Diffusion MRI Outside the Brain
Rita G. Nunes1
1ISR-Lisboa/LARSyS and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal

Synopsis

Clinical applications of DWI outside the brain have grown significantly over the years, particularly in the detection and characterization of cancer lesions. This lecture will focus on the specific challenges of applying this technique to body imaging, presenting some of the existing strategies for dealing with these issues.

Target Audience:

Clinicians and scientists interested in applying diffusion-weighted imaging (DWI) to regions of the body other than the brain.

Outcome/Objectives:

Understand the challenges in acquiring DWI outside the brain. Become familiar with strategies for dealing with motion and minimizing image distortion and chemical shift artifacts.

Intra-scan motion effects:

DWI takes advantage of the random thermal motion of water molecules to probe the microstructure of tissues, sensitizing the images to motion of the order of micrometers, much smaller compared to typical spatial image resolutions (~2mm). The implication is that the images become extremely sensitive to any type of motion occurring during diffusion sensitization, resulting in a spatially varying phase pattern [1]. Single-shot Echo Planar Imaging (EPI) is often used to avoid k-space inconsistencies, but even then fast spatially-varying accrued phase can result in signal loss in the image [2]; this signal attenuation is likely to be interpreted as being due to diffusion, leading to biased diffusion estimates [2]. To minimize the impact of motion, it is possible to modify the structure of the diffusion sensitizing gradients so as to null specific gradient moments Mα:

$$$M_{\alpha} = \int t^{\alpha}G(t)dt$$$
where the integration in time t is performed over the duration of the diffusion sensitization module. This approach avoids phase accrual due to motion occurring with constant velocity (by nulling the 1st order gradient moment), constant acceleration (nulling the 2nd order moment), etc. Even when more complex, non-rigid motion occurs (such as seen in the heart, liver and other abdominal organs), a reduction in motion-related signal dropout can be achieved with promising results reported in liver and cardiac DWI [3,4]. The optimization of the diffusion gradient waveforms can also include known hardware constraints, such as the maximum achievable gradient amplitude and slew rate, characteristics of dominant eddy current fields and resulting concomitant fields [3,4].

Inter-scan motion effects

To extract diffusion parameters such as the apparent diffusion coefficient (ADC), it is essential that images acquired with different levels of diffusion-weighting (or b-values) or corresponding to different orientations of the diffusion gradients are spatially consistent so that a pixel-by-pixel analysis can be performed. When imaging the heart it hence becomes necessary to synchronize image acquisition to the cardiac cycle which can be done by simultaneously recording the electrocardiogram (ECG) [5]. To avoid positional inconsistencies due to respiration, options such as breath-holding or the use of respiratory trigger may be considered [6]. Avoiding the impact of respiratory movements is particularly relevant in kidney and liver imaging, while cardiac motion may impact the left lobe of the liver [7]. Minimizing peristaltic movements may be required when imaging the bowels in which case the administration of antiperistaltic drugs may be adequate [8].Despite the use of these approaches, it may still be necessary to account for residual motion, which can be done by performing image registration. Unfortunately, registration of images across different b-values is challenging due to variations in image contrast and the reduction in signal-to-noise ratio as the level of diffusion-weighting increases. A potential solution is to simultaneously carry out estimation of the motion transformations, required to ensure spatial consistency across images, and the diffusion parameters. By not decoupling the two processes (motion estimation and fitting of the diffusion model), it becomes possible to register each image to a synthetic template having the same level of diffusion-weighting, which can be generated by applying the forward model of the signal to the current estimates of the diffusion parameters [9,10].

Minimizing B0 geometric distortions and chemical shift artifacts

Body imaging often requires using large fields-of-view (FOV) for which obtaining a homogenous B0 (shimming) may be challenging; this is due to the heterogeneity of the tissues, including variable fat content (e.g. breast) and presence of air/tissue interfaces (e.g. bowels). When the organ of interest is restricted to a small region of the FOV, it is possible to optimize B0 field homogeneity only within this region, while taking care not to compromise the efficiency of fat saturation pulses acting on other parts of the FOV [11].Fat suppression is relevant in many body imaging applications, including imaging of the breast, liver and bowels. In breast imaging, adequate shimming may be challenging to achieve due to the off-isocentre position of the breasts, in which case inversion-recovery strategies for fat suppression may be worth considering [12].A consequence of having to spatially encode a very large FOV is the need to use long EPI readout windows. In the presence of a very inhomogenous B0 environment, this results in highly geometrically distorted images. An alternative is to ensure that the signal arises only from a restricted anatomical region so that spatial encoding of a much smaller FOV becomes necessary. Since the diffusion sensitization module typically uses refocusing pulses, one option is to tilt the excitation volume relative to the refocusing volume so that only regions which have been acted upon by the two types of pulses are refocused [13]. Another option is to use a selective excitation pulse [14]. To reduce image distortions, the use of alternative readout strategies may also be considered, including multi-shot EPI or readout-segmented EPI [15] although at the cost of requiring correction of phase inconsistencies between k-space segments and longer acquisition times.

Acknowledgements

RGN has received funding from the POR Lisboa 2020 program (grant number LISBOA-01-0145-FEDER-029686) and Fundação para a Ciência e Tecnologia (PTDC_EMD-EMD_29686_2017).

References

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12. Nogueira, L., Brandão, S., Nunes, R.G., Ferreira, H.A., Loureiro, J., Ramos, I. Breast DWI at 3 T: influence of the fat-suppression technique on image quality and diagnostic performance. Clinical Radiology 70(3):286-94 (2015)

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Review Chapter

16. Nunes RG, Nogueira L, Gaspar AS, Adubeiro N, Brandão S. Diffusion MRI Outside the Brain. International Conference on Medical Image Computing and Computer-Assisted Intervention. 227-249 Springer, Cham (2018)

Proc. Intl. Soc. Mag. Reson. Med. 28 (2020)