Abstract. Diffuse optical tomography has shown promising results as a tool for breast cancer screening and monitoring response to chemotherapy. Dynamic imaging of the transient response of the breast to an external stimulus, such as pressure or a respiratory maneuver, can provide additional information that can be used to detect tumors. We present a new digital continuous-wave optical tomography system designed to simultaneously image both breasts at fast frame rates and with a large number of sources and detectors. The system uses a master-slave digital signal processor-based detection architecture to achieve a dynamic range of 160 dB and a frame rate of 1.7 Hz with 32 sources, 64 detectors, and 4 wavelengths per breast. Included is a preliminary study of one healthy patient and two breast cancer patients showing the ability to identify an invasive carcinoma based on the hemodynamic response to a breath hold. C 2011 Society of Photo-Optical Instrumentation Engineers (SPIE).

Imaging studies of the breast comprise three principal sensing domains: structural, mechanical, and functional. Combinations of these domains can yield either additive or wholly new information, depending on whether one domain interacts with the other. In this report, we describe a new approach to breast imaging based on the interaction between controlled applied mechanical force and tissue hemodynamics. Presented is a description of the system design, performance characteristics, and representative clinical findings for a second-generation dynamic near-infrared optical tomographic breast imager that examines both breasts simultaneously, under conditions of rest and controlled mechanical provocation. The expected capabilities and limitations of the developed system are described in relation to the various sensing domains for breast imaging.

Purpose: The work presented here demonstrates an application of diffuse optical tomography (DOT) to the problem of breast-cancer diagnosis. The potential for using spatial and temporal variability measures of the hemoglobin signal to identify useful biomarkers was studied. Methods: DOT imaging data were collected using two instrumentation platforms the authors developed, which were suitable for exploring tissue dynamics while performing a simultaneous bilateral exam. For each component of the hemoglobin signal (e.g., total, oxygenated), the image time series was reduced to eight scalar metrics that were affected by one or more dynamic properties of the breast microvasculature (e.g., average amplitude, amplitude heterogeneity, strength of spatial coordination). Receiver-operator characteristic (ROC) analyses, comparing groups of subjects with breast cancer to various control groups (i.e., all noncancer subjects, only those with diagnosed benign breast pathology, and only those with no known breast pathology), were performed to evaluate the effect of cancer on the magnitudes of the metrics and of their interbreast differences and ratios. Results: For women with known breast cancer, simultaneous bilateral DOT breast measures reveal a marked increase in the resting-state amplitude of the vasomotor response in the hemoglobin signal for the affected breast, compared to the contralateral, noncancer breast. Reconstructed 3D spatial maps of observed dynamics also show that this behavior extends well beyond the tumor border. In an effort to identify biomarkers that have the potential to support clinical aims, a group of scalar quantities extracted from the time series measures was systematically examined. This analysis showed that many of the quantities obtained by computing paired responses from the bilateral scans (e.g., interbreast differences, ratios) reveal statistically significant differences between the cancer-positive and -negative subject groups, while the corresponding measures derived from individual breast scans do not. ROC analyses yield area-under-curve values in the 77%-87% range, depending on the metric, with sensitivity and specificity values ranging from 66% to 91%. An interesting result is the initially unexpected finding that the hemodynamic-image metrics are only weakly dependent on the tumor burden, implying that the DOT technique employed is sensitive to tumor-induced changes in the vascular dynamics of the surrounding breast tissue as well. Computational modeling studies serve to identify which properties of the vasomotor response (e.g., average amplitude, amplitude heterogeneity, and phase heterogeneity) principally determine the values of the metrics and their codependences. Findings from the modeling studies also serve to clarify the influence of spatial-response heterogeneity and of system-design limitations, and they reveal the impact that a complex dependence of metric values on the modeled behaviors has on the success in distinguishing between cancer-positive and -negative subjects. Conclus...

Cervical cancer, a common chronic disease, is one of the most prevalent and curable cancers among women. Pap smear images are a popular technique for screening cervical cancer. This study proposes a computer-aided diagnosis for cervical cancer utilising the novel Cervical Net deep learning (DL) structures and feature fusion with Shuffle Net structural features. Image acquisition and enhancement, feature extraction and selection, as well as classification are the main steps in our cervical cancer screening system. Automated features are extracted using pre-trained convolutional neural networks (CNN) fused with a novel Cervical Net structure in which 544 resultant features are obtained. To minimise dimensionality and select the most important features, principal component analysis (PCA) is used as well as canonical correlation analysis (CCA) to obtain the best discriminant features for five classes of Pap smear images. Here, five different machine learning (ML) algorithms are fed into these features. The proposed strategy achieved the best accuracy ever obtained using a support vector machine (SVM), in which in which fused features between Cervical Net and Shuffle Net is 99.1% for all classes.

For much of the past decade, we have developed most of the essential hardware and software components needed for practical implementation of dynamic NIRS imaging. Until recently, however, these efforts have been hampered by the lack of calibrating phantoms whose dynamics substantially mimic those seen in tissue. Here we present findings that document the performance of a dynamic phantom based on use of twisted nematic liquid crystal (LC) technology. Programmable time courses of applied voltage cause the opacity of the LC devices, which are embedded in a background matrix consisting of polysiloxane (silicone) admixed with scattering and absorbing materials, to vary in a manner that mimics the spatiotemporal hemodynamic pattern of interest. Methods for producing phantoms with selected absorption and scattering, internal heterogeneity, external geometry, hardness, and number and locations of embedded LCs are described. Also described is a method for overcoming the apparent limitation that arises from LCs being mainly independent of the illumination wavelength. The results presented demonstrate that: the opacity vs. voltage response of LCs are highly stable and repeatable; the dynamic phantom can be driven at physiologically relevant speeds, and will produce time-varying absorption that follows the programmed behavior with high fidelity; image time series recovered from measurements on the phantom have high temporal and spatial location accuracy. Thus the dynamic phantom can fill the need for test media that practitioners may use to confirm the accuracy of computed imaging results, assure the correct operation of imaging hardware, and compare performance of different data analysis algorithms.