Motion Correction with a Model Target (MoCoMo): A universal approach for quantitative MRI?
Fotios Tagkalakis1, Kanishka Sharma1, Susmita Basak1, Christopher Kelly2, David Shelley3, Irvin Teh2, Jehill Parikh4, Peter Thelwall4, Neil Sheerin5, and Steven Sourbron1

1Leeds Imaging Biomarkers Group, Biomedical Imaging Science Department, University of Leeds, Leeds, United Kingdom, 2Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, United Kingdom, 3Advanced Imaging Centre, University of Leeds, Leeds, United Kingdom, 4Newcastle Magnetic Resonance Centre, Newcastle University, Newcastle, United Kingdom, 5Institute of Cellular Medicine, Newcastle University, Newcastle, United Kingdom


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.


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 liver(1), heart(2) and kidney(3)– but in principle applies to any MRI contrast mechanism.
The 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.


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:

  1. in T1, T2-mapping the difference between corrected and uncorrected parameter maps is negligible;
  2. 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;
  3. in heavy breathing DCE and DTI data, MoCoMo shows a major improvement on anatomical delineation and precision of parameter maps.


Motion-correction algorithms should be applied to all data indistinctively, regardless of the amount of motion present. Hence, it is critical to verify that motion-correction does not induce deformations in cases where no actual motion is present. However, most free-form deformation models risk fitting to artefacts that are not incorporated in the signal model, producing poor outcomes. The results suggest that there is no such major concern (Figs.2,3). In cases of moderate or significant motion (Figs.4,5), MoCoMo effectively eliminates motion-induced blurring, depicting clearly the expected anatomical delineation between cortex and medulla. The effect is entirely consistent between DCE and DTI, two very different contrast mechanisms – suggesting that MoCoMo can be a harmonised motion correction approach effective across all quantitative MRI modalities.


This preliminary study suggests that MoCoMo is a suitable candidate for universal motion correction across all functional MRI modalities.


One DCE-MRI dataset kindly provided by Prof. Patrick Mark and Dr. Alexandra Radjenovic, University of Glasgow. The BEAt-DKD project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 115974. This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA with JDRF.


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Figure 1. Illustration of the methods 1(a) Motion Correction with a Model Target (MoCoMo) – basic architecture. Initialization: for the first iteration the N registered frames were initialized to the N Acquired frames. 1(b) Table of acquisition parameters for each sequence processed.

Figure 2. T1 maps Top row: T1 maps for 3 different volunteers before registration, Bottom row: T1 maps after MoCoMo registration

Figure 3. T2 maps Top row: T2 maps for 3 different volunteers before registration, Bottom row: T2 maps after MoCoMo registration

Figure 4. Fractional Anisotropy maps Top row: FA maps for 3 different volunteers before registration, Bottom row: FA maps after MoCoMo registration

Figure 5. Renal blood flow maps Top row: RBF maps for 3 different volunteers before registration, Bottom row: RBF maps after MoCoMo registration. The units for the maps are in ml/s-1/ml.

Proc. Intl. Soc. Mag. Reson. Med. 27 (2019)