Biomedical subjects

Usha Sinha

Publications and source records attributed to Usha Sinha.

16 recordsLinked to original sources

PubMed2006

In vivo diffusion tensor imaging of the human calf muscle.

PURPOSE: To demonstrate the feasibility of in vivo calf muscle fiber tracking in human subjects. MATERIALS AND METHODS: An EPI-based diffusion tensor imaging (DTI) sequence with six-direction diffusion gradient sensitization was implemented, and DT images were acquired at 3 Tesla on five subjects using an extremity coil. The mean diffusivity, fractional anisotropy (FA), and fiber angle (with respect to the magnet z-axis) were measured in different muscles, and fibers were tracked from several regions of interest (ROIs). RESULTS: The fiber orientations in the current DTI studies agree well with those determined in previous spectroscopic studies. The orientation angles ranged from 13.4 degrees in the lateral gastrocnemius to 48.5 degrees in the medial soleus. The diffusion ellipsoid in muscle tissue is anisotropic and approximates a prolate model, as shown by color maps of the anisotropy. Fibers were tracked from the different muscle regions, and the unipennate and bipennate structure of muscle fibers was visualized. CONCLUSION: The study clearly shows that in vivo fiber tracking of muscle fibers is feasible and could potentially be applied to study muscle structure function relationships.

Adult↗

PubMed2006

Statistical representation of mean diffusivity and fractional anisotropy brain maps of normal subjects.

PURPOSE: To create diffusion tensor atlases from echo planar imaging (EPI) images acquired at 3 T in 10 normal subjects. MATERIALS AND METHODS: Data from 10 right-handed healthy adult volunteers (mean age of 31 +/- 3 years; eight males) were acquired using a 3.0-T scanner. Geometric distortion artifacts correction was accomplished by combining parallel acquisition to reduce the distortion as well as postprocessing by registration to a geometrically accurate T2-weighted fast-spin-echo image. This reduced distortions to within a voxel for most of the internal structures of the brain. The apparent diffusion coefficient (ADC) and fractional anisotropy (FA) atlases were created by warping images using an iterative optical-flow-based local deformation algorithm that used two channels of data: ADC and FA. RESULTS: A three-dimensional distance measure was used to evaluate the accuracy of the registration algorithm with contours defined on two structures: the corpus callosum and cerebellum. The average three-dimensional distance value for the nine subjects (with the 10th as the reference) was 0.2 mm for the corpus callosum and 1.2 mm for the cerebellum. CONCLUSION: A high-resolution, diffusion MR atlas with full brain coverage was developed. Additionally, maps of the SD of the diffusion indices were also generated to provide an estimate of the variance within a normal population. Active shape and texture models were also generated for the corpus callosum as an alternate method of representing the variance in morphology and diffusion indices.

Adult↗

PubMed2006

Quantitative metrics for evaluating parallel acquisition techniques in diffusion tensor imaging at 3 Tesla.

OBJECTIVES: Single-shot echo-planar based diffusion tensor imaging is prone to geometric and intensity distortions. Parallel imaging is a means of reducing these distortions while preserving spatial resolution. A quantitative comparison at 3 T of parallel imaging for diffusion tensor images (DTI) using k-space (generalized auto-calibrating partially parallel acquisitions; GRAPPA) and image domain (sensitivity encoding; SENSE) reconstructions at different acceleration factors, R, is reported here. MATERIALS AND METHODS: Images were evaluated using 8 human subjects with repeated scans for 2 subjects to estimate reproducibility. Mutual information (MI) was used to assess the global changes in geometric distortions. The effects of parallel imaging techniques on random noise and reconstruction artifacts were evaluated by placing 26 regions of interest and computing the standard deviation of apparent diffusion coefficient and fractional anisotropy along with the error of fitting the data to the diffusion model (residual error). RESULTS: The larger positive values in mutual information index with increasing R values confirmed the anticipated decrease in distortions. Further, the MI index of GRAPPA sequences for a given R factor was larger than the corresponding mSENSE images. The residual error was lowest in the images acquired without parallel imaging and among the parallel reconstruction methods, the R = 2 acquisitions had the least error. The standard deviation, accuracy, and reproducibility of the apparent diffusion coefficient and fractional anisotropy in homogenous tissue regions showed that GRAPPA acquired with R = 2 had the least amount of systematic and random noise and of these, significant differences with mSENSE, R = 2 were found only for the fractional anisotropy index. CONCLUSION: Evaluation of the current implementation of parallel reconstruction algorithms identified GRAPPA acquired with R = 2 as optimal for diffusion tensor imaging.

