Guido H. Jajamovich

Joint Detection and Estimation: Optimum Tests and Applications

We consider a well-defined joint detection and parameter estimation problem. By combining the Bayesian formulation of the estimation subproblem with suitable constraints on the detection subproblem, we develop optimum one- and two-step test for the joint detection/estimation setup. The proposed combined strategies have the very desirable characteristic to allow for the trade-off between detection power and estimation quality. Our theoretical developments are, then, applied to the problems of retrospective changepoint detection and multiple-input multiple-output (MIMO) radar. In the former case, we are interested in detecting a change in the statistics of a set of available data and provide an estimate for the time of change, while in the latter in detecting a target and estimating its location. Intense simulations in the MIMO radar example demonstrate that by using jointly optimum schemes, we can experience significant improvement in estimation quality, as compared to generalized the likelihood ratio test or the test that treats the two subproblems separately, with only small sacrifices in detection power.

Optimal Joint Target Detection and Parameter Estimation by MIMO Radar

We consider multiple-input multiple-output (MIMO) radar systems with widely spaced antennas. Such antenna configuration facilitates capturing the inherent diversity gain due to independent signal dispersion by the target scatterers. We consider a new MIMO radar framework for detecting a target that lies in an unknown location. This is in contrast with conventional MIMO radars which break the space into small cells and aim at detecting the presence of a target in a specified cell. We treat this problem through offering a novel composite hypothesis testing framework for target detection when 1) one or more parameters of the target are unknown and we are interested in estimating them, and 2) only a finite number of observations are available. The test offered optimizes a metric which accounts for both detection and estimation accuracies. In this paper, as the parameter of interest we focus on the vector of time-delays that the waveforms undergo from being emitted by the transmit antennas until being observed by the receive antennas. The analytical and empirical results establish that for the proposed joint target detection and time-delay estimation framework, MIMO radars exhibit significant gains over phased-array radars for extended targets which consist of multiple independent scatterers. For point targets modeled as single scatterers, however, the detection/estimation accuracies of MIMO and phased-array radars for this specific setup (joint target detection and time-delay estimation) are comparable.

Quantitative Liver MRI Combining Phase Contrast Imaging, Elastography, and DWI: Assessment of Reproducibility and Postprandial Effect at 3.0 T

Purpose To quantify short-term reproducibility (in fasting conditions) and postprandial changes after a meal in portal vein (PV) flow parameters measured with phase contrast (PC) imaging, liver diffusion parameters measured with multiple b value diffusion-weighted imaging (DWI) and liver stiffness (LS) measured with MR elastography (MRE) in healthy volunteers and patients with liver disease at 3.0 T. Materials and Methods In this IRB–approved prospective study, 30 subjects (11 healthy volunteers and 19 liver disease patients; 23 males, 7 females; mean age 46.5 y) were enrolled. Imaging included 2D PC imaging, multiple b value DWI and MRE. Subjects were initially scanned twice in fasting state to assess short-term parameter reproducibility, and then scanned 20 min. after a liquid meal. PV flow/velocity, LS, liver true diffusion coefficient (D), pseudodiffusion coefficient (D*), perfusion fraction (PF) and apparent diffusion coefficient (ADC) were measured in fasting and postprandial conditions. Short-term reproducibility was assessed in fasting conditions by measuring coefficients of variation (CV) and Bland-Altman limits of agreement. Differences in MR metrics before and after caloric intake and between healthy volunteers and liver disease patients were assessed. Results PV flow parameters, D, ADC and LS showed good to excellent short-term reproducibility in fasting state (CV <16%), while PF and D* showed acceptable and poor reproducibility (CV = 20.4% and 51.6%, respectively). PV flow parameters and LS were significantly higher (p<0.04) in postprandial state while liver diffusion parameters showed no significant change (p>0.2). LS was significantly higher in liver disease patients compared to healthy volunteers both in fasting and postprandial conditions (p<0.001). Changes in LS were significantly correlated with changes in PV flow (Spearman rho = 0.48, p = 0.013). Conclusions Caloric intake had no/minimal/large impact on diffusion/stiffness/portal vein flow, respectively. PC MRI and MRE but not DWI should be performed in controlled fasting state.

