Literature DB >> 20197602

Computer-assisted analysis of peripheral zone prostate lesions using T2-weighted and dynamic contrast enhanced T1-weighted MRI.

Pieter C Vos1, Thomas Hambrock, Jelle O Barenstz, Henkjan J Huisman.   

Abstract

In this study, computer-assisted analysis of prostate lesions was researched by combining information from two different magnetic resonance (MR) modalities: T2-weighted (T2-w) and dynamic contrast-enhanced (DCE) T1-w images. Two issues arise when incorporating T2-w images in a computer-aided diagnosis (CADx) system: T2-w values are position as well as sequence dependent and images can be misaligned due to patient movement during the acquisition. A method was developed that computes T2 estimates from a T2-w and proton density value and a known sequence model. A mutual information registration strategy was implemented to correct for patient movement. Global motion is modelled by an affine transformation, while local motion is described by a volume preserving non-rigid deformation based on B-splines. The additional value to the discriminating performance of a DCE T1-w-based CADx system was evaluated using bootstrapped ROC analysis. T2 estimates were successfully computed in 29 patients. T2 values were extracted and added to the CADx system from 39 malignant, 19 benign and 29 normal annotated regions. T2 values alone achieved a diagnostic accuracy of 0.85 (0.77-0.92) and showed a significantly improved discriminating performance of 0.89 (0.81-0.95), when combined with DCE T1-w features. In conclusion, the study demonstrated a simple T2 estimation method that has a diagnostic performance such that it complements a DCE T1-w-based CADx system in discriminating malignant lesions from normal and benign regions. Additionally, the T2 estimate is beneficial to visual inspection due to the removed coil profile and fixed window and level settings.

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Year:  2010        PMID: 20197602     DOI: 10.1088/0031-9155/55/6/012

Source DB:  PubMed          Journal:  Phys Med Biol        ISSN: 0031-9155            Impact factor:   3.609


  26 in total

1.  Simultaneous Segmentation of Prostatic Zones Using Active Appearance Models With Multiple Coupled Levelsets.

Authors:  Robert Toth; Justin Ribault; John Gentile; Dan Sperling; Anant Madabhushi
Journal:  Comput Vis Image Underst       Date:  2013-09-01       Impact factor: 3.876

2.  Computer-aided diagnosis of prostate cancer with MRI.

Authors:  Baowei Fei
Journal:  Curr Opin Biomed Eng       Date:  2017-09

3.  Elastic registration of multimodal prostate MRI and histology via multiattribute combined mutual information.

Authors:  Jonathan Chappelow; B Nicolas Bloch; Neil Rofsky; Elizabeth Genega; Robert Lenkinski; William DeWolf; Anant Madabhushi
Journal:  Med Phys       Date:  2011-04       Impact factor: 4.071

4.  Computer-aided analysis of prostate multiparametric MR images: an unsupervised fusion-based approach.

Authors:  N Betrouni; N Makni; S Lakroum; S Mordon; A Villers; P Puech
Journal:  Int J Comput Assist Radiol Surg       Date:  2015-01-22       Impact factor: 2.924

5.  PROSTATEx Challenges for computerized classification of prostate lesions from multiparametric magnetic resonance images.

Authors:  Samuel G Armato; Henkjan Huisman; Karen Drukker; Lubomir Hadjiiski; Justin S Kirby; Nicholas Petrick; George Redmond; Maryellen L Giger; Kenny Cha; Artem Mamonov; Jayashree Kalpathy-Cramer; Keyvan Farahani
Journal:  J Med Imaging (Bellingham)       Date:  2018-11-10

6.  Automated prostate cancer detection using T2-weighted and high-b-value diffusion-weighted magnetic resonance imaging.

Authors:  Jin Tae Kwak; Sheng Xu; Bradford J Wood; Baris Turkbey; Peter L Choyke; Peter A Pinto; Shijun Wang; Ronald M Summers
Journal:  Med Phys       Date:  2015-05       Impact factor: 4.071

7.  Multi-kernel graph embedding for detection, Gleason grading of prostate cancer via MRI/MRS.

Authors:  Pallavi Tiwari; John Kurhanewicz; Anant Madabhushi
Journal:  Med Image Anal       Date:  2012-12-13       Impact factor: 8.545

8.  Multimodal wavelet embedding representation for data combination (MaWERiC): integrating magnetic resonance imaging and spectroscopy for prostate cancer detection.

Authors:  P Tiwari; S Viswanath; J Kurhanewicz; A Sridhar; A Madabhushi
Journal:  NMR Biomed       Date:  2011-09-30       Impact factor: 4.044

9.  Relationship between T2 relaxation and apparent diffusion coefficient in malignant and non-malignant prostate regions and the effect of peripheral zone fractional volume.

Authors:  C J Simpkin; V A Morgan; S L Giles; S F Riches; C Parker; N M deSouza
Journal:  Br J Radiol       Date:  2013-02-20       Impact factor: 3.039

Review 10.  Computer-aided Detection of Prostate Cancer with MRI: Technology and Applications.

Authors:  Lizhi Liu; Zhiqiang Tian; Zhenfeng Zhang; Baowei Fei
Journal:  Acad Radiol       Date:  2016-04-25       Impact factor: 3.173

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