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Improved motion correction using image registration based on variational synthetic image estimation: application to inline t1 mapping of myocardium

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This undesired motion compromises the accuracy of pixel-by-pixel T1 estimation... Unfortunately, registration of MOLLI images is particularly difficult as image contrast changes significantly over time (Figure 1)... In this work, we propose a novel registration algorithm based on estimating motion-free synthetic images presenting similar contrast to original data by solving a variational energy minimization problem... Robust motion correction is achieved by registering synthetic images to corresponding MOLLI frames. 4 volunteers and 9 patients were scanned (Siemens MAGNETOM Avanto/Espree/Verio, 42/18 pre/post-contrast series)... Given N frames of MOLLI images with inversion time TI, synthetic image is defined as a function to minimize the energy functional defined in Figure 2... To estimate the initial signal image, few MOLLI frames with similar contrast are selected and an initial registration and T1 fitting is performed... Effectiveness was first evaluated by visual reading... All datasets were classified into two categories: 21 ‘no significant motion’ and 39 ‘with significant motion’... A frame-to-frame registration between images with largely varying contrast can lead to unrealistic deformation (Figure 3), which was found in 40 cases among the whole cohort (67%), while proposed approach was robust against contrast changes... For every case, two frames with motion were selected and their myocardium was manually delineated... Two measures are computed (Table 1): Dice ratio and MBE (minimal distance between endo/epi contours of two frames)... Typical performance is illustrated in Figure 4-5.

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Example of MOLLI motion correction. (a-c) Original MOLLI images with contour overlay. Myocardium is still in this case. (d-f) Motion correction results by directly applying the non-rigid registration. Largely varying contrast causes the failure of registration, as shown in (d-e). (g-i) Motion correction based on synthetic image estimation.
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Figure 3: Example of MOLLI motion correction. (a-c) Original MOLLI images with contour overlay. Myocardium is still in this case. (d-f) Motion correction results by directly applying the non-rigid registration. Largely varying contrast causes the failure of registration, as shown in (d-e). (g-i) Motion correction based on synthetic image estimation.

Mentions: Effectiveness was first evaluated by visual reading. All datasets were classified into two categories: 21 ‘no significant motion’ and 39 ‘with significant motion’. A frame-to-frame registration between images with largely varying contrast can lead to unrealistic deformation (Figure 3), which was found in 40 cases among the whole cohort (67%), while proposed approach was robust against contrast changes. Quantitative validation was performed on all cases with discernible motion (reconstructed in-plane resolution: 1.67 ~ 2.08 mm2). For every case, two frames with motion were selected and their myocardium was manually delineated. Two measures are computed (Table 1): Dice ratio and MBE (minimal distance between endo/epi contours of two frames). Typical performance is illustrated in Figure 4-5.


Improved motion correction using image registration based on variational synthetic image estimation: application to inline t1 mapping of myocardium
Example of MOLLI motion correction. (a-c) Original MOLLI images with contour overlay. Myocardium is still in this case. (d-f) Motion correction results by directly applying the non-rigid registration. Largely varying contrast causes the failure of registration, as shown in (d-e). (g-i) Motion correction based on synthetic image estimation.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3106463&req=5

Figure 3: Example of MOLLI motion correction. (a-c) Original MOLLI images with contour overlay. Myocardium is still in this case. (d-f) Motion correction results by directly applying the non-rigid registration. Largely varying contrast causes the failure of registration, as shown in (d-e). (g-i) Motion correction based on synthetic image estimation.
Mentions: Effectiveness was first evaluated by visual reading. All datasets were classified into two categories: 21 ‘no significant motion’ and 39 ‘with significant motion’. A frame-to-frame registration between images with largely varying contrast can lead to unrealistic deformation (Figure 3), which was found in 40 cases among the whole cohort (67%), while proposed approach was robust against contrast changes. Quantitative validation was performed on all cases with discernible motion (reconstructed in-plane resolution: 1.67 ~ 2.08 mm2). For every case, two frames with motion were selected and their myocardium was manually delineated. Two measures are computed (Table 1): Dice ratio and MBE (minimal distance between endo/epi contours of two frames). Typical performance is illustrated in Figure 4-5.

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Please rate it.

This undesired motion compromises the accuracy of pixel-by-pixel T1 estimation... Unfortunately, registration of MOLLI images is particularly difficult as image contrast changes significantly over time (Figure 1)... In this work, we propose a novel registration algorithm based on estimating motion-free synthetic images presenting similar contrast to original data by solving a variational energy minimization problem... Robust motion correction is achieved by registering synthetic images to corresponding MOLLI frames. 4 volunteers and 9 patients were scanned (Siemens MAGNETOM Avanto/Espree/Verio, 42/18 pre/post-contrast series)... Given N frames of MOLLI images with inversion time TI, synthetic image is defined as a function to minimize the energy functional defined in Figure 2... To estimate the initial signal image, few MOLLI frames with similar contrast are selected and an initial registration and T1 fitting is performed... Effectiveness was first evaluated by visual reading... All datasets were classified into two categories: 21 ‘no significant motion’ and 39 ‘with significant motion’... A frame-to-frame registration between images with largely varying contrast can lead to unrealistic deformation (Figure 3), which was found in 40 cases among the whole cohort (67%), while proposed approach was robust against contrast changes... For every case, two frames with motion were selected and their myocardium was manually delineated... Two measures are computed (Table 1): Dice ratio and MBE (minimal distance between endo/epi contours of two frames)... Typical performance is illustrated in Figure 4-5.

No MeSH data available.