Joseph Suresh Paul

Indian Institute of Information Technology and Management - Kerala · Computer Science

This article aims at developing a model based optimization for reduction of temporal unwrapping and field estimation errors in multi-echo acquisition of Gradient Echo sequence. Using the assumption that the phase is linear along the temporal dimension, the field estimation is performed by application of unity rank approximation to the Hankel matrix...

Purpose: Develop a processing scheme for Gradient Echo (GRE) phase to enable restoration of susceptibility-related (SuR) features in regions affected by imperfect phase unwrapping, background suppression and low signal-to-noise ratio (SNR) due to phase dispersion. Theory and Methods: The predictable components sampled across the echo dimension in a...

Purpose To develop a spatio-temporal approach to accurately unwrap multi-echo gradient-recalled echo phase in the presence of high-field gradients. Theory and Methods Using the virtual echo–based Nyquist sampled (VENyS) algorithm, the temporal unwrapping procedure is modified by introduction of one or more virtual echoes between the first lower an...

Purpose: Constraints in extended neighborhood system demand the use of a large number of interpolations in directionality-guided compressed-sensing nonlinear diffusion MR image reconstruction technique. This limits its practical application in terms of computational complexity. The proposed method aims at multifold improvement in its runtime witho...

For compressed sensing magnetic resonance imaging (CS-MRI) that utilize sparse representations, the regularization parameter establishes a trade-off between sparsity and data fidelity. While convergence to the desired solution is slow for mean squared error (MSE) optimal constant regularization, continuation using decreasing parameter values enable...

In a GRE acquisition, effect of noise in each voxel can be minimized through the knowledge of prior information about the phase error, which can result in more robust estimation of susceptibility-related features. However, estimating the error in the given phase image of GRE acquisition is difficult. Moreover, the errors can significantly affect th...

Reconstruction of an image from undersampled k-space data using conventional sparsity priors use any of the pre-specified transforms like wavelet, which utilizes the global sparsity of images. Alternatively, methods such as block-matching and 3D filtering (BM3D) utilize the non-local sparsity of images, resulting in better preservation of finer det...

The influence of granularities in the background suppressed phase of susceptibility‐weighted images (SWI) and susceptibility‐weighted angiogram (SWAN) becomes significant when the susceptibility based contrast is enhanced by exponential weighting of the high‐pass filtered phase. Furthermore, the effect of noise due to the inherently low signal‐to‐n...

Purpose: Address the shortcomings of edge-preserving filters to preserve the complex nature of edges, by adapting the direction of diffusion to the local variations in intensity function on a subpixel level, thereby achieving a reconstruction accuracy superior to that of data-driven learning-based approaches. Theory and methods: Rate of diffusio...

Susceptibility weighted imaging (SWI) involves post-processing of gradient echo images which are sensitive to the spatial variations in magnetic susceptibility. The aim of this study is to develop an automated filtering scheme to enhance the contrast-to-noise ratio (CNR) and blooming on SWI. Here, the high-pass filtering for SWI processing is desig...

Compressed sensing (CS) methods in magnetic resonance imaging (MRI) offer rapid acquisition and improved image quality but require iterative reconstruction schemes with regularization to enforce sparsity. Regardless of the difficulty in obtaining a fast numerical solution, the total variation (TV) regularization is a preferred choice due to its edg...

In low-resolution phase contrast magnetic resonance angiography, the maximum intensity projected channel images will be blurred with consequent loss of vascular details. The channel images are enhanced using a stabilized deblurring filter, applied to each channel prior to combining the individual channel images. The stabilized deblurring is obtaine...

In this study, a post-processing filter to enhance diffusion sensitivity, resulting in larger intensity changes in regions with the abrupt transition of local diffusivity in raw diffusion weighted image (DWI) volumes. Weights computed using a nonlinear three-dimensional neighbourhood operation are assigned to each voxel within the neighbourhood, wi...