Abdominal 4D Flow MR Imaging in a Breath Hold: Combination of Spiral Sampling and Dynamic Compressed Sensing for Highly Accelerated Acquisition

PURPOSE To develop a highly accelerated phase-contrast cardiac-gated volume flow measurement (four-dimensional [4D] flow) magnetic resonance (MR) imaging technique based on spiral sampling and dynamic compressed sensing and to compare this technique with established phase-contrast imaging techniques for the quantification of blood flow in abdominal vessels. MATERIALS AND METHODS This single-center prospective study was compliant with HIPAA and approved by the institutional review board. Ten subjects (nine men, one woman; mean age, 51 years; age range, 30-70 years) were enrolled. Seven patients had liver disease. Written informed consent was obtained from all participants. Two 4D flow acquisitions were performed in each subject, one with use of Cartesian sampling with respiratory tracking and the other with use of spiral sampling and a breath hold. Cartesian two-dimensional (2D) cine phase-contrast images were also acquired in the portal vein. Two observers independently assessed vessel conspicuity on phase-contrast three-dimensional angiograms. Quantitative flow parameters were measured by two independent observers in major abdominal vessels. Intertechnique concordance was quantified by using Bland-Altman and logistic regression analyses. RESULTS There was moderate to substantial agreement in vessel conspicuity between 4D flow acquisitions in arteries and veins (κ = 0.71 and 0.61, respectively, for observer 1; κ = 0.71 and 0.44 for observer 2), whereas more artifacts were observed with spiral 4D flow (κ = 0.30 and 0.20). Quantitative measurements in abdominal vessels showed good equivalence between spiral and Cartesian 4D flow techniques (lower bound of the 95% confidence interval: 63%, 77%, 60%, and 64% for flow, area, average velocity, and peak velocity, respectively). For portal venous flow, spiral 4D flow was in better agreement with 2D cine phase-contrast flow (95% limits of agreement: -8.8 and 9.3 mL/sec, respectively) than was Cartesian 4D flow (95% limits of agreement: -10.6 and 14.6 mL/sec). CONCLUSION The combination of highly efficient spiral sampling with dynamic compressed sensing results in major acceleration for 4D flow MR imaging, which allows comprehensive assessment of abdominal vessel hemodynamics in a single breath hold.

Intravoxel Incoherent Motion Diffusion Imaging of the Liver: Optimal b-value Subsampling and Impact on Parameter Precision and Reproducibility

PURPOSE To increase diffusion sampling efficiency in intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) of the liver by reducing the number of diffusion weightings (b-values). MATERIALS AND METHODS In this IRB approved HIPAA compliant prospective study, 53 subjects (M/F 38/15, mean age 52 ± 13 y) underwent IVIM DWI at 1.5T using 16 b-values (0-800s/mm(2)), with 14 subjects having repeat exams to assess IVIM parameter reproducibility. A biexponential diffusion model was used to quantify IVIM hepatic parameters (PF: perfusion fraction, D: true diffusion and D*: pseudo diffusion). All possible subsets of the 16 b-values were probed, with number of b values ranging from 4 to 15, and corresponding parameters were quantified for each subset. For each b-value subset, global parameter estimation error was computed against the parameters obtained with all 16 b-values and the subsets providing the lowest error were selected. Interscan estimation error was also evaluated between repeat exams to assess reproducibility of the IVIM technique in the liver. The optimal b-values distribution was selected such that the number of b-values was minimal while keeping parameter estimation error below interscan reproducibility error. RESULTS As the number of b-values decreased, the estimation error increased for all parameters, reflecting decreased precision of IVIM metrics. Using an optimal set of 4 b-values (0, 15, 150 and 800s/mm(2)), the errors were 6.5, 22.8 and 66.1% for D, PF and D* respectively. These values lie within the range of test-retest reproducibility for the corresponding parameters, with errors of 12.0, 32.3 and 193.8% for D, PF and D* respectively. CONCLUSION A set of 4 optimized b-values can be used to estimate IVIM parameters in the liver with significantly shorter acquisition time (up to 75%), without substantial degradation of IVIM parameter precision and reproducibility compared to the 16 b-value acquisition used as the reference.

