Usability Evaluation of a Noninvasive Neutropenia Screening Device (PointCheck) for Patients Undergoing Cancer Chemotherapy: Mixed Methods Observational Study.

Background: Patients with cancer undergoing cytotoxic chemotherapy face an elevated risk of developing serious infection as a consequence of their treatment, which lowers their white blood cell count and, more specifically, their absolute neutrophil count. This condition is known as neutropenia. Neutropenia accompanied by a fever is referred to as febrile neutropenia, a common side effect of chemotherapy with a high mortality rate.

Diffraction-unlimited imaging based on conventional optical devices.

We propose a computational paradigm where off-the-shelf optical devices can be used to image objects in a scene well beyond their native optical resolution. By design, our approach is generic, does not require active illumination, and is applicable to several types of optical devices. It only requires the placement of a spatial light modulator some distance from the optical system.

Visualization of blood cell contrast in nailfold capillaries with high-speed reverse lens mobile phone microscopy.

Quantification of optical absorption gaps in nailfold capillaries has recently shown promise as a non-invasive technique for neutropenia screening. Here we demonstrate a low-cost, portable attachment to a mobile phone that can resolve optical absorption gaps in nailfold capillaries using a reverse lens technique and oblique 520nm illumination. Resolution <4m within a 1mm on-axis region is demonstrated, and wide field of view (3.

Non-invasive detection of severe neutropenia in chemotherapy patients by optical imaging of nailfold microcirculation.

White-blood-cell (WBC) assessment is employed for innumerable clinical procedures as one indicator of immune status. Currently, WBC determinations are obtained by clinical laboratory analysis of whole blood samples. Both the extraction of blood and its analysis limit the accessibility and frequency of the measurement.

Compressed imaging by sparse random convolution.

The theory of compressed sensing (CS) shows that signals can be acquired at sub-Nyquist rates if they are sufficiently sparse or compressible. Since many images bear this property, several acquisition models have been proposed for optical CS. An interesting approach is random convolution (RC).

Analysis of white blood cell dynamics in nailfold capillaries.

Based on video data acquired with low-cost, portable microscopy equipment, we introduce a semi-automatic method to count visual gaps in the blood flow as a proxy for white blood cells (WBC) passing through nailfold capillaries. Following minimal user interaction and a pre-processing stage, our method consists in the spatio-temporal segmentation and analysis of capillary profiles. Besides the mere count information, it also estimates the speed associated with every WBC event.

Adaptive image resizing based on continuous-domain stochastic modeling.

We introduce an adaptive continuous-domain modeling approach to texture and natural images. The continuous-domain image is assumed to be a smooth function, and we embed it in a parameterized Sobolev space. We point out a link between Sobolev spaces and stochastic auto-regressive models, and exploit it for optimally choosing Sobolev parameters from available pixel values.

A practical inverse-problem approach to digital holographic reconstruction.

In this paper, we propose a new technique for high-quality reconstruction from single digital holographic acquisitions. The unknown complex object field is found as the solution of a nonlinear inverse problem that consists in the minimization of an energy functional. The latter includes total-variation (TV) regularization terms that constrain the spatial amplitude and phase distributions of the reconstructed data.

Binary compressed imaging.

Compressed sensing can substantially reduce the number of samples required for conventional signal acquisition at the expense of an additional reconstruction procedure. It also provides robust reconstruction when using quantized measurements, including in the one-bit setting. In this paper, our goal is to design a framework for binary compressed sensing that is adapted to images.

Anisotropic interpolation of sparse generalized image samples.

Practical image-acquisition systems are often modeled as a continuous-domain prefilter followed by an ideal sampler, where generalized samples are obtained after convolution with the impulse response of the device. In this paper, our goal is to interpolate images from a given subset of such samples. We express our solution in the continuous domain, considering consistent resampling as a data-fidelity constraint.

Hessian-based norm regularization for image restoration with biomedical applications.

We present nonquadratic Hessian-based regularization methods that can be effectively used for image restoration problems in a variational framework. Motivated by the great success of the total-variation (TV) functional, we extend it to also include second-order differential operators. Specifically, we derive second-order regularizers that involve matrix norms of the Hessian operator.

Optical imaging using binary sensors.

This paper addresses the problem of reconstructing an image from 1-bit-quantized measurements, considering a simple but nonconventional optical acquisition model. Following a compressed-sensing design, a known pseudo-random phase-shifting mask is introduced at the aperture of the optical system. The associated reconstruction algorithm is tailored to this mask.