An Optical Sensory System for Assessment of Residual Cancer Burden in Breast Cancer Patients Undergoing Neoadjuvant Chemotherapy.

Breast cancer patients undergoing neoadjuvant chemotherapy (NAC) require precise and accurate evaluation of treatment response. Residual cancer burden (RCB) is a prognostic tool widely used to estimate survival outcomes in breast cancer. In this study, we introduced a machine-learning-based optical biosensor called the Opti-scan probe to assess residual cancer burden in breast cancer patients undergoing NAC. The Opti-scan probe data were acquired from 15 patients (mean age: 61.8 years) before and after each cycle of NAC. Using regression analysis with k-fold cross-validation, we calculated the optical properties of healthy and unhealthy breast tissues. The ML predictive model was trained on the optical parameter values and breast cancer imaging features obtained from the Opti-scan probe data to calculate RCB values. The results show that the ML model achieved a high accuracy of 0.98 in predicting RCB number/class based on the changes in optical properties measured by the Opti-scan probe. These findings suggest that our ML-based Opti-scan probe has considerable potential as a valuable tool for the assessment of breast cancer response after NAC and to guide treatment decisions. Therefore, it could be a promising, non-invasive, and accurate method for monitoring breast cancer patient's response to NAC.

A Robust Angular Rate Sensor Utilizing 2:1 Auto-Parametric Resonance Excitation.

This paper presents a single-axis angular rate sensor that is robust to variations in its operating voltage and frequencies. The sensor is developed to overcome the shortcomings of conventional mode-matched Micromachined Vibratory Gyroscopes in open loop operations, namely narrow frequency bandwidths and unstable scale factors. The developed sensor utilizes inherent forcing and inertial nonlinearities from electrostatic forces and fabrication imperfections to auto-parametrically excite the sense mode via 2:1 auto-parametric resonance, which yields a broader bandwidth frequency response for the sensor's sense mode. The experimental results demonstrated −3 dB frequency bandwidth of 500 Hz, a scale factor of 50 µV/°/s, and a dynamic range of ±330°/s.

Deep orthogonal multi-wavelength fusion for tomogram-free diagnosis in diffuse optical imaging.

Novel portable diffuse optical tomography (DOT) devices for breast cancer lesions hold great promise for non-invasive, non-ionizing breast cancer screening. Critical to this capability is not just the identification of lesions but rather the complex problem of discriminating between malignant and benign lesions. To accurately reconstruct the highly heterogeneous tissue of a cancer lesion in healthy breast tissue using DOT, multiple wavelengths can be leveraged to maximize signal penetration while minimizing sensitivity to noise. However, these wavelength responses can overlap, capture common information, and correlate, potentially confounding reconstruction and downstream end tasks. We show that an orthogonal fusion loss regularizes multi-wavelength DOT leading to improved reconstruction and accuracy of end-to-end discrimination of malignant versus benign lesions. We further show that our raw-to-task model significantly reduces computational complexity without sacrificing accuracy, making it ideal for real-time throughput, desired in medical settings where handheld devices have severely restricted power budgets. Furthermore, our results indicate that image reconstruction is not necessary for unbiased classification of lesions with a balanced accuracy of 77% and 66% on the synthetic dataset and clinical dataset, respectively, using the raw-to-task model. Code is available at https://github.com/sfu-mial/FuseNet.

Multitask Deep Learning Reconstruction and Localization of Lesions in Limited Angle Diffuse Optical Tomography.