Demonstration of nonlinearity bias in the measurement of the apparent diffusion coefficient in multicenter trials.

Characterize system‐specific bias across common magnetic resonance imaging (MRI) platforms for quantitative diffusion measurements in multicenter trials.

The Impact of Arterial Input Function Determination Variations on Prostate Dynamic Contrast-Enhanced Magnetic Resonance Imaging Pharmacokinetic Modeling: A Multicenter Data Analysis Challenge

Pharmacokinetic analysis of dynamic contrast-enhanced (DCE) MRI data allows estimation of quantitative imaging biomarkers such as Ktrans (rate constant for plasma/interstitium contrast reagent (CR) transfer) and ve (extravascular and extracellular volume fraction). However, the use of quantitative DCE-MRI in clinical practice is limited with uncertainty in arterial input function (AIF) determination being one of the primary reasons. In this multicenter study to assess the effects of AIF variations on pharmacokinetic parameter estimation, DCE-MRI data acquired at one center from 11 prostate cancer patients were shared among nine centers. Individual AIF from each data set was determined by each center and submitted to the managing center. These AIFs, along with a literature population averaged AIF, and their reference-tissue-adjusted variants were used by the managing center to perform pharmacokinetic data analysis using the Tofts model (TM). All other variables, including tumor region of interest (ROI) definition and pre-contrast T1, were kept constant to evaluate parameter variations caused solely by AIF discrepancies. Considerable parameter variations were observed with the within-subject coefficient of variation (wCV) of Ktrans obtained with unadjusted AIFs being as high as 0.74. AIF-caused variations were larger in Ktrans than ve and both were reduced when reference-tissue-adjusted AIFs were used. These variations were largely systematic, resulting in nearly unchanged parametric map patterns. The intravasation rate constant, kep (= Ktrans/ve), was less sensitive to AIF variation than Ktrans (wCV for unadjusted AIFs: 0.45 vs. 0.74), suggesting that it might be a more robust imaging biomarker of prostate microvasculature than Ktrans.

DCE-MRI of the liver: effect of linear and nonlinear conversions on hepatic perfusion quantification and reproducibility.

To evaluate the effect of different methods to convert magnetic resonance (MR) signal intensity (SI) to gadolinium concentration ([Gd]) on estimation and reproducibility of model‐free and modeled hepatic perfusion parameters measured with dynamic contrast‐enhanced (DCE)‐MRI.

Prospective comparison of magnetic resonance imaging to transient elastography and serum markers for liver fibrosis detection

Establishing accurate non‐invasive methods of liver fibrosis quantification remains a major unmet need. Here, we assessed the diagnostic value of a multiparametric magnetic resonance imaging (MRI) protocol including diffusion‐weighted imaging (DWI), dynamic contrast‐enhanced (DCE)‐MRI and magnetic resonance elastography (MRE) in comparison with transient elastography (TE) and blood tests [including ELF (Enhanced Liver Fibrosis) and APRI] for liver fibrosis detection.

Joint Multitarget Tracking and Sensor Localization in Collaborative Sensor Networks

Multitarget tracking methods in a sensor network often assume the knowledge of the locations of the sensor nodes. However, in reality sensor nodes are randomly deployed with no prior knowledge about their positions. We propose a method to track an unknown and variable number of targets in the presence of false detections with the positions of sensor nodes estimated jointly to avoid the need of extra localization hardware. Moreover, as low-power consumption is a requirement in sensor networks, a collaborative estimation scheme is presented. For each target in the field under observation there is only a small set of sensor nodes that are active while the others remain in an idle state. The proposed technique is based on a Rao-Blackwellized sequential Monte Carlo (SMC) method that takes advantage of the fact that the state space of the unknown variables is separable. Therefore the problem is divided in two parts. The first one generates samples to estimate the number of targets and solves the association uncertainty between measurements and targets; while the second one is a multiple target tracking problem that can be solved with a modified unscented Kalman filter (MUKF) for each sample. It is shown through simulations that it is possible to track the multiple targets and also get accurate estimates of the unknown locations of the sensor nodes.