Nitin Aggarwal

Line detection in images through regularized hough transform

The problem of determining the location and orientation of straight lines in images is of great importance in the fields of computer vision and image processing. Traditionally the Hough transform, (a special case of the Radon transform) has been widely used to solve this problem for binary images. In this paper, we pose the problem of detecting straight lines in gray-scale images as an inverse problem. Our formulation is based on use of the inverse Radon operator, which relates the parameters determining the location and orientation of the lines in the image to the noisy input image. The advantage of this formulation is that we can then approach the problem of line detection within a regularization framework and enhance the performance of the Hough-based line detector through the incorporation of prior information in the form of regularization. We discuss the type of regularizers that are useful for this problem and derive efficient computational schemes to solve the resulting optimization problems enabling their use in large applications. Finally, we show how our new approach can be alternatively viewed as one of finding an optimal representation of the noisy image in terms of elements chosen from a dictionary of lines. This interpretation relates the problem of Hough-based line finding to the body of work on adaptive signal representation.

A novel Global Assessment Scale for Wilson's Disease (GAS for WD)

Wilsons disease (WD) is an inherited disorder of copper metabolism. Despite being treatable, patients with WD suffer severe disabilities due to delay in initiation and difficulty in monitoring treatment. We propose a two tier, Global Assessment Scale for Wilsons Disease (GAS for WD) that grades the multisystemic manifestations of the disease. Tier 1 scores the global disability in four domains: Liver, Cognition and behavior, Motor, and Osseomuscular. Tier 2 is multidimensional scale for a fine grained evaluation of the neurological dysfunction. We prospectively validated this scale in 30 patients with WD. Both tiers had a high inter‐rater reliability (Intraclass correlation coefficient ICC (A, 2) = 0.96–1.0). Tier 2 items were internally consistent (Cronbachs α = 0.89) and factorial analysis showed that 90.3% of the Tier 2 total score variance was determined by seven factors. Scores of both tiers were commensurate with the disease burden as assessed by standard disability scales (Child Pugh, UPDRS, SS3, and CGI) and satisfied criteria for validity. Longitudinal follow‐up over 1.5 years showed that the scale was sensitive to clinical change. This suggests that GAS for WD is a practical tool with potential applications in management of patients, and in testing and comparison of treatment regimens.

Patient-adapted reconstruction and acquisition dynamic imaging method (PARADIGM) for MRI

Dynamic magnetic resonance imaging (MRI) is a challenging problem because the MR data acquisition is often not fast enough to meet the combined spatial and temporal Nyquist sampling rate requirements. Current approaches to this problem include hardware-based acceleration of the acquisition, and model-based image reconstruction techniques. In this paper we propose an alternative approach, called PARADIGM, which adapts both the acquisition and reconstruction to the spatio-temporal characteristics of the imaged object. The approach is based on time-sequential sampling theory, addressing the problem of acquiring a spatio-temporal signal under the constraint that only a limited amount of data can be acquired at a time instant. PARADIGM identifies a model class for the particular imaged object using a scout MR scan or auxiliary data. This object-adapted model is then used to optimize MR data acquisition, such that the imaging constraints are met, acquisition speed requirements are minimized, essentially perfect reconstruction of any object in the model class is guaranteed, and the inverse problem of reconstructing the dynamic object has a condition number of one. We describe spatio-temporal object models for various dynamic imaging applications including cardiac imaging. We present the theory underlying PARADIGM and analyze its performance theoretically and numerically. We also propose a practical MR imaging scheme for 2D dynamic cardiac imaging based on the theory. For this application, PARADIGM is predicted to provide a 10?25 ? acceleration compared to the optimal non-adaptive scheme. Finally we present generalized optimality criteria and extend the scheme to dynamic imaging with three spatial dimensions.

Spatio-temporal modeling and minimum redundancy adaptive acquisition in dynamic MRI

We propose two models for dynamic cardiac imaging, based on the spatial and temporal-spectral support of the object. The models explicitly account for the aperiodicity of cardiac motion. The models are used for both adaptive minimum redundancy data acquisition optimized for the object being imaged and to reconstruct a high temporal resolution movie of the dynamic object. Schemes for (a) estimating the model parameters, (b) designing the minimum redundancy acquisition sequence, and (c) reconstructing the image sequence from acquired data, are presented. Simulated cardiac MRI experiments show high quality cine reconstructions with 20-fold reduction in acquisition rates.

Spatio-temporal modeling and adaptive acquisition for cardiac MRI

We propose a model-based approach for real-time cardiac imaging that does not require cardiac or respiratory gating or breathholds. The model captures the spatial and temporal-spectral characteristics of the heart and also accounts for respiratory motion during imaging. The model parameters are estimated as part of the MRI experiment and drive both data acquisition and cine reconstruction algorithm. Simulated cardiac MRI experiments show quality cine reconstructions and robustness to modeling inaccuracies.

Optimal sampling in parallel magnetic resonance imaging

Parallel MR imaging methods like SMASH, SENSE etc. use multiple receiver coils to accelerate the imaging process by reducing the Fourier space sampling requirement. In this paper we show how one can optimally select the sampling locations based upon (1) knowledge of the statistics of the object being imaged and, (2) a statistical criterion for optimality determined by the application. In particular we show that the optimal uniform sample spacing is not necessarily an integer multiple of the Nyquist interval and depends upon the specific coil sensitivities and configuration.

PATIENT-ADAPTIVE SPATIO-TEMPORAL MRI: FROM PARADIGM TO PARADISE AND BEYOND

Spatial and temporal resolution and image quality in dynamic MRI are severely limited by physical constraints of MRI on the rate of acquisition. The most challenging and important application is cardiac MR (CMR) imaging. We survey work on an explicit model-based methodology developed in our lab, enabling more than an order-of-magnitude reduction in the acquisition requirements in both single and multiple channel MRI, and providing guarantees on the quality of reconstruction subject to the modeling assumptions. Based on time-sequential sampling theory, the approach uses the models to (i) design a minimum redundancy acquisition sequence; and (ii) reconstruct a movie (cine) of the object. By adapting the model to the imaged subject, both acquisition and reconstruction are adaptive. Phantom studies with known ground truth, and in-vivo CMR experiments demonstrate unprecedented spatial and temporal resolutions.

Accelerated parallel magnetic resonance imaging by adaptive k-space sampling

Parallel MR imaging methods like SMASH, SENSE etc. use multiple receiver coils to accelerate the imaging process by reducing the Fourier space sampling requirement. In this paper, we propose a method for selecting the optimal k-space sampling scheme for parallel MR imaging that minimizes the expected reconstruction error given coil sensitivities and desired acceleration factor. We demonstrate that higher acceleration factors are feasible by spacing the k-space non-uniformly instead of at an integer multiple of the Nyquist spacing.

Region-of-interest MRI: k-space sampling conditions

In several MR applications one is interested in reconstructing only a certain region-of-interest (ROI) of the field-of-view (FOV) that is of anatomical or functional importance for the study. However, according to the Nyquist criterion, one is still forced to acquire Fourier (k-space) samples at a rate determined by the FOV instead of the ROI. In this paper we derive necessary and sufficient sampling conditions for recovering the ROI, for uniform and periodic non-uniform sampling, and obtain bounds on the minimal sampling density for arbitrary sampling patterns. We also extend the analysis to parallel MR imaging. The results can be applied to functional MRI, cardiac MRI, etc to reduce the total acquisition time or increase the spatial and temporal resolution, or SNR of the reconstructions