Purpose: Avoid formation of staircase artifacts in nonlinear diffusion-based MR image reconstruction without compromising computational speed. Methods: Whereas second-order diffusion encourages the evolution of pixel neighborhood with uniform intensities, fourth-order diffusion considers smooth region to be not necessarily a uniform intensity re...

Low sampling density and reduced Signal-to-Noise Ratio (SNR) resulting from accelerated Magnetic Resonance (MR) data acquisition calls for sparsity promoting regularization for improved reconstruction. The simplest optimization strategy relies on Thresholded Landweber (TL) algorithm that results in reconstructions with slow rate of convergence. A v...

Purpose: Eliminate the need for parametric tuning in total variation (TV) based multichannel compressed-sensing image reconstruction using statistically optimized nonlinear diffusion without compromising image quality. Theory and methods: Nonlinear diffusion controls the denoising process using a contrast parameter that separates the gradients c...

This paper quantitatively evaluates regularized GRAPPA or SPIRiT reconstruction in autocalibrating parallel MRI, and develops a Frequency Dependent Regularization (FDR) aimed at achieving higher SNR with reduced penalty on image resolution. The latter is achieved by imposing an upper limit on the extent of regularization for each k-space location....

This chapter summarizes the effects of flow in PC-MRI and introduces methods used to analyze and quantify flow. The chapter begins with a brief introduction to low flip angle GRE sequence and need for velocity encoding. Explanation of phase contrast techniques is provided based on the concept of velocity encoding, followed by individual sections de...

Quantitative evaluation of image processing algorithms for angiography images can only be approached using synthetic images, where the true geometry of vessels is known. This requires both simulation of flow and evolution of magnetization due to flow. The chapter begins with introduction of flow phantoms presented in a computational perspective. Si...

A statistical approach for combination of channel phases is developed for optimizing the Contrast-to-Noise Ratio (CNR) in Susceptibility Weighted Images (SWI) acquired using autocalibrating partially parallel techniques. The unwrapped phase images of each coil are filtered using local random field based probabilistic weights, derived using energy f...

In recent years, several attempts have been made to improve the quality of GRAPPA reconstruction by concomitant reduction of noise amplification and aliasing. While it is true that accuracy of the underlying GRAPPA model depends on the goodness of fit, variations in input noise and channel encoding can serve to introduce differences in reconstructi...

Magnetic resonance imaging (MRI) is a well-known medical imaging technique, that exclusively uses the response of the hydrogen nucleus which is abundant in the human body. In recent years, parallel MRI techniques have been developed to accelerate image acquisition. A notable development in parallel MRI was the introduction of SMASH by Sodicksen and...

In low-resolution phase contrast magnetic resonance angiography, the maximum intensity projected channel images will be blurred with consequent loss of vascular details. The channel images are enhanced using a stabilized deblurring filter, applied to each channel prior to combining the individual channel images. The stabilized deblurring is obtaine...

MRI Simulator is based on numerical solution of the Bloch Equation. The numerical solution is obtained as a time update form of the magnetization vector. Parameters of this time update coefficients are shown to be related to the pulse sequence parameters and gradient amplitudes. The contents include application of the time update solution for simul...

This chapter highlights different post-processing techniques applied to PC-MRA speed images. Although vascular signals in PC-MRA have higher signal magnitudes as compared to background, presence of noise, eddy current and field inhomogeneities can interfere with useful information. This leads to signal losses in vascular regions, and increased sign...

This chapter provides a quick introduction to the basics of MRI, adequate for understanding of the terminology and concepts used in Phase-Contrast MR Angiography (PC-MRA). Chapter begins with a description of signal generation, followed by an explanation for the need of spatial encoding necessary for derivation of the Fourier imaging approximation....

Providing many unique MATLAB codes and functions throughout, this book covers the basics of Magnetic Resonance Imaging (MRI), leading to an in-depth understanding of the concepts and tools required for analysis and interpretation of Phase Contrast MR Angiography (PC-MRA). The concept of PC-MRA is often difficult, but essential for practicing engine...

