T2-adjusted computed diffusion-weighted imaging: A novel method to enhance tumour visualisation.
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Date
2016-12-01Author
Cheng, L
Blackledge, MD
Collins, DJ
Orton, MR
Jerome, NP
Feiweier, T
Rata, M
Morgan, V
Tunariu, N
Leach, MO
Koh, D-M
Type
Journal Article
Metadata
Show full item recordAbstract
PURPOSE: To introduce T2-adjusted computed DWI (T2-cDWI), a method that provides synthetic images at arbitrary b-values and echo times (TEs) that improve tissue contrast by removing or increasing T2 contrast in diffusion-weighted images. MATERIALS AND METHODS: In addition to the standard DWI acquisition protocol T2-weighted echo-planar images at multiple (≥2) echo times were acquired. This allows voxelwise estimation of apparent diffusion coefficient (ADC) and T2 values, permitting synthetic images to be generated at any chosen b-value and echo time. An analytical model is derived for the noise properties in T2-cDWI, and validated using a diffusion test-object. Furthermore, we present T2-cDWI in two example clinical case studies: (i) a patient with mesothelioma demonstrating multiple disease tissue compartments and (ii) a patient with primary ovarian cancer demonstrating solid and cystic disease compartments. RESULTS: Measured image noise in T2-cDWI from phantom experiments conformed to the analytical model and demonstrated that T2-cDWI at high computed b-value/TE combinations achieves lower noise compared with conventional DWI. In patients, T2-cDWI with low b-value and long TE enhanced fluid signal while suppressing solid tumour components. Conversely, large b-values and short TEs overcome T2 shine-through effects and increase the contrast between tumour and fluid compared with conventional high-b-value DW images. CONCLUSION: T2-cDWI is a promising clinical tool for improving image signal-to-noise, image contrast, and tumour detection through suppression of T2 shine-through effects.
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Subject
Humans
Neoplasms
Diffusion Magnetic Resonance Imaging
Phantoms, Imaging
Image Processing, Computer-Assisted
Middle Aged
Female
Male
Research team
Computational Imaging
Magnetic Resonance
Language
eng
Date accepted
2016-09-30
License start date
2016-12
Citation
Computers in biology and medicine, 2016, 79 pp. 92 - 98
Publisher
PERGAMON-ELSEVIER SCIENCE LTD