Evaluation of higher-order time-domain perturbation theory of photon diffusion on breast-equivalent phantoms and optical mammograms

Time-domain perturbation theory of photon diffusion up to third order was evaluated for its accuracy in deducing optical properties of breast tumors using simulated and physical phantoms and by analyzing 141 projection mammograms of 87 patients with histology-validated tumors that had been recorded by scanning time-domain optical mammography. The slightly compressed breast was modeled as (partially) homogeneous diffusely scattering infinite slab containing a scattering and absorbing spherical heterogeneity representing the tumor. Photon flux densities were calculated from densities of transmitted photons, assuming extended boundary conditions. Explicit formulas are provided for second-order changes in transmitted photon density due to the presence of absorbers or scatterers. The results on phantoms obtained by perturbation theory carried up to third order were compared with measured temporal point spread functions, with numerical finite-element method (FEM) simulations of transmitted photon flux density, with results obtained from the diffraction of diffuse photon density waves, and from Pade approximants. The breakdown of first-, second-, and third-order perturbation theory is discussed for absorbers and a general expression was derived for the convergence of the Born series in this case. Taking tumor optical properties derived by the diffraction model as reference we conclude that estimates of tumor absorption coefficients by perturbation theory agree with reference values within +/-25% in only 65% (first order), 66% (second order), and 77% (third order) of all mammograms analyzed. In the remaining cases tumor absorption is generally underestimated due to the breakdown of perturbation theory. On average the empirical Pade approximants yield tumor absorption coefficients similar to third-order perturbation theory, yet at noticeable lower computational efforts

GSplit LBI: Taming the Procedural Bias in Neuroimaging for Disease Prediction

In voxel-based neuroimage analysis, lesion features have been the main focus in disease prediction due to their interpretability with respect to the related diseases. However, we observe that there exists another type of features introduced during the preprocessing steps and we call them "\textbf{Procedural Bias}". Besides, such bias can be leveraged to improve classification accuracy. Nevertheless, most existing models suffer from either under-fit without considering procedural bias or poor interpretability without differentiating such bias from lesion ones. In this paper, a novel dual-task algorithm namely \emph{GSplit LBI} is proposed to resolve this problem. By introducing an augmented variable enforced to be structural sparsity with a variable splitting term, the estimators for prediction and selecting lesion features can be optimized separately and mutually monitored by each other following an iterative scheme. Empirical experiments have been evaluated on the Alzheimer's Disease Neuroimaging Initiative\thinspace(ADNI) database. The advantage of proposed model is verified by improved stability of selected lesion features and better classification results.Comment: Conditional Accepted by Miccai,201

Detailing renal hemodynamics and oxygenation in rats by a combined near-infrared spectroscopy and invasive probe approach

We hypothesize that combining quantitative near-infrared spectroscopy (NIRS) with established invasive techniques will enable advanced insights into renal hemodynamics and oxygenation in small animal models. We developed a NIRS technique to monitor absolute values of oxygenated and deoxygenated hemoglobin and of oxygen saturation of hemoglobin within the renal cortex of rats. This NIRS technique was combined with invasive methods to simultaneously record renal tissue oxygen tension and perfusion. The results of test procedures including occlusions of the aorta or the renal vein, hyperoxia, hypoxia, and hypercapnia demonstrated that the combined approach, by providing different but complementary information, enables a more comprehensive characterization of renal hemodynamics and oxygenation

Time-resolved diffusing wave spectroscopy applied to dynamic heterogeneity imaging

We report in this paper what is to our knowledge the first observation of a time-resolved diffusing wave spectroscopy signal recorded by transillumination through a thick turbid medium: the DWS signal is measured for a fixed photon transit time, which opens the possibility of improving the spatial resolution. This technique could find biomedical applications, especially in mammography.Comment: 9 pages, 4 figure

Image informatics strategies for deciphering neuronal network connectivity

Brain function relies on an intricate network of highly dynamic neuronal connections that rewires dramatically under the impulse of various external cues and pathological conditions. Among the neuronal structures that show morphologi- cal plasticity are neurites, synapses, dendritic spines and even nuclei. This structural remodelling is directly connected with functional changes such as intercellular com- munication and the associated calcium-bursting behaviour. In vitro cultured neu- ronal networks are valuable models for studying these morpho-functional changes. Owing to the automation and standardisation of both image acquisition and image analysis, it has become possible to extract statistically relevant readout from such networks. Here, we focus on the current state-of-the-art in image informatics that enables quantitative microscopic interrogation of neuronal networks. We describe the major correlates of neuronal connectivity and present workflows for analysing them. Finally, we provide an outlook on the challenges that remain to be addressed, and discuss how imaging algorithms can be extended beyond in vitro imaging studies

Breast imaging technology: Probing physiology and molecular function using optical imaging - applications to breast cancer

The present review addresses the capacity of optical imaging to resolve functional and molecular characteristics of breast cancer. We focus on recent developments in optical imaging that allow three-dimensional reconstruction of optical signatures in the human breast using diffuse optical tomography (DOT). These technologic advances allow the noninvasive, in vivo imaging and quantification of oxygenated and deoxygenated hemoglobin and of contrast agents that target the physiologic and molecular functions of tumors. Hence, malignancy differentiation can be based on a novel set of functional features that are complementary to current radiologic imaging methods. These features could enhance diagnostic accuracy, lower the current state-of-the-art detection limits, and play a vital role in therapeutic strategy and monitoring

Lazy Lasso for local regression

Locally weighted regression is a technique that predicts the response for new data items from their neighbors in the training data set, where closer data items are assigned higher weights in the prediction. However, the original method may suffer from overfitting and fail to select the relevant variables. In this paper we propose combining a regularization approach with locally weighted regression to achieve sparse models. Specifically, the lasso is a shrinkage and selection method for linear regression. We present an algorithm that embeds lasso in an iterative procedure that alternatively computes weights and performs lasso-wise regression. The algorithm is tested on three synthetic scenarios and two real data sets. Results show that the proposed method outperforms linear and local models for several kinds of scenario

Multi-laboratory efforts for the standardization of performance assessment of diffuse optics instruments - The BitMap Exercise

A multi-laboratory exercise, involving 29 diffuse optical instruments, aimed at performance assessment of diffuse optics instruments on standardized protocols is presented. The overarching methodology and future actions will also be discussed