Literature DB >> 22819178

Comparison of different mathematical models of diffusion-weighted prostate MR imaging.

Michael Quentin1, Dirk Blondin, Janina Klasen, Rotem Shlomo Lanzman, Falk-Roland Miese, Christian Arsov, Peter Albers, Gerald Antoch, Hans-Jörg Wittsack.   

Abstract

PURPOSE: To evaluate which mathematical model (monoexponential, biexponential, statistical, kurtosis) fits best to the diffusion-weighted signal in prostate magnetic resonance imaging (MRI).
MATERIALS AND METHODS: 24 prostate 3-T MRI examinations of young volunteers (YV, n=8), patients with biopsy proven prostate cancer (PC, n=8) and an aged matched control group (AC, n=8) were included. Diffusion-weighted imaging was performed using 11 b-values ranging from 0 to 800 s/mm(2).
RESULTS: Monoexponential apparent diffusion coefficient (ADC) values were significantly (P<.001) lower in the peripheral (PZ) zone (1.18±0.16 mm(2)/s) and the central (CZ) zone (0.73±0.13 mm(2)/s) of YV compared to AC (PZ 1.92±0.17 mm(2)/s; CZ 1.35±0.21 mm(2)/s). In PC ADC(mono) values (0.61±0.06 mm(2)/s) were significantly (P<.001) lower than in the peripheral of central zone of AC. Using the statistical analysis (Akaike information criteria) in YV most pixels were best described by the biexponential model (82%), the statistical model, respectively kurtosis (93%) each compared to the monoexponential model. In PC the majority of pixels was best described by the monoexponential model (57%) compared to the biexponential model.
CONCLUSION: Although a more complex model might provide a better fitting when multiple b-values are used, the monoexponential analyses for ADC calculation in prostate MRI is sufficient to discriminate prostate cancer from normal tissue using b-values ranging from 0 to 800 s/mm(2).
Copyright © 2012 Elsevier Inc. All rights reserved.

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Year:  2012        PMID: 22819178     DOI: 10.1016/j.mri.2012.04.025

Source DB:  PubMed          Journal:  Magn Reson Imaging        ISSN: 0730-725X            Impact factor:   2.546


  20 in total

1.  Increased signal intensity of prostate lesions on high b-value diffusion-weighted images as a predictive sign of malignancy.

Authors:  Michael Quentin; Lars Schimmöller; Christian Arsov; Robert Rabenalt; Gerald Antoch; Peter Albers; Dirk Blondin
Journal:  Eur Radiol       Date:  2013-08-31       Impact factor: 5.315

2.  Evaluation of fitting models for prostate tissue characterization using extended-range b-factor diffusion-weighted imaging.

Authors:  Fredrik Langkilde; Thiele Kobus; Andriy Fedorov; Ruth Dunne; Clare Tempany; Robert V Mulkern; Stephan E Maier
Journal:  Magn Reson Med       Date:  2017-07-17       Impact factor: 4.668

3.  Computed diffusion-weighted imaging of the prostate at 3 T: impact on image quality and tumour detection.

Authors:  Andrew B Rosenkrantz; Hersh Chandarana; Nicole Hindman; Fang-Ming Deng; James S Babb; Samir S Taneja; Christian Geppert
Journal:  Eur Radiol       Date:  2013-06-12       Impact factor: 5.315

4.  Segmented diffusion-weighted imaging of the prostate: Application to transperineal in-bore 3T MR image-guided targeted biopsy.

Authors:  Andriy Fedorov; Kemal Tuncali; Lawrence P Panych; Janice Fairhurst; Elmira Hassanzadeh; Ravi T Seethamraju; Clare M Tempany; Stephan E Maier
Journal:  Magn Reson Imaging       Date:  2016-05-27       Impact factor: 2.546

5.  Feasibility study of computed vs measured high b-value (1400 s/mm²) diffusion-weighted MR images of the prostate.

Authors:  Leonardo K Bittencourt; Ulrike I Attenberger; Daniel Lima; Ralph Strecker; Andre de Oliveira; Stefan O Schoenberg; Emerson L Gasparetto; Daniel Hausmann
Journal:  World J Radiol       Date:  2014-06-28

6.  Application of the diffusion kurtosis model for the study of breast lesions.

Authors:  Luísa Nogueira; Sofia Brandão; Eduarda Matos; Rita Gouveia Nunes; Joana Loureiro; Isabel Ramos; Hugo Alexandre Ferreira
Journal:  Eur Radiol       Date:  2014-03-22       Impact factor: 5.315

7.  Statistical assessment of bi-exponential diffusion weighted imaging signal characteristics induced by intravoxel incoherent motion in malignant breast tumors.

Authors:  Jing Yuan; Oi Lei Wong; Gladys G Lo; Helen H L Chan; Ting Ting Wong; Polly S Y Cheung
Journal:  Quant Imaging Med Surg       Date:  2016-08

8.  The histogram analysis of diffusion-weighted intravoxel incoherent motion (IVIM) imaging for differentiating the gleason grade of prostate cancer.

Authors:  Yu-Dong Zhang; Qing Wang; Chen-Jiang Wu; Xiao-Ning Wang; Jing Zhang; Hui Liu; Xi-Sheng Liu; Hai-Bin Shi
Journal:  Eur Radiol       Date:  2014-11-28       Impact factor: 5.315

9.  Evaluation of breast cancer using intravoxel incoherent motion (IVIM) histogram analysis: comparison with malignant status, histological subtype, and molecular prognostic factors.

Authors:  Gene Young Cho; Linda Moy; Sungheon G Kim; Steven H Baete; Melanie Moccaldi; James S Babb; Daniel K Sodickson; Eric E Sigmund
Journal:  Eur Radiol       Date:  2015-11-28       Impact factor: 5.315

10.  Comparison of fitting methods and b-value sampling strategies for intravoxel incoherent motion in breast cancer.

Authors:  Gene Young Cho; Linda Moy; Jeff L Zhang; Steven Baete; Riccardo Lattanzi; Melanie Moccaldi; James S Babb; Sungheon Kim; Daniel K Sodickson; Eric E Sigmund
Journal:  Magn Reson Med       Date:  2014-10-09       Impact factor: 4.668

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