Adult↗

PubMed2005

Geometric distortion correction of high-resolution 3 T diffusion tensor brain images.

Diffusion-weighted images based on echo planar sequences suffer from distortions due to field inhomogeneities from susceptibility differences as well as from eddy currents arising from diffusion gradients. In this paper, a novel approach using nonlinear warping based on optic flow to correct distortions of baseline and diffusion weighted echo planar images (EPI) acquired at 3 T is presented. The distortion correction was estimated by warping the echo planar images to the anatomically correct T2-weighted fast spin echo images (T2-FSE). A global histogram intensity matching of the T2-FSE precedes the base line EPI image distortion correction. A local intensity-matching algorithm was used to transform labeled T2-FSE regions to match intensities of diffusion-weighted EPI images prior to distortion correction of these images. Evaluation was performed using three methods: (i) visual comparison of overlaid contours, (ii) a global mutual information index, and (iii) a local distance measure between homologous points. Visual assessment and the global index demonstrated a decrease in geometrical distortion and the distance measure showed that distortions are reduced to a subvoxel level. In conclusion, the warping algorithm is effective in reducing geometric distortions, enabling generation of anatomically correct diffusion tensor images at 3 T.

Adult↗

PubMed2004

In vivo diffusion tensor imaging of the human prostate.

This study demonstrates the feasibility of in vivo prostate diffusion tensor imaging (DTI) in human subjects. We implemented an EPI-based diffusion-weighted (DW) sequence with seven-direction diffusion gradient sensitization, and acquired DT images from six subjects using cardiac gating with a phased-array prostate surface coil operating in a linear mode. We calculated two indices to quantify diffusion anisotropy. The direction of the eigenvector corresponding to the leading eigenvalue was displayed by means of a color-coding scheme. The average diffusion values of the prostate peripheral zone (PZ) and central gland (CG) were 1.95 +/- 0.08 x 10(-3) mm2 s and 1.53 +/- 0.34 x 10(-3) mm2 s, respectively. The average fractional anisotropy (FA) values for the PZ and CG were 0.46 +/- 0.04 and 0.40 +/- 0.08, respectively. The diffusion ellipsoid in prostate tissue was anisotropic and approximated a prolate model, as shown in the color maps of the anisotropy. Consistent with the tissue architecture, the prostate fiber orientations were predominantly in the superior-inferior (SI) direction for both the PZ and CG. This study shows the feasibility of in vivo DTI and establishes normative DT values for six subjects.

Anisotropy↗

PubMed2004

Parametric brain MR atlases: standardization for imaging informatics.

This paper is focused on the development of normal MR brain atlases of intrinsic MR parameters. These parameters permit quantitative comparisons across imaging studies (as opposed to raw image intensity values) and are important markers of neurological diseases. The development includes fast sequences to generate three parameters (T1: spin-lattice relaxation, T2: spin-spin relaxation, and Diffusion Tensor) covering the whole brain with isotropic and high-resolution images. The analysis of raw data to generate the parametric images is followed by registration algorithms to bring the image studies acquired on normal subjects aligned to a common frame of reference. The registration method includes both linear and non-linear algorithms. Two atlas schemes are discussed: an average atlas and a probabilistic atlas. Initial results on sequence development and registration are presented. The atlases are envisaged as an integral part of an imaging informatics infrastructure that enables image analysis across imaging studies to perform automated image data mining.

Algorithms↗

PubMed2002

In vivo diffusion tensor imaging of human calf muscle.

PURPOSE: To investigate a tetrahedral diffusion gradient encoding scheme to measure the diffusion tensor in vivo for human calf muscle. MATERIALS AND METHODS: The theoretical TE which maximizes the signal-to-noise ratio (SNR) of the diffusion images was derived for both the orthogonal and tetrahedral sampling strategies and the SNR advantage verified experimentally. A diffusion echo-planar imaging (EPI) sequence was used to image five volunteers. Gradient cycling and geometric averaging was performed to eliminate cross-terms between the imaging and diffusion gradients. RESULTS: Trace diffusion coefficients in human muscle are spatially invariant and have low intersubject variability (<4%). Images of the off-diagonal terms confirm the anisotropy of muscle, and fiber orientation maps were derived from these off-diagonal images. A noninvariant index of anisotropy, A(ratio) (average value: 1.28), was found to be less susceptible to noise than the invariant index. CONCLUSION: This technique is robust and can be readily implemented on clinical scanners with EPI capabilities.