Diffuse optical tomography (DOT) leverages near-infrared light propagation through tissue to assess its optical properties and identify abnormalities. DOT image reconstruction is an ill-posed problem due to the highly scattered photons in the medium and the smaller number of measurements compared to the number of unknowns. Limited-angle DOT reduces probe complexity at the cost of increased reconstruction complexity. Reconstructions are thus commonly marred by artifacts and, as a result, it is difficult to obtain an accurate reconstruction of target objects, e.g., malignant lesions. Reconstruction does not always ensure good localization of small lesions. Furthermore, conventional optimization-based reconstruction methods are computationally expensive, rendering them too slow for real-time imaging applications. Our goal is to develop a fast and accurate image reconstruction method using deep learning, where multitask learning ensures accurate lesion localization in addition to improved reconstruction. We apply spatial-wise attention and a distance transform based loss function in a novel multitask learning formulation to improve localization and reconstruction compared to single-task optimized methods. Given the scarcity of real-world sensor-image pairs required for training supervised deep learning models, we leverage physics-based simulation to generate synthetic datasets and use a transfer learning module to align the sensor domain distribution between in silico and real-world data, while taking advantage of cross-domain learning. Applying our method, we find that we can reconstruct and localize lesions faithfully while allowing real-time reconstruction. We also demonstrate that the present algorithm can reconstruct multiple cancer lesions. The results demonstrate that multitask learning provides sharper and more accurate reconstruction.

Integration of a multi-camera vision system and strapdown inertial navigation system (SDINS) with a modified Kalman filter.

This paper describes the development of a modified Kalman filter to integrate a multi-camera vision system and strapdown inertial navigation system (SDINS) for tracking a hand-held moving device for slow or nearly static applications over extended periods of time. In this algorithm, the magnitude of the changes in position and velocity are estimated and then added to the previous estimation of the position and velocity, respectively. The experimental results of the hybrid vision/SDINS design show that the position error of the tool tip in all directions is about one millimeter RMS. The proposed Kalman filter removes the effect of the gravitational force in the state-space model. As a result, the resulting error is eliminated and the resulting position is smoother and ripple-free.

A modular impact diverting mechanism for football helmets.

To mitigate the injurious effect of the rotational acceleration of the brain, a modular Impact Diverting Mechanism (IDM) has been developed. The IDM can replace stickers (decals) that normally attach to the exterior of a football helmet. The IDM decals reduce friction and catch points between the covered area with the IDM on the outer shell of the helmet and the impacting surface, thereby decreasing rotational acceleration acting on the player's head. A Riddell Speed helmet's exterior was prepared with the IDM and outfitted to a headform equipped with linear accelerometers and gyroscopes. The helmets were tested at an impact velocity of 5.5 m/s at 15°, 30°, and 45° to the vertical: on the front, side, and back of the helmet. Results of 135 impact tests in the lab show that the IDM decal, when compared to helmets without it, reduced the rotational acceleration, rotational velocity, SI, HIC, and RIC ranging from 22% to 77%, 20% to 74%, 13% to 68%, 7% to 68%, 31% to 94%, respectively. Protection against rotational acceleration from oblique impacts is not prioritized in modern football helmets, as evident by current standard helmet testing protocols. This study demonstrates that the inclusion of the IDM decals in football helmets can help reduce the effects of rotational acceleration of the head during oblique impacts.

Translation of a portable diffuse optical breast scanner probe for clinical application: a preliminary study.

Most breast cancer lesions absorb higher levels of near-infrared (NIR) radiation compared to healthy breast tissue due to its increased vascularity. Oxy-hemoglobin (HbO2) and deoxy-hemoglobin (Hb) primarily found in cancerous vascular lesions, absorbs higher levels of radiation in the 650 nm to 850 nm wavelength range than the surrounding fatty tissue and water in the human breast. NIR diffuse optical spectroscopy (DOS) provides real-time functional and compositional information based on the optical properties of biological tissues, which cannot be accomplished by other portable breast imaging modalities. Here we present the first set of clinical trials using a non-invasive, hand-held diffuse optical breast scanner (DOB-Scan probe3) to capture in vivo cross-sectional images of the breast. The scanner uses four NIR illuminating sources with different wavelengths, 690 nm, 750 nm, 800 nm, and 850 nm, to determine the concentrations of the four main constituents of breast tissue, oxy-hemoglobin (HbO2), deoxy-hemoglobin (Hb), water (H2O), and fat. In this paper, we briefly explain the hardware design and image reconstruction algorithm of the DOB-Scan probe, the data collection process, and the imaging results of four different participants, selected from twenty, all who are diagnosed with breast cancer. For each patient, images were scanned from two locations, the first over the cancerous lesion and the second over the same region on the contralateral healthy breast, as a means of establishing controls for comparison. During each scan, four cross-sectional images of the breast, corresponding to four different NIR wavelengths, are reconstructed and displayed on a user interface for reference. Clinical results confirm that the absorption coefficients of cancerous lesions are significantly higher than the normal surrounding tissue. We propose to deploy the probe to effectively identify cancerous breast tissue at an early stage in a primary care setting, which could increase the efficiency of screening programs.

