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SummaryA significant challenge in the field of biomedicine is the development of methods to integrate the multitude of dispersed data sets into comprehensive frameworks to be used to generate optimal clinical decisions. Recent technological advances in single cell analysis allow for high-dimensional molecular characterization of cells and populations, but to date, few mathematical models have attempted to integrate measurements from the single cell scale with other data types. Here, we present a framework that actionizes static outputs from a machine learning model and leverages these as measurements of state variables in a dynamic mechanistic model of treatment response. We apply this framework to breast cancer cells to integrate single cell transcriptomic data with longitudinal population-size data. We demonstrate that the explicit inclusion of the transcriptomic information in the parameter estimation is critical for identification of the model parameters and enables accurate prediction of new treatment regimens. Inclusion of the transcriptomic data improves predictive accuracy in new treatment response dynamics with a concordance correlation coefficient (CCC) of 0.89 compared to a prediction accuracy of CCC = 0.79 without integration of the single cell RNA sequencing (scRNA-seq) data directly into the model calibration. To the best our knowledge, this is the first work that explicitly integrates single cell clonally-resolved transcriptome datasets with longitudinal treatment response data into a mechanistic mathematical model of drug resistance dynamics. We anticipate this approach to be a first step that demonstrates the feasibility of incorporating multimodal data sets into identifiable mathematical models to develop optimized treatment regimens from data.

The performance of any scene-adaptive Nonuniformity Correction (NUC) algorithm is fundamentally limited by the quality of the scene-based predicted value of each pixel. TARID-based composite imagery can serve as a scene-based pixel predictor with improved robustness, and reduced noise than that of more common scene-based pixel predictors. These improved properties result in dramatically faster algorithm convergence, generating corrected imagery with reduced spatial noise due to intrinsic nonuniform or inoperative pixels in a Focal Plane Array.

A general method is described to improve the operational resolution of an Electro-Optic (EO) imaging sensor using multiple frames of an image sequence. This method only assumes the constituent video has some ambient motion between the sensor and stationary background, and the optical image is electronically captured and digitally recorded by a staring focal plane detector array. Compared to alternative techniques that may require externally controlled or measured dither motion, this approach offers significantly enhanced operational resolution with substantially relaxed constraints on sensor stabilization, optics, and exposure time.

This paper outlines a generalized image reconstruction approach to improve the resolution of an Electro-Optic (EO) imaging sensor using multiple frames of an image sequence. This method only assumes the constituent video has some ambient motion between the sensor and stationary background, and the optical image is physically captured by a staring focal plane array.

Many imaging systems consist of a combination of distinct electro-optic sensors that are constrained to view the same scene through a common aperture or from a common platform. Oftentimes, a spatial registration of one sensor's image is required to conform to the slightly disparate imaging geometry of a different sensor on the same platform. This is generally achieved through a judicious selection of image tie-points and geometric transformation model. This paper outlines a procedure to improve any such registration technique by leveraging the temporal motion within a pair of video sequences and requiring an additional constraint of minimizing the disparity in optical flow between registered video sequences.

Current target acquisition models are for monochrome imagery systems (single detector). The increasing interest in multispectral infrared systems and color daylight imagers highlights the need for models that describe the target acquisition process for color systems (2 or more detectors). This study investigates the detection of simple color targets in a noise color background.© (2001) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

This paper outlines a generalized image reconstruction approach that improves the resolution of an Electro-Optic (EO) imaging sensor based on multiple frame exposures during a temporal window of video. Such an approach is innovative in that it does not depend on controlled micro dithering of the camera, nor require the set of exposures to maintain any strictly defined transformation. It suffices to assume such video is physically captured by a focal plane array, and loosely requires some relative motion between sensor and subject.© (2001) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

A multi-spectral imaging system can be defined as a combination of electro-optic imagers that are mechanically constrained to view the same scene. Subsequent processing of the output imagery invariably requires a spatial registration of one spectral band image to geometrically conform to the imagery from a different sensor. This paper outlines a procedure to leverage motion estimation of a pair of video sequences to determine a transformation that minimizes the disparity in optical flow between the sequences.

Sensor fusion of up to three disparate imagers can readily be achieved by assigning each component video stream to a separate channel any standard RBG color monitor such as with television or personal computer systems. Provided the component imagery is pixel registered, such a straightforward systems can provide improved object-background separation, yielding quantifiable human-factors performance improvement compared to viewing monochrome imagery of a single sensor. Consideration is given to appropriate dynamic range management of the available color gamut, and appropriate color saturation in the presence of imager noise.

The emergence of new infrared sensor technologies and the availability of powerful, inexpensive computers have made many new imaging applications possible. Researchers working in the area of traditional image processing are showing an increased interest in working with infrared imagery. However, because of the inherent differences between infrared and visible phenomenology, a number of fundamental problems arise when trying to apply traditional processing methods to the infrared. Furthermore, the technologies required to image in the infrared are currently much less mature than comparable camera technologies used in visible imaging. Also infrared sensors need to capture six to eight additional bits of additional dynamic range over the eight normally used for visible imaging. Over the past half-century, visible cameras have become highly developed and can deliver images that meet engineering standards compatible with image displays. Similar image standards are often not possible in the infrared for a number of technical and phenomenological reasons. The purpose of this paper is to describe some of these differences and discuss a related topic known as image preprocessing. This is an area of processing that roughly lies between traditional image processing and image generation; because the camera images are less than ideal, additional processing is needed to perform necessary functions such as dynamic range management, non-uniformity correction, resolution enhancement, or color processing. A long-range goal for the implementation of these algorithms is to move them on-chip as analog retina-like or cortical-like circuits, thus achieving extraordinary reductions in power dissipation, size, and cost. Because this area of research is relatively new and still evolving, the discussion in this paper is limited to only a partial overview of the topic.