Reconstructing 3-D skin surface motion for the DIET breast cancer screening system.

Digital image-based elasto-tomography (DIET) is a prototype system for breast cancer screening. A breast is imaged while being vibrated, and the observed surface motion is used to infer the internal stiffness of the breast, hence identifying tumors. This paper describes a computer vision system for accurately measuring 3-D surface motion.

Separate modal analysis for tumor detection with a digital image elasto tomography (DIET) breast cancer screening system.

Purpose: It is estimated that every year, 1 × 10(6) women are diagnosed with breast cancer, and more than 410,000 die annually worldwide. Digital Image Elasto Tomography (DIET) is a new noninvasive breast cancer screening modality that induces mechanical vibrations in the breast and images its surface motion with digital cameras to detect changes in stiffness. This research develops a new automated approach for diagnosing breast cancer using DIET based on a modal analysis model.

Silicone breast phantoms for elastographic imaging evaluation.

Purpose: Breast cancer is a major public health issue for women, and early detection significantly increases survival rate. Currently, there is increased research interest in elastographic soft-tissue imaging techniques based on the correlation between pathology and mechanical stiffness. Anthropomorphic breast phantoms are critical for ex vivo validation of emerging elastographic technologies.

Localization and detection of breast cancer tumors with digital image elasto-tomography.

Digital Image Elasto-Tomography (DIET) is a novel elastic contrast based breast imaging method using time-harmonic motion data obtained from a calibrated array of high resolution digital cameras scanning the tissue surface. The method is currently undergoing initial clinical testing and preliminary results in cases of malignant breast tumors are now available. The method has proved capable of detecting and localizing the stiff lesions within the heterogeneous tissue structure of the beast through the use of an evolution based optimization algorithm implemented in linear finite elements.

Clinical validation of the quick dynamic insulin sensitivity test.

The quick dynamic insulin sensitivity test (DISTq) can yield an insulin sensitivity result immediately after a 30-min clinical protocol. The test uses intravenous boluses of 10 g glucose and 1 U insulin at t = 1 and 11 min, respectively, and measures glucose levels in samples taken at t = 0, 10, 20, and 30 min. The low clinical cost of the protocol is enabled via robust model formulation and a series of population-derived relationships that estimate insulin pharmacokinetics as a function of insulin sensitivity (SI).

A spectrum of dynamic insulin sensitivity test protocols.

Background: Numerous tests have been developed to estimate insulin sensitivity (SI). However, most of the established tests are either too expensive for widespread application or do not yield reliable results. The dynamic insulin sensitivity and secretion test (DISST) uses assays of glucose, insulin, and C-peptide from nine samples to quantify SI and endogenous insulin secretion (UN) at a comparatively low cost.

The dynamic insulin sensitivity and secretion test--a novel measure of insulin sensitivity.

The objective was to validate the methodology for the dynamic insulin sensitivity and secretion test (DISST) and to demonstrate its potential in clinical and research settings. One hundred twenty-three men and women had routine clinical and biochemical measurements, an oral glucose tolerance test, and a DISST. For the DISST, participants were cannulated for blood sampling and bolus administration.

A graphical method for practical and informative identifiability analyses of physiological models: a case study of insulin kinetics and sensitivity.

Background: Derivative based a-priori structural identifiability analyses of mathematical models can offer valuable insight into the identifiability of model parameters. However, these analyses are only capable of a binary confirmation of the mathematical distinction of parameters and a positive outcome can begin to lose relevance when measurement error is introduced. This article presents an integral based method that allows the observation of the identifiability of models with two-parameters in the presence of assay error.

Design and clinical pilot testing of the model-based dynamic insulin sensitivity and secretion test (DISST).

Background: Insulin resistance is a significant risk factor in the pathogenesis of type 2 diabetes. This article presents pilot study results of the dynamic insulin sensitivity and secretion test (DISST), a high-resolution, low-intensity test to diagnose insulin sensitivity (IS) and characterize pancreatic insulin secretion in response to a (small) glucose challenge. This pilot study examines the effect of glucose and insulin dose on the DISST, and tests its repeatability.

In vitro evaluation of surface based non-invasive breast cancer screening with digital image based Elasto tomography (DIET).

Digital Image-based Elasto Tomography (DIET) is a non-invasive breast cancer screening modality that induces vibrations into a breast and images its surface motion with digital cameras. Disturbances in the motion are caused by areas of higher stiffness within the breast, potentially cancerous tumors. A concept is presented to detect the angular location of a tumor by analyzing the phase delay of the vibrations on the surface.

An image based vibration sensor for soft tissue modal analysis in a Digital Image Elasto Tomography (DIET) system.

Digital Image Elasto Tomography (DIET) is a non-invasive elastographic breast cancer screening technology, based on image-based measurement of surface vibrations induced on a breast by mechanical actuation. Knowledge of frequency response characteristics of a breast prior to imaging is critical to maximize the imaging signal and diagnostic capability of the system. A feasibility analysis for a non-invasive image based modal analysis system is presented that is able to robustly and rapidly identify resonant frequencies in soft tissue.

A three-compartment model of the C-peptide-insulin dynamic during the DIST test.

Dynamic insulin sensitivity (SI) tests often utilise model-based parameter estimation. This research analyses the impact of expanding the typically used two-compartment model of insulin and C-peptide kinetics to incorporate a hepatic third compartment. The proposed model requires only four C-peptide assays to simulate endogenous insulin production (uen), greatly reducing the cost and clinical burden.

