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ImageJ in Biomedical Imaging Applications

Abstract

Magnetic Resonance Imaging (MRI) is a commonly used technique in the field of biomedical research for non invasive monitoring of various models of human diseases, such as traumatic brain edema, stroke or spinal cord injury. Since the data acquisition load on the MR workstations in most of the time is rather high this requires development of portable applications to process and analyze reconstructed images offline. ImageJ was initially developed as an image processing platform for microscopic applications. However, with the spread of non-optical biomedical imaging modalities, such as MRI, Computer Tomography (CT) and Positron Emission Tomography (PET) the demand to incorporate processing of such data in ImageJ has grown. In this workshop I will demonstrate the use of ImageJ in the processing and analysis of MRI data sets.

The data processing workflow, which I will demonstrate is developed for the Bruker Biospin MR scanner. The system is operated by the proprietary Paravision software, which also allows for performing image reconstruction, morphometry, and computation of parametric image maps. Such parametric maps are derived images where each pixel value represents a parametric fit to time varying data sequence. Parametric maps are, for example, T1, T2 and proton density maps. The native Bruker data format has a rather complex structure, i.e. the data sets are distributed in multiple text files containing metadata and binary files containing the raw MR data and reconstructed images.

In order to implement the most common processing steps, I have developed a framework for computation of parametric maps based on time-varying data in ImageJ. The T2-map functionality has been already validated against the Paravision software. The workflow will be exemplified by demonstration of processing of MR images of rodent brains and phantoms containing iron oxide nanoparticles.

Acknowledgments

I would like to thank Ms. Carole Ciliberti for the initial programming work on the Bruker data format.

Keywords

MRI, T2, T1, image statistics, curve fitting

Administrative data

Presenting author: Dimiter Prodanov (1)
Organisation: Imec

co-authors: Simon Roussel (2), Jesse Trekker (3), Tom Dresselaers (3), Uwe Himmelreich (4)

(1) Bioelectronic Systems group, Imec, Leuven, Belgium; (2) CI-NAPS, University Paris Descartes, Paris, France; (3) Functional Nanosystems group, Imec, Leuven, Belgium; (4) MoSAIC, KULeuven, Leuven, Belgium

Type: Workshop
Duration: 90 min

Hardware and Software Requirements: I will send the distribution packages.
Knowledge of participant: user level

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