Providing many unique MATLAB codes and functions throughout, this book covers the basics of Magnetic Resonance Imaging (MRI), leading to an in-depth understanding of the concepts and tools required for analysis and interpretation of Phase Contrast MR Angiography (PC-MRA). The concept of PC-MRA is often difficult, but essential for practicing engine...

The aim of this paper is to introduce procedural steps for extension of the 1D homodyne phase correction for k-space truncation in all gradient encoding directions. Compared to the existing method applied to 2D partial k-space, signal losses introduced by the phase correction filter is observed to be minimal for the extended approach. In addition,...

Anisotropic Diffusion is widely used for noise reduction with simultaneous preservation of vascular structures in maximum intensity projected (MIP) angiograms. However, extension to minimum intensity projected (mIP) venograms in Susceptibility Weighted Imaging (SWI) poses difficulties due to spatially varying baseline. Here, we introduce a modified...

This special issue provides a collection of papers that focus on information fusion in medical imaging to improve the quality of images, applications of image fusion in medical diagnostics, and different models/approaches for achieving image fusion. Image quality indicators, texture analysis, morphology-based studies, transform-based fusion approac...

Retrieval of similar anatomical structures of brain MR images across patients would help the expert in diagnosis of diseases. In this paper, modified local binary pattern with ternary encoding called modified local ternary pattern (MOD-LTP) is introduced, which is more discriminant and less sensitive to noise in near-uniform regions, to locate slic...

Application of filtering based techniques for reduction of truncation artifacts in image reconstruction from undersampled 2D partial k-space is often accompanied by smearing of localized fine structures, with loss of clinical information. To address this problem, we use model based echo-filters synthesized by establishing a causal relationship betw...

Magnetic Resonance images play a crucial role in the diagnosis and management of the diseases of the brain. The MRI can acquire cross sectional images of our body, based on T1 and T2 relaxation of the tissues. As the information presented in these two images is often complimentary, both these images need to be compared for accurate clinical diagnos...

Scan time reduction in MRI can be achieved by partial k-space reconstruction. Truncation of the k-space results in generation of artifacts in the reconstructed image. A subspace projection algorithm is developed for artifact-free reconstruction of sparse MRI. The algorithm is applied to a frequency weighted k-space, which fits into a signal-space m...

Signal space models in both phase-encode, and frequency-encode directions are presented for extrapolation of 2D partial kspace. Using the boxcar representation of low-resolution spatial data, and a geometrical representation of signal space vectors in both positive and negative phase-encode directions, a robust predictor is constructed using a seri...

We present an edge preserving and denoising filter for enhancing the features in images, which contain an ROI having a narrow spatial extent. Typical examples include angiograms, or ROI spatially distributed in multiple locations and contained within an outlying region, such as in multiple-sclerosis. The filtering involves determination of multipli...

AbstractA linear prediction (LP) filter derived from a complete echo with zero‐phase encoding amplitude is used for recovering anatomical details from a partially acquired echo sequence. The LP filter is shown to reconstruct missing k‐space phase and amplitude information, with errors sufficiently low so as to provide image reconstruction with a co...

Brain-computer interfaces (BCIs) enable control of computers and other assistive devices, such as neuro-prostheses, which are used for communication, movement restoration, neuro-modulation, and muscle stimulation, by using only signals measured directly from the brain. A BCI creates a new output channel for the brain to a computer or a device. This...

Using the boxcar representation in the spatial domain and a signal-space representation of its frequency-weighted k -space, an iterative prediction method is developed to derive an improved low-resolution phase approximation for phase correction. Compared to the homodyne filter, the proposed predictor is found to be more efficient due to its capa...

Magnetic resonance images play a vital role in identifying various brain related problems. Some of the diseases of the brain show abnormalities predominately at a particular anatomical location which on MR appears at a slice at defined level. This paper proposes a novel technique to locate desired slice using Rotational, Scaling and Translational (...

Axial brain slices containing similar anatomical structures are retrieved using features derived from the histogram of Local binary pattern (LBP). A rotation invariant description of texture in terms of texture patterns and their strength is obtained with the incorporation of local variance to the LBP, called Modified LBP (MOD-LBP). In this paper,...