Independent cohort cross-validation of the real-time DISTq estimation of insulin sensitivity.

Insulin sensitivity (SI) is useful in the diagnosis, screening and treatment of diabetes. However, most current tests cannot provide an accurate, immediate or real-time estimate. The DISTq method does not require insulin or C-peptide assays like most SI tests, thus enabling real-time, low-cost SI estimation.

DISTq: An Iterative Analysis of Glucose Data for Low-Cost, Real-Time and Accurate Estimation of Insulin Sensitivity.

Insulin sensitivity (SI) estimation has numerous uses in medical and clinical situations. However, highresolution tests that are useful for clinical diagnosis and monitoring are often too intensive, long and costly for regular use. Simpler tests that mitigate these issues are not accurate enough for many clinical diagnostic or monitoring scenarios.

A minimal C-peptide sampling method to capture peak and total prehepatic insulin secretion in model-based experimental insulin sensitivity studies.

Aims And Background: Model-based insulin sensitivity testing via the intravenous glucose tolerance test (IVGTT) or similar is clinically very intensive due to the need for frequent sampling to accurately capture the dynamics of insulin secretion and clearance. The goal of this study was to significantly reduce the number of samples required in intravenous glucose tolerance test protocols to accurately identify C-peptide and insulin secretion characteristics.

Methods: Frequently sampled IVGTT data from 12 subjects [5 normal glucose-tolerant (NGT) and 7 type 2 diabetes mellitus (T2DM)] were analyzed to calculate insulin and C-peptide secretion using a well-accepted C-peptide model.

The identification of insulin saturation effects during the dynamic insulin sensitivity test.

Background: Many insulin sensitivity (SI) tests identify a sensitivity metric that is proportional to the total available insulin and measured glucose disposal despite general acceptance that insulin action is saturable. Accounting for insulin action saturation may aid inter-participant and/or inter-test comparisons of insulin efficiency, and model-based glycaemic regulation.

Method: Eighteen subjects participated in 46 dynamic insulin sensitivity tests (DIST, low-dose 40-50 minute insulin-modified IVGTT).

A subcutaneous insulin pharmacokinetic model for computer simulation in a diabetes decision support role: validation and simulation.

Objective: The goal of this study was to validate a previously derived and identified physiological subcutaneous (SC) insulin absorption model for computer simulation in a clinical diabetes decision support role using published pharmacokinetic summary measures.

Methods: Validation was performed using maximal plasma insulin concentration (C(max)) and time to maximal concentration (t(max) pharmacokinetic summary measures. Values were either reported or estimated from 37 pharmacokinetic studies over six modeled insulin types.

A subcutaneous insulin pharmacokinetic model for computer simulation in a diabetes decision support role: model structure and parameter identification.

Objective: The goal of this study was to develop a unified physiological subcutaneous (SC) insulin absorption model for computer simulation in a clinical diabetes decision support role. The model must model the plasma insulin appearance of a wide range of current insulins, especially monomer insulin and insulin glargine, utilizing common chemical states and transport rates, where appropriate.

Methods: A compartmental model was developed with 13 patient-specific model parameters covering six diverse insulin types [rapid-acting, regular, neutral protamine Hagedorn (NPH), lente, ultralente, and glargine insulin].

Model-based insulin sensitivity as a sepsis diagnostic in critical care.

Background: Timely diagnosis and treatment of sepsis in critical care require significant clinical effort, experience, and resources. Insulin sensitivity is known to decrease with worsening condition and could thus be used to aid diagnosis. Some glycemic control protocols are able to identify insulin sensitivity in real time.

Development of a clinical type 1 diabetes metabolic system model and in silico simulation tool.

Objectives: The goal of this study was to develop a system model of type 1 diabetes for the purpose of in silico simulation for the prediction of long-term glycemic control outcomes.

Methods: The system model was created and identified on a physiological cohort of virtual type 1 diabetes patients (n = 40). Integral-based identification was used to develop (n = 40) insulin sensitivity profiles.

Implementation and evaluation of the SPRINT protocol for tight glycaemic control in critically ill patients: a clinical practice change.

Introduction: Stress-induced hyperglycaemia is prevalent in critical care. Control of blood glucose levels to within a 4.4 to 6.

Monte Carlo analysis of a new model-based method for insulin sensitivity testing.

Insulin resistance (IR), or low insulin sensitivity, is a major risk factor in the pathogenesis of type 2 diabetes and cardiovascular disease. A simple, high resolution assessment of IR would enable earlier diagnosis and more accurate monitoring of intervention effects. Current assessments are either too intensive for clinical settings (Euglycaemic Clamp, IVGTT) or have too low resolution (HOMA, fasting glucose/insulin).

Model-based insulin and nutrition administration for tight glycaemic control in critical care.

Objective: Present a new model-based tight glycaemic control approach using variable insulin and nutrition administration.

Background: Hyperglycaemia is prevalent in critical care. Current published protocols use insulin alone to reduce blood glucose levels, require significant added clinical effort, and provide highly variable results.

Clinical validation of a model-based glycaemic control design approach and comparison to other clinical protocols.

Hyperglycaemia is prevalent in critical care and tight control can reduce mortality from 9-43% depending on the level of control and the cohort. This research presents a table-based method that varies both insulin dose and nutritional input to achieve tight control. The system mimics a previously validated model-based system, but can be used for long term, large patient number clinical evaluation.