuse optical tomography to prevent unwanted edge smoothing, which usually degrades the attributes of images for distinguishing tumors from background tissues when using Tikhonov regularization. In the edge-preserving regularization method presented here, a potential function with edge-preserving properties is introduced as a regularized term in an objective function. With the minimization of this proposed objective function, an iterative method to solve this optimization problem is presented in which half-quadratic regularization is introduced to simplify the minimization task. Both numerical and experimental data are employed to justify the proposed technique. The reconstruction results indicate that edge-preserving regularization provides a superior performance over Tikhonov regularization.

Rapid convergence to the inverse solution regularized with Lorentzian distributed function for near-infrared continuous wave diffuse optical tomography.

A promising method to achieve rapid convergence for image reconstruction is introduced for the continuous-wave near-infrared (NIR) diffuse optical tomography (DOT). Tomographic techniques are usually implemented off line and are time consuming to realize image reconstruction, especially for NIR DOT. Therefore, it is essential to both speed up reconstruction and achieve stable and convergent solutions. We propose an approach using a constraint based on a Lorentzian distributed function incorporated into Tikhonov regularization, thereby rapidly converging a stable solution. It is found in the study that using the proposed method with around five or six iterations leads to a stable solution. The result is compared to the primary method usually converging in approximately 25 iterations. Our algorithm rapidly converges to stable solution in the case of noisy (>20 dB) detected intensities.

Highly resolved diffuse optical tomography: a systematic approach using high-pass filtering for value-preserved images.

We attempt to develop a systematic scheme through adopting high-pass filtering (HPF) to well resolve value-preserved images such as medical images. Our approach is derived from the Poisson maximum a posteriori superresolution algorithm employing the HP filters, where four filters are considered such as two low-pass-filter-combination based filters, wavelet filter, and negative-oriented Laplacian HP filter. The proposed approach is incorporated into the procedure of finite-element-method (FEM)-based image reconstruction for diffuse optical tomography in the direct current domain, posterior to each iteration without altering the original FEM modeling. This approach is justified with various HPF for different cases that breast-like phantoms embedded with two or three inclusions that imitate tumors are employed to examine the resolution performances under certain extreme conditions. The proposed approach to enhancing image resolution is evaluated for all tested cases. A qualitative investigation of reconstruction performance for each case is presented. Following this, we define a set of measures on the quantitative evaluation for a range of resolutions including separation, size, contrast, and location, thereby providing a comparable evaluation to the visual quality. The most satisfactory result is obtained by using the wavelet HP filter, and it successfully justifies our proposed scheme.