Anisotropy↗

PubMed2002

In vivo diffusion-weighted MRI of the breast: potential for lesion characterization.

PURPOSE: To investigate the potential of apparent diffusion coefficients (ADCs) in characterizing breast lesions in vivo. MATERIALS AND METHODS: Two diffusion-weighted (DW) sequences were implemented on a 1.5 Tesla scanner, with low b-value orthogonal and high b-value tetrahedral sensitized sequences. The orthogonal sequence was evaluated on 16 normal volunteers and 23 patients with known lesion types (six benign and 17 malignant). The tetrahedral sequence was evaluated on a smaller number of subjects: two normal, two malignant, and two benign. RESULTS: The mean value of the ADC of the malignant tumors was reduced compared to that of the benign lesions and normal tissue. This finding was related to the increased cellularity of the malignant lesions. The ADC values were elevated for all tissue types with the low b-value sequence as compared to the high b-value sequence, indicating contributions from perfusion effects at the low b-values. CONCLUSION: The study clearly shows that DW-MRI can help characterize breast lesions in vivo.

Breast↗

PubMed2002

DataServer: an infrastructure to support evidence-based radiology.

Following a requirements analysis for development of an information infrastructure supporting evidence-based radiology, the objective of this study was the development of a data gateway to support flexible access to the totality of a patient's electronic medical records through a single, uniform representation, regardless of the underlying data sources (eg, hospital information systems [HIS], radiology information systems [RIS], picture archiving and communication systems [PACS]). XML-based (eXtensible Markup Language) technologies were employed to create an application framework permitting querying of different clinical databases. The contents of different data sources were represented by using XML. On the basis of these representations, users can specify queries. The system transforms the XML queries into a query format understood by the specific databases, processes the query, and transforms the results back into an XML format. XML results can then be transformed in accordance to different data-formatting standards. Access to several different data sources, including HIS, RIS, and PACS, has been accomplished with this framework. The extensible nature of the XML data gateway enables data sources to be readily added. The framework also provides a means by which data can be systematically de-identified to protect patient confidentiality, thus supporting research endeavors.

Evidence-Based Medicine↗

PubMed2002

Functional magnetic resonance of human breast tumors: diffusion and perfusion imaging.

This review is focused on two relatively new developments in magnetic resonance imaging (MRI) and their application to breast lesion characterization: diffusion and perfusion MRI. Diffusion MRI measures the mobility of the water protons and thus provides a window to tissue microstructure. Perfusion MRI measures the rate at which blood is delivered to tissue and thus provides information about microvasculature. Because both tissue structure and vasculature are likely to change in disease states, measurement of diffusion and perfusion may have direct physiologic relevance. This review covers topics related to the imaging sequences, image analysis, and clinical studies for diffusion and perfusion breast MRI. Preliminary studies show that the apparent diffusion coefficient (ADC) is a marker of cell density and can distinguish malignant from benign lesions. Perfusion MR also shows promise for breast tumor characterization: malignant tumors have consistently higher relative tissue blood volumes (rTBV) than normal and benign tumors. Additional research is required with large patient cohorts to establish these two techniques on a clinical footing.

Breast↗

PubMed2002

A review of medical imaging informatics.

This review of medical imaging informatics is a survey of current developments in an exciting field. The focus is on informatics issues rather than traditional data processing and information systems, such as picture archiving and communications systems (PACS) and image processing and analysis systems. In this review, we address imaging informatics issues within the requirements of an informatics system defined by the American Medical Informatics Association. With these requirements as a framework, we review, in four sections: (1) Methods to present imaging and associated data without causing an overload, including image study summarization, content-based medical image retrieval, and natural language processing of text data. (2) Data modeling techniques to represent clinical data with focus on an image data model, including general-purpose time-based multimedia data models, health-care-specific data models, knowledge models, and problem-centric data models. (3) Methods to integrate medical data information from heterogeneous clinical data sources. Advances in centralized databases and mediated architectures are reviewed along with a discussion on our efforts at data integration based on peer-to-peer networking and shared file systems. (4) Visualization schemas to present imaging and clinical data: the large volume of medical data presents a daunting challenge for an efficient visualization paradigm. In this section we review current multimedia visualization methods including temporal modeling, problem-specific data organization, including our problem-centric, context and user-specific visualization interface.

