Brain atlases find many applications: Analysis of diffusion tensor imaging and multi-voxel spectroscopy. Population-specific analysis based on normative data (e.g., brain shape and size) for Alzheimer’s disease (AD), dementia and other heuro-degenerative disease diagnosis.

In this project, we focus on the construction of the atlas of the Human Brain for the Indian population. In the current pilot phase, data has been sourced from 50 normal (21-30 yrs of age) subjects from two different scanners (GE and Siemens). When, scans are collected from different manufacturer, there is a need to standardize their intensity range, as they vary from scanner to scanner. We address this issue by proposing a novel standardization method.

Post standardization, the volumes are to be registered to derive a probabilistic atlas.

Strokes are mainly classified in two categories:
1) Ischemic stroke or infarct (due to lack of blood supply)
2) Hemorrhagic stroke (due to rupture of blood vessel)

MRI(DWI) are the preferred modality for detection of stroke. Due to resource constraints MR scanners are not as widely available as Computed tomography (CT) scanners in some countries. CT scans are also faster and less expensive. Hence, this work investigates the use of CT for stroke detection.

An automated method to detect (from non-contrast CT images) and classify an abnormality into acute infarct, chronic infarct and hemorrhage at the slice level has been developed. The proposed method is based on the observation that stroke presence disturbs the natural contra-lateral symmetry of a slice. Accordingly, we characterize stroke as a distortion between the two halves of the brain in terms of tissue density and texture distribution.

Varying the acquisition parameter (b-value) generates different DWI scans with varied contrast. DWI with higher b-values provide improved sensitivity, conspicuity of stroke lesions and reduced artifacts at the cost of lower SNR. Along with the DWI scans, the Apparent Diffusion Coefficients (ADC) maps are also derived which give a measure of the true diffusion process in the brain irrespective of the acquisition artifacts that resemble stroke.

An automated framework comprising of three stages is proposed for accurately capturing the stroke lesions: (i) Stroke Detection (ii) Contrast Enhancement and (iii) Stroke Segmentation, utilizing information from low and high b-valued DWI data and the ADC maps

The detection stage utilizes the information from lower b-value data and the ADC data to identify the stroke lesions on the higher b-value data and reject the false positives due to low SNR in high b-value data. The detected stroke lesions are segmented using the higher b-value data and ADC information. Integrating information from the higher b-value data along with the ADC data helps better define the stroke lesion in the segmentation stage. An auto-windowing technique is designed to enhance the contrast of the stroke lesions in the DWI data prior to segmentation. The contrast enhancement scheme also assists the radiologists in the manual/visual diagnosis of stroke in DWI scans.

The imaging is generally done over an extended period of time (tens of minutes) during which a patient can move which in turn results in corruption of the acquired time series of volumes. This problem is often observed in dynamic magnetic resonance (MR) imaging. Correction for motion after scanning is a highly time-intensive process since it involves registering each volume to a reference volume. Moreover, the injected contrast alters the signal intensity as a function of time and often confounds traditional motion correction algorithms

We have developed a fast and efficient solution for motion correction in 3D dynamic susceptibility contrast (DSC) MR images. This is in the form of a robust, multi-stage system based on a divide and conquer strategy consisting of the following steps: i) subdivision of the time series data into bolus and non-bolus phases depending on the status of bolus in the brain, ii) 2D block-wise phase correlation for detecting motion between adjacent volumes and categorizing the corruption into four categories: none, minimal, mild and severe depending on the degree of motion and iii) a 2-pass, 3D registration consisting of intra-set and inter-set registrations to align the motion corrupted volumes

The subdivision of time-series into distinct sets is achieved using Gamma variate function (GVF) fitting. The dynamic nonuniform variation in signal intensity due to the injected bolus is handled by employing a clustering-based identification of bolus-affected pixels followed by correction of their intensity using the above GVF fitting.

This work is on the hippocampus segmentation (from T1 weighted MRI) and shape-based analysis to support diagnosis of AD. Segmentation of hippocampus is performed by a physics-based model that operates on local and global image forces to evolve a surface. A coarse segmentation method is used to obtain an estimate of the surface to be used in evolution. Since the segmentation method used here is dependent on intensity-based features, image contrast enhancement is performed by an adaptive intensity windowing algorithm which forms the preprocessing stage to segmentation. Thus, each block operates on the output of the previous block, and the output of the final block is useful information to diagnosis.

Quantitative analysis performed on segmented volume of hippocampus has been used to classify it into one of the three stages of diagnosis - normal, mildly affected by the disease and severely affected by the disease. In order to study the structural changes in sub-hippocampal regions with respect to progression of the disease, the piecewise quantitative information has been used.