Fotios Tagkalakis^{1}, Kanishka Sharma^{1}, Susmita Basak^{1}, Christopher Kelly^{2}, David Shelley^{3}, Irvin Teh^{2}, Jehill Parikh^{4}, Peter Thelwall^{4}, Neil Sheerin^{5}, and Steven Sourbron^{1}

Motion correction with a model-target (MoCoMo) has been used in DCE-MRI to overcome the problem of changes in image contrast, but the method applies in principle to any other quantitative MRI method. The aim of this study is to demonstrate this hypothesis by applying the algorithm to renal DCE, DTI, T1 and T2-mapping in human subjects. The results show that MoCoMo is effective in removing even major motion effects in all 4 modalities and does not affect data where no motion is present. We conclude that MoCoMo is a suitable candidate for universal motion correction across all functional MRI modalities.

Introduction

Motion correction in quantitative MRI is challenging due to changes in contrast introduced by varying parameters such as inversion time, echo time, diffusion weighting or contrast agent concentration. An elegant solution is Motion Correction with a Model Target (MoCoMo), which utilizes the MRI signal model to build registration targets of variable contrast, effectively performing a joint optimisation of the deformation fields and the quantitative MRI maps. This approach has been applied in DCE-MRI of liverThe aim of this study is to investigate whether MoCoMo is an effective motion correction approach across multiple quantitative MRI modalities. The hypothesis is tested in DCE, DTI, T1 and T2 renal mapping.

Motion Correction with a Model Target (MoCoMo)

MoCoMo’s basic principle is illustrated in Fig 1(a). Given a set of N acquired frames, measured with N sets of imaging parameters, a new set of N registered frames is created by iterating two steps: (1) the current set of N registered frames is fitted pixel-by-pixel to the signal model, producing N fitted frames; (2) these are used as targets for a frame-by-frame registration of the N acquired frames, producing a new set of N registered frames. In the first iteration the N registered frames are initialised to the N acquired frames.

Image acquisition

Test data were drawn from renal pilot studies for 4 different contrast mechanisms, measured in 3 centres, on 2 different 3T scanners (Siemens, Philips). Fig.1(b) presents the basic acquisition parameters. 3 datasets from different subjects were randomly selected for each of the 4 contrast mechanisms.

Image processing

Data were processed by the same
observer using in-house built Matlab algorithms. 2D registrations of a single
slice were performed for this study.
Pixel-by-pixel model fitting was
performed with a linear 2-compartment model fit for DCE-MRI^{(3)}, a
mono-exponential recovery for T1^{(4)}, a mono-exponential
decay for T2^{(5)}, and a diffusion tensor
model for DTI^{(6)}. For the DCE, an
arterial input function was selected manually inside the abdominal aorta on the
transverse slice.
Frame-by-frame image co-registration
was implemented in Elastix^{(7)} following a
multi-resolution scheme, using a mutual information metric with adaptive
stochastic gradient descent^{(8)}. Each registration was performed
with a combination of rigid body registration (Euler, to correct for gross
motion) and free-form registration (B-spline, for smaller deformations). The
control points for B-spline were placed 100 mm apart for the first iteration,
and reduced stepwise to 25mm over the subsequent iterations. 3 iterations were
performed in total.

Evaluation of results

The effects of MoCoMo were evaluated by visual comparison of parameter maps calculated with and without motion correction. For each contrast mechanism, evaluation was based on the parameter most sensitive to motion: T1, T2, Fractional Anisotropy (FA) from DTI and Blood Flow (BF) from DCE.

Results

Fig.2-5 show the results without motion correction (top row) and with motion correction (bottom row) for each of the four contrast mechanisms (T1, T2, FA, BF respectively), for each subject (left, middle, right column). Visual inspection of the T1 and T2-mapping data acquired during breath hold showed image artefacts due to imperfect rephrasing in the readout, but little to no motion-induced displacements. The free-breathing DTI and DCE data showed heavy breathing motion, while the shallow breathing DCE data showed low-amplitude breathing motion.

The results show that:

- in T1, T2-mapping the difference between corrected and uncorrected parameter maps is negligible;
- in shallow breathing DCE data (Fig.5 columns2-3), MoCoMo produces sharpened parameter maps with increased BF values in the renal cortex, consistent with removal of motion-induced partial volume effects;
- in heavy breathing DCE and DTI data, MoCoMo shows a major improvement on anatomical delineation and precision of parameter maps.

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