Databases, Factual↗

PubMed2002

Designing a patient education framework via use case analysis.

One of the challenges of developing patient education content in electronic form is to determine the information and interaction needs that motivate a patient to learn in the first place. We have found that use case analysis of patient education helps in clarifying the types of information and interaction required to educate a patient effectively. This paper presents a use case model for patient education as well as a Java-based framework that facilitates both the extension and updating of individually tailored, electronic patient education content. The framework defines an abstract interface that represents a particular panel of information, and provides a content manager that dynamically discovers and refreshes new panels as they are added or modified.

Computational Biology↗

PubMed2002

Structured reporting in neuroradiology.

We have developed a system to structure free-text neuroradiology reports using a natural language processing program and formatted the output into the digital image and communication in medicine (DICOM) standard for structured reporting (SR). DICOM SR formats the correspondence of pertinent diagnostic images to the radiologist's dictated report of clinical findings. In addition, DICOM SR allows the information to be organized into a tree structure. Individual nodes of the tree can contain individual items or lists. Structuring the content of free-text information allows the creation of hierarchies with defined relationships between the concepts contained within the report.

Brain↗

PubMed2002

Image study summarization of MR brain images by automated localization of relevant structures.

The paper discusses a methodology to objectify the patient presenting condition by automated selection of relevant images from a serial MR study. Structured data entry is used to capture the patient's chief complaint, pertinent history, signs, and symptoms. Expert created rules use this data to arrive at a differential and to identify the affected brain region/structure. Another expert created knowledge base then maps this information to the relevant image type, including image sequence specifics and orientation. A DICOM study reader identifies the relevant imaging sequences from the MR study. The structure localization method involves a search based on principal component analysis. A training set of subimages containing the structure of interest is used to generate a basis set of prototype images called eigenimages. The structure is located in an image by searching the image for a subregion that best matches the basis set. The structure localization was used to locate the lateral ventricles and orbits in nine images that were not part of the training set. The automated localizations were compared to expert localizations and the center of the regions located by the two techniques agreed to within +/- 1.7 mm (average for the nine localizations each of two structures).

Automation↗

PubMedDate not supplied

Reduction of electronic noise from radiofrequency generator during radiofrequency ablation in interventional MRI.

MRI has been used increasingly in the recent past for the guidance and monitoring of minimally invasive interventional procedures, using typically radiofrequency (RF) and laser energy, cryoablation, and percutaneous ethanol. RF energy has been used over the last 30 years for the ablation of tissues. Its use in conjunction with MRI for monitoring is limited, however, because of the electronic noise produced by the RF generators, which can significantly deteriorate image quality. The objective of this work was to devise methods by which this noise can be reduced to an acceptable level to allow simultaneous acquisition of MR images for monitoring purposes with the application of RF energy. Three different methods of noise reduction were investigated in a 0.2 T MR scanner: filtration using external hardware circuitry, MR scanner software-controlled filtration, and keyholing. The last two methods were unable by themselves to suppress the noise to an acceptable degree. Hardware filtration, however, provides excellent suppression of RF noise and is able to withstand up to 12 W of RF energy. When all the three approaches are combined, significant reduction of RF noise is achieved. The feasibility of creating an RF lesion of about 1.2 cm diameter in vivo in a porcine model simultaneously with temperature-sensitive MRI with adequate noise suppression is demonstrated.

Animals↗

PubMedDate not supplied

Principal component analysis for content-based image retrieval.

Most picture archiving and communication systems provide image search capabilities that support queries based on patient demographics and study descriptions. In a preliminary study, principal component analysis was used to represent and retrieve images on the basis of content. Principal component analysis reduces the dimensionality of the search to a basis set of prototype images that best describes the images. Each image is described by its projection on the basis set; a match to a query image is determined by comparing its projection vector on the basis set with that of the images in the database. The training image database consisted of 100 axial brain images from a three-dimensional T1-weighted magnetic resonance imaging study. The algorithm was evaluated by using 96 axial images from eight patients. Image retrieval was considered accurate if the automated algorithm returned the match section to within 3 mm of an expert-selected section; the retrieval accuracy was 83% when the images were preprocessed for uniformity in intensity and geometry. Principal component analysis can be applied to content-based retrieval of medical images. The algorithm is designed to be part of an automated image selection module that filters relevant images from an imaging study.

Algorithms↗