A Nonlinear Rate Microsensor utilising Internal Resonance.

Micro- and nano-resonators have been studied extensively both for the scientific viewpoint to understand basic interactions at small scales as well as for applied research to build sensors and mechanical signal processors. Majority of the resonant microsystems, particularly those manufactured at a large scale, have employed simple mechanical structures with one dominant resonant mode, such as in timing resonators, or linearly coupled resonant modes, as in vibratory gyroscopes. There is an increasing interest in the development of models and methods to better understand the nonlinear interactions at micro- and nano-scales and also to potentially improve the performance of the existing devices in the market beyond limits permissible by the linear effects. Internal resonance is a phenomenon that allows for nonlinear coupling and energy transfer between different vibration modes of a properly designed system. Herein, for the first time, we describe and experimentally demonstrate the potential for employing internal resonance for detection of angular rate signals, where the Coriolis effect modifies the energy coupling between the distinct drive and sense vibration modes. In doing so, in addition to providing a robust method of exciting the desired mode, the proposed approach further alleviates the mode-matching requirements and reduces instabilities due to the cross-coupling between the modes in current linear vibratory gyroscopes.

Utilization of 2:1 Internal Resonance in Microsystems.

In this paper, the nonlinear mode coupling at 2:1 internal resonance has been studied both analytically and experimentally. A modified micro T-beam structure is proposed, and the equations of motion are developed using Lagrange's energy method. A two-variable expansion perturbation method is used to describe the nonlinear behavior of the system. It is shown that in a microresonator with 2:1 internal resonance, the low-frequency mode is autoparametrically excited after the excitation amplitude reaches a certain threshold. The effect of damping on the performance of the system is also investigated.

Cancer Detection Based on Electrical Impedance Spectroscopy: A Clinical Study.

An electrical Impedance based tool is designed and developed to aid physicians performing clinical exams focusing on cancer detection. Current research envisions improvement in sensor-based measurement technology to differentiate malignant and benign lesions in human subjects. The tool differentiates malignant anomalies from nonmalignant anomalies using Electrical Impedance Spectroscopy (EIS). This method exploits cancerous tissue behavior by using EIS technique to aid early detection of cancerous tissue. The correlation between tissue electrical properties and tissue pathologies is identified by offering an analysis technique based on the Cole model. Additional classification and decision-making algorithm is further developed for cancer detection. This research suggests that the sensitivity of tumor detection will increase when supplementary information from EIS and built-in intelligence are provided to the physician.

Factors influencing the oblique impact test of motorcycle helmets.

PURPOSE: Oblique impact tests can provide important information regarding the level of protection of a helmet. Two factors that influence the results of oblique impact tests on motorcycle helmets are discussed in this work. The first factor is the angle of the anvil on which the helmet impacts. The second one is the friction between the headform and the helmet's interior. METHODS: To study the first factor, 2 anvil angles are provided, one 30° and the other one 15° to the vertical. To analyze the second factor, we consider 2 types of headform surfaces: the original metal surface of the standard headform and the same headform covered uniformly with a layer of silicone rubber that is 1 mm thick. RESULTS: The results show that varying the anvil's angle and surface friction can directly affect the linear and rotational acceleration of the headform. CONCLUSION: Testing helmets for different oblique impact angles can help assess their protection capability. The coefficient of friction between the helmet's interior and the headform plays an important role in the headform's rotational acceleration during an impact. Using a standard surface friction for headform similar or close to that of the human scalp can ensure that the results of the oblique impact tests are more consistent and realistic.