Prostate MRI: The Physicist's View
Eleftheria Panagiotaki1
1University College London, United Kingdom

Synopsis

This part of the course will cover different quantitative MRI methods for prostate cancer characterisation. We will mainly focus on Diffusion-weighted MRI and T2-weighted MR methods.

Highlights:
· Contrast on MR images is heavily dependent on tissue microstructure.
· Modelling/analysis can improve prostate MRI and offer insight into microstructure.
· Different quantitative analysis methods can potentially identify new biomarkers.
· Advanced MR analysis methods allow greater sensitivity, which may be required by the complex nature of cancer pathology.
Target Audience: Researchers and clinicians who are interested in an introduction to MRI techniques for prostate.
Outcome: Following this lecture, the audience will be familiar with the basic concept of the different MRI methods used for prostate cancer.
Purpose: MRI is becoming the first-line investigation for diagnosing prostate cancer, replacing the invasive biopsy. Prostate MRI utilises different imaging contrasts that we can quantify with different methods in an attempt to improve the diagnostic imaging power. In this lecture we will discuss the standard and some of the emerging MRI analysis methods for examining prostate tissue. We will focus on Diffusion-weighted Imaging (DWI) and T2 methods.

Methods

This lecture will cover the following methods:
DWI:· Apparent Diffusion Coefficient (ADC) Most DW-MRI studies have used the technique in its simplest form by calculating the apparent diffusion coefficient (ADC) to identify clinically significant tumour foci more clearly (1, 2, 3).
Intravoxel Incoherent Motion (IVIM) Le Bihan et al (4) proposed the intravoxel incoherent motion (IVIM) model to separate “pure” water diffusion effects in the tissue from pseudo-diffusion of water in the blood capillary network.
Diffusion Kurtosis Imaging (DKI) Diffusion kurtosis imaging (DKI) is a generalisation of ADC estimation (5) that quantifies the Gaussian and non-Gaussian components of the diffusion behaviour in tissue. Several studies have demonstrated greater discriminatory sensitivity of DKI for benign and cancer tissue than standard ADC (6).
Compartment models for cancer (VERDICT-MRI) The VERDICT framework (7,8) uses a three compartment tissue model designed to capture the main histological features that influence the DWI signal from in-vivo cancer tumours. The three compartments account explicitly for i) water trapped in cells, ii) water in the vascular network, and iii) interstitial water.·
B tensor Imaging (9,10) relies on novel diffusion gradient waveforms to acquire complementary pieces of diffusion information that, once combined, enable the disentanglement of various tissue properties. These measurements generalize the concept of diffusion encoding from the conventional b-value and diffusion-sensitized orientation to more versatile b-tensors.
T2· Luminal water imaging (11) uses multicomponent modeling of T2 mapping data to differentiate between T2 values of the different components within the prostate tissue.
Combined T2 and DWI· Hybrid multidimensional MR imaging (12) measures the change in ADC and T2 as a function of echo time and b value, respectively and uses these changes as a source of information about the underlying tissue microstructure. · Relaxed-VERDICT (13): VERDICT prostate model that includes compartment-specific T1/T2 relaxation to provide microstructural estimates unbiased by the relaxation tissue properties.

Acknowledgements

EP/N021967/1

References

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Proc. Intl. Soc. Mag. Reson. Med. 30 (2022)