The paper investigates the role of ultrasound wave shape and beam size on the modulation of photon fluence rate in a homogenous turbid medium. For a rectangular geometry irradiated by a point source of light, the spatial variation of modulation amplitudes is numerically computed using a diffusion approximation model. For a focused Continuous-Wave (...

We show that the transient response of acoustically modulated optical flux in a turbid medium irradiated by a pulsed point source of light is delayed in time relative to the light-alone flux obtained in the absence of acoustic modulation. The time delay is shown to result from an initial phase of flux reversal, as determined by the time point of th...

Far-field intensities of light scattered from a linear centro-symmetric array illuminated by a plane wave of incident light are estimated at a series of detector angles. The intensities are computed from the superposition of E-fields scattered by the individual array elements. An average scattering phase function is used to model the scattered fiel...

Acoustic modulation of light scattering from a linear centrosymmetric array is analyzed by considering far-field contributions due to optoelastic (OE) effect and acoustically induced translation of the array elements. The modulated light intensity is shown to vary sinusoidally at the acoustic frequency when the physical constants representative of...

Laser speckle imaging (LSI) has increasingly become a viable technique for real-time medical imaging. However, the computational intricacies and the viewing experience involved limit its usefulness for real-time monitors such as those intended for neurosurgical applications. In this paper, we report a proposed technique, tLASCA, which processes sta...

A closed form solution for time-averaged modulated fluence rate is presented for acoustically modulated diffusive light propagation in a medium. The solution assumes that the component of modulated light flux in the direction of acoustic pressure variation is zero.

Simultaneous use of EEG and MRI offer new methodologies for studying the structure and function of the brain. Section 12.1 outlines the techniques for utilizing anatomical information from the MRI in solving the EEG inverse problem. Section 12.2 addresses the theoretical and practical considerations for recording and analyzing simultaneous EEG-fMRI...

Till now, most studies of the Blood Oxygen Level-Dependent (BOLD) response to interictal epileptic discharges (IED) have assumed that its time course matches closely to that of brief physiological stimuli, commonly called the canonical event-related haemodynamic response function (canonical HRF). Analyses based on that assumption have produced sign...

Diffuse lesions of the white matter of the human brain are common pathological findings in magnetic resonance images of elderly subjects. These lesions are typically caused by small vessel diseases (e.g., due to hypertension, diabetes), and related to cognitive decline. Because these lesions are inhomogeneous, unsharp, and faint, but show an intens...

Laser speckle imaging has increasingly become a viable technique for real-time medical imaging. However, the computational intricacies and the viewing experience involved limit its usefulness for real-time monitors such as those intended for neurosurgical applications. In this paper, we propose a new technique, tLASCA, which processes statistics pr...

Till now, most studies of the Blood Oxygen Level-Dependent (BOLD) response to interictal epileptic discharges (IED) have assumed that its time course matches closely to that of brief physiological stimuli, commonly called the canonical event-related haemodynamic response function (canonical HRF). Analyses based on that assumption have produced sign...

Statistically mapped speckle autocorrelation images (SAR) were used to track the hemodynamically active perfusion regions in the rat cortex during and following DC current stimulation with high transverse spatial resolution (38 um). The SAR images provided a spatio-temporal information about the net activation patterns of Cerebral Blood Flow (CBF)...

Previous studies using simultaneous EEG and fMRI recordings have yielded discrepant results regarding the topography of brain activity in relation to spontaneous power fluctuations in the alpha band of the EEG during eyes-closed rest. Here, we explore several possible explanations for this discrepancy by re-analyzing in detail our previously report...

Laser Speckle Contrast Analysis (LASCA) has been used to image the development of an ischemic core following photochemically induced infarction in rats up to 1 h post-lesion. Using LASCA, we have been able to image a central ischemic core which had little or no perfusion surrounded by a penumbral region with reduced perfusion. In addition, we have...

