This lecture covers the advancement in the technical development of Diffusion Weighted Imaging (DWI). DWI depends upon the microscopic mobility of water. Water mobility within tissue is highly influenced by the cellular environment, allowing DWI mapping of the diffusion of molecules, mainly water, in biological tissue in vivo and non-invasively. Molecular diffusion in tissues is restricted and reflects interactions with macromolecules, fibers, membranes, etc., revealing details about tissue architecture, which could be either normal or in a diseased state. Thus, DWI has become a non-invasive tool of choice for many clinical applications from assessment of cerebral ischemia to tumor aggressiveness. A series of technical advances, such as developments of echo-planar imaging (EPI), high gradient amplitudes, multi-channel coils, navigator triggered acquisition for motion compensation, and parallel imaging, have been instrumental in extending the application of DWI to the body. However, primary challenges, such as multiple occurrence of motion, persist.
Target Audience: Scientists and clinicians interested in DWI
Outcome/Objectives:
-To familiarize users with recent technical developments in DWI acquisition for body application.
-To understand DWI signal and options on how to optimize signal-to-noise and reduce artifacts.
-To explore the benefits of using advanced non-mono exponential modelling of multi b-value DWI data.
-To understand biophysical constraints in the algorithms and realize the ultimate value of quantification in clinics.
Purpose: Diffusion imaging is the key application in MRI with its ability to study and investigate tissue architectural features on the cellular level. Recent advancements in MRI technology, such as a higher gradient system, allowed introduction of novel and more sophisticated imaging sequences and analysis. This lecture will provide an overview on the technical advancements in DWI for both acquisition and modelling as well as to better understand the DWI signal properties.
Technical Considerations for in-vivo Body DWI: The versatility and the potential of DWI both for research and clinical application have been addressed in outstanding articles by Le Bihan and others [1-8] . DWI has become very popular in the clinical domain. Developments in MR hardware and software have helped us to measure the diffusion phenomenon in body organs such as liver, breast, prostate, and whole body, to name a few, with the major applications being in oncology. The acquisition of DWI is grounded in fundamental MRI physics and requires several steps to generate parametric maps as seen in literature. The talk will cover basic principles of diffusion and improvement in hardware, from how DWI is performed in clinical settings to DWI prototype data acquisition methods along with navigator triggered acquisition for motion compensation, DWI signal and non-mono exponential data modelling.
A few examples will be illustrated to show the applicability of newer MR acquisition methods for body application, listed below:
-Reduced Field of View DWI [9] [10]
-Multishot DWI: Spin echo based distortion free diffusion using Propeller trajectory and Multiplexed Sensitivity Encoding (MUSE) [11] [12] [13]
-Reverse polarity gradient distortion correction and Gradient nonlinearity correction for ADC quantification [14] [15] [16]
-Restricted spectrum imaging [17]
-Magnetic Resonance Fingerprinting with Diffusion imaging [18] [19]
DWI signal can be optimized for different organs in the body. It is important to improve signal-to-noise for organs of interest. The number of b values, highest b value and NEX are to be considered to achieve a satisfactory DWI signal. Body organs suffer from artefacts resulting from bulk motion to susceptibility, and artefact reduction is key for DWI clinical protocols.
Tissue properties can be extracted from DWI data by fitting a model to the measured data with each voxel. DWI models provide cellular directional organization leading to diffusion anisotropy and can measure diffusion and perfusion by non-mono-exponential modelling. It is a basic prerequisite to understand biophysical constraints in the algorithms.
- Non-Gaussian intravoxel incoherent motion data modelling will be illustrated by example.
The ultimate value of quantification is the use of quantitative imaging biomarkers in clinics.
Clinical Application of in-vivo DWI: DWI yields quantitative imaging biomarkers that can be helpful in the clinical oncology realm from tumor assessment to monitoring response to treatment.
Clinical applications will be discussed in the next lecture.
1. Lima, M. and Le Bihan, D.Clinical intravoxel incoherent motion and diffusion MR imaging: past, present, and future. 2015. 278(1): p. 13-32.
2. Le Bihan, D., et al., Artifacts and pitfalls in diffusion MRI. J Magn Reson Imaging, 2006. 24(3): p. 478-88.
3. Le Bihan, D., et al., Imaging of diffusion and microcirculation with gradient sensitization: design, strategy, and significance. J Magn Reson Imaging, 1991. 1(1): p. 7-28.
4. Padhani, A.R., et al., Diffusion-weighted magnetic resonance imaging as a cancer biomarker: consensus and recommendations. Neoplasia, 2009. 11(2): p. 102-25.
5. Partridge, S.C., et al., Diffusion-weighted breast MRI: Clinical applications and emerging techniques. J Magn Reson Imaging, 2017. 45(2): p. 337-355.
6. Shukla‐Dave, A., et al., Quantitative imaging biomarkers alliance (QIBA) recommendations for improved precision of DWI and DCE‐MRI derived biomarkers in multicenter oncology trials. 2018.
7. Wu, W. and K.L.J.J.o.M.R.I. Miller, Image formation in diffusion MRI: a review of recent technical developments. 2017. 46(3): p. 646-662.
8. Foltz, W.D., et al., Readout-segmented echo-planar diffusion-weighted imaging improves geometric performance for image-guided radiation therapy of pelvic tumors. 2015. 117(3): p. 525-531.
9. Wilm, B.J., et al., Reduced field‐of‐view MRI using outer volume suppression for spinal cord diffusion imaging. 2007. 57(3): p. 625-630.
10. Zaharchuk, G., et al., Reduced field-of-view diffusion imaging of the human spinal cord: comparison with conventional single-shot echo-planar imaging. 2011. 32(5): p. 813-820.
11. Pipe, J.G., V.G. Farthing, et al., Multishot diffusion‐weighted FSE using PROPELLER MRI. 2002. 47(1): p. 42-52.
12. Deng, J., et al., Multishot diffusion-weighted PROPELLER magnetic resonance imaging of the abdomen. 2006. 41(10): p. 769-775.
13. Chen, N., et al., A robust multi-shot scan strategy for high-resolution diffusion weighted MRI enabled by multiplexed sensitivity-encoding (MUSE). 2013. 72: p. 41-47.
14. Bodammer, N., et al., Eddy current correction in diffusion‐weighted imaging using pairs of images acquired with opposite diffusion gradient polarity. 2004. 51(1): p. 188-193.
15. Morgan, P.S., et al., Correction of spatial distortion in EPI due to inhomogeneous static magnetic fields using the reversed gradient method. 2004. 19(4): p. 499-507.
16. Newitt, D.C., et al., Gradient nonlinearity correction to improve apparent diffusion coefficient accuracy and standardization in the american college of radiology imaging network 6698 breast cancer trial. 2015. 42(4): p. 908-919.
17. Rakow-Penner, R., et al., Novel technique for characterizing prostate cancer utilizing MRI restriction spectrum imaging: proof of principle and initial clinical experience with extraprostatic extension. 2015. 18(1): p. 81.
18. Chen, Y., et al., MR fingerprinting for rapid quantitative abdominal imaging. 2016. 279(1): p. 278-286.
19. Yu, A.C., et al., Development of a combined MR fingerprinting and diffusion examination for prostate cancer. 2017. 283(3): p. 729-738.