Literature DB >> 19116196

Nonrigid registration of joint histograms for intensity standardization in magnetic resonance imaging.

Florian Jäger1, Joachim Hornegger.   

Abstract

A major disadvantage of magnetic resonance imaging (MRI) compared to other imaging modalities like computed tomography is the fact that its intensities are not standardized. Our contribution is a novel method for MRI signal intensity standardization of arbitrary MRI scans, so as to create a pulse sequence dependent standard intensity scale. The proposed method is the first approach that uses the properties of all acquired images jointly (e.g., T1- and T2-weighted images). The image properties are stored in multidimensional joint histograms. In order to normalize the probability density function (pdf) of a newly acquired data set, a nonrigid image registration is performed between a reference and the joint histogram of the acquired images. From this matching a nonparametric transformation is obtained, which describes a mapping between the corresponding intensity spaces and subsequently adapts the image properties of the newly acquired series to a given standard. As the proposed intensity standardization is based on the probability density functions of the data sets only, it is independent of spatial coherence or prior segmentations of the reference and current images. Furthermore, it is not designed for a particular application, body region or acquisition protocol. The evaluation was done using two different settings. First, MRI head images were used, hence the approach can be compared to state-of-the-art methods. Second, whole body MRI scans were used. For this modality no other normalization algorithm is known in literature. The Jeffrey divergence of the pdfs of the whole body scans was reduced by 45%. All used data sets were acquired during clinical routine and thus included pathologies.

Mesh:

Year:  2009        PMID: 19116196     DOI: 10.1109/TMI.2008.2004429

Source DB:  PubMed          Journal:  IEEE Trans Med Imaging        ISSN: 0278-0062            Impact factor:   10.048


  19 in total

1.  Cross gender-age trabecular texture analysis in cone beam CT.

Authors:  H Ling; X Yang; P Li; V Megalooikonomou; Y Xu; J Yang
Journal:  Dentomaxillofac Radiol       Date:  2014-02-03       Impact factor: 2.419

2.  PATCH BASED INTENSITY NORMALIZATION OF BRAIN MR IMAGES.

Authors:  Snehashis Roy; Aaron Carass; Jerry L Prince
Journal:  Proc IEEE Int Symp Biomed Imaging       Date:  2013-12-31

3.  Unsupervised MRI Homogenization: Application to Pediatric Anterior Visual Pathway Segmentation.

Authors:  Carlos Tor-Diez; Antonio R Porras; Roger J Packer; Robert A Avery; Marius George Linguraru
Journal:  Mach Learn Med Imaging       Date:  2020-09-29

4.  Interactive segmentation of plexiform neurofibroma tissue: method and preliminary performance evaluation.

Authors:  Lior Weizman; Lior Hoch; Dafna Ben Bashat; Leo Joskowicz; Li-tal Pratt; Shlomi Constantini; Liat Ben Sira
Journal:  Med Biol Eng Comput       Date:  2012-06-16       Impact factor: 2.602

5.  Longitudinal Intensity Normalization of Magnetic Resonance Images using Patches.

Authors:  Snehashis Roy; Aaron Carass; Jerry L Prince
Journal:  Proc SPIE Int Soc Opt Eng       Date:  2013-03-12

6.  Pulse Sequence based Multi-acquisition MR Intensity Normalization.

Authors:  Amod Jog; Snehashis Roy; Aaron Carass; Jerry L Prince
Journal:  Proc SPIE Int Soc Opt Eng       Date:  2013-03-03

7.  Robust Intensity Standardization in Brain Magnetic Resonance Images.

Authors:  Giorgio De Nunzio; Rosella Cataldo; Alessandra Carlà
Journal:  J Digit Imaging       Date:  2015-12       Impact factor: 4.056

8.  Hybrid cone-beam tomographic reconstruction: incorporation of prior anatomical models to compensate for missing data.

Authors:  Ofri Sadowsky; Junghoon Lee; E Grant Sutter; Simon J Wall; Jerry L Prince; Russell H Taylor
Journal:  IEEE Trans Med Imaging       Date:  2010-07-26       Impact factor: 10.048

9.  An Automated Method for Segmenting White Matter Lesions through Multi-Level Morphometric Feature Classification with Application to Lupus.

Authors:  Mark Scully; Blake Anderson; Terran Lane; Charles Gasparovic; Vince Magnotta; Wilmer Sibbitt; Carlos Roldan; Ron Kikinis; Henry J Bockholt
Journal:  Front Hum Neurosci       Date:  2010-04-19       Impact factor: 3.169

10.  Efficient and robust model-to-image alignment using 3D scale-invariant features.

Authors:  Matthew Toews; William M Wells
Journal:  Med Image Anal       Date:  2012-11-29       Impact factor: 8.545

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.