In response to a lesion, intact regions of cortex in both hemispheres undergo adaptive changes in network function. For example, changes in excitability and intracortical inhibition in primary motor cortex (M1) were reported after lesioning contralateral or ipsilateral brain regions. Close interactions exist between M1 and primary somatosensory cor...

Contrast values estimated from temporal statistics is used for laser speckle contrast analysis (LASCA) of cortical blood flow and perfusion. Using temporal statistics, we are able to reliably estimate the blood flow and perfusion by processing only a selected number of pixels from the raw speckle images. For the number of frames (N > 6) for estimat...

In this paper, an adaptive Markov process amplitude algorithm is used to model and simulate electroencephalogram (EEG) signals. EEG signal modeling is used as a tool to identify pathophysiological EEG changes potentially useful in clinical diagnosis. The least mean square algorithm is adopted to continuously estimate the parameters of a first-order...

A major problem associated with longterm ECG recordings is the enormous volume of data they contain and the requirement of an efficient procedure for its archival in reduced form is highly desirable. The storage of multiple recordings poses limitations, especially when they are to be used at a later time for applications involving high resolution m...

AbsIract-A major problem associated with longterm ECG recordings is the enormous volume of data they contain and the requirement of an efficient procedure for its archiral in reduced form is highly desirable. The storage of multiple recordings poses limitations, especially when they are to be used at a later time for applications inrolving high res...

Deep brain stimulation (DBS) is a critical therapeutic regimen for drug-resistant epilepsy. Our proposed method of assessing the neuromodulatory effects of DBS on seizure threshold and post-ictal activity is based on the principle that seizure build-up is always preceded by constantly changing EEG and field potential bursting levels. We use a novel...

In this paper, subband wavelet entropy (SWE) is used for the segmentation of electroencephalographic signals (EEG) recorded during injury and recovery following global cerebral ischemia. Wavelet analysis is used to decompose the EEG into standard clinical subbands followed by computation of the Shannon entropy. The EEG was measured from rodent brai...

Our proposed algorithm for seizure prediction is based on the principle that seizure build-up is always preceded by constantly changing bursting levels. We use a novel measure of residual subband wavelet entropy (RSWE) to directly estimate the entropy of bursts, which is otherwise obscured by the ongoing background activity. Our results are obtaine...

A novel method, "wavelet entropy," is presented for the analysis of EEG signals recorded during injury and recovery following global cerebral ischemia. The EEG is recorded from rodent brains in a controlled experimental brain injury model by hypoxic-ischemic cardiac arrest. Wavelet analysis is used to decompose the EEG into standard clinical subban...

Previous studies have shown that parameters of EEG restitution reflect the severity of global hypoxic-ischemic brain injury. Here, the hypothesis is tested that patterns of EEG restitution during the first 4 hours predict later behavioral recovery. Time course and correlations between behavior, electrocorticogram (EcoG), and neuronal injury were in...

We consider an improved least square algorithm based on subspace rotations using Procrustes approximation. Robust estimation of the poles of a narrowband signal is achieved by reducing the bias of the estimates. The key idea is to consider the signal as a vector in multi-dimensional space, and to separate the “pure signal” and the noise into two mu...

The nonextensive entropy measure is developed to study the electroencephalogram (EEG) during the recovery of the brain's electrical function from asphyxic cardiac arrest (ACA) injury. The statistical characteristics of the Tsallis-like time-dependent entropy (TDE) for different signal distributions are investigated. Both the mean and the variance o...

In this paper, adaptive Markov process amplitude modeling was used to simulate and segment EEG signals. The least mean square adaptive algorithm was used to estimate the parameters of a first order Markov model. The coefficients of the model were utilized for EEG signal segmentation. The EEG signals were recorded from a controlled experimental setu...

In this paper, we present a Wiener filtering (WF) approach for extraction of somatosensory evoked potentials (SEPs) from the background electroencephalogram (EEG), with sweep-to-sweep variations in its signal power. To account for the EEG power variations, WF is modified by iteratively weighting the power spectrum using the coherence function. Cohe...