Prospective high-resolution respiratory-resolved whole-heart MRI for image-guided cardiovascular interventions

Abstract

Cardiovascular diseases, including arrhythmias and heart failure, are commonly treated with percutaneous procedures guided by X-ray fluoroscopy. The visualization of the targeted structures can be enhanced using preacquired respiratory-resolved anatomic data (dynamic roadmap), which is displayed as an overlay onto X-ray fluoroscopy images. This article demonstrates how dynamic roadmaps using an affine motion model can be obtained from one respiratory-resolved three-dimensional whole-heart acquisition using the previously introduced Radial Phase Encoding-Phase Ordering with Automatic Window Selection method. Respiratory motion in different regions of the heart was analyzed in 10 volunteers, and it was shown that the use of dynamic roadmaps can reduce misalignment errors from more than 10 down to less than 1.5 mm. Furthermore, the results suggest that reliable motion information can be obtained from highly undersampled images due to the advantageous undersampling properties of the radial phase encoding trajectory. Finally, results of a three-dimensional dynamic roadmap obtained from a patient before catheter ablation for atrial fibrillation treatment are presented.

FIG 1

a: RPE-PAWS acquires data simultaneously in multiple bins (red/blue bars) covering the entire amplitude A of the respiratory motion of the diaphragm. The width w is defined prior to the acquisition and the number of the acquired respiratory phases N is given by A/w. Data are acquired with an interleaved bit-reversed radial phase encoding (RPE) trajectory which leads to a homogeneous covering of k-space during the entire scan and allows for the optimal combination of k-space data from adjacent bins (numbers indicate the sampling order in each bin). A further decrease of scan time can be achieved using a partial Fourier acquisition along the radial direction (color/gray = sampled/unsampeld k-space positions). bs: The scan is successfully finished if one combination of two bins yields the desired k-space information and a complete image (C) can be reconstructed. Additional images can be reconstructed from the noncomplete (nC) bin combinations. c: After selecting a region of interest around the heart the complete image (C) is registered to the noncomplete images (nC) using a 3D affine registration. d: This yields 12 affine parameters (stars) which are fitted with a third-order polynomial (line) for the final motion model. The graphs show the affine parameters for translation, rotation, scaling and shearing as a function of A for foot-head (blue), left-right (black) and anterior-posterior (red).

FIG 2

Assessment of the effect of undersampling on the motion model accuracy. a,b: Histogram showing the amount of k-space data acquired in different respiratory bin combinations for a regular (a) and irregular (b) breathing pattern. Only an irregular breathing pattern leads to a high number of bins with a large amount of acquired k-space data sufficient for this analysis. c: The noncomplete bin combinations (>45%) of one volunteer with an irregular breathing pattern were retrospectively undersampled to 40%, 30%, …5% of k-space data (100% correspond to the complete image). The prediction of the six motion models M_40%, M_30%,…M_5% of the position of the landmarks LM were compared to the original model M_ref. d: Behavior of 4 × 3 affine parameters for different undersampling factors. Each column represents the results of the registration of one of the non complete images (nC1, nC2,…) to the complete image. The complete image represents a respiratory phase between nC4 and nC6. Each individual image shows either translation (transl), rotation (rot), scaling (scale) or shearing (shear) in anterior-posterior (AP, red), foot-head (FH, blue) and right-left (RL, black) as a function of the undersampled motion models (>45%, 40%, 30%,…5%). The scaling of the figures was set to visualize significant changes of the parameters. e: The RMS and maximum error for different motion models. The latter were obtained from retrospectively undersampled images and compared to the motion model determined from the original data. The amount of k-space data used for the noncomplete images is given in percent relative to the complete image. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]

FIG 3

a: Eight landmarks were selected at anatomically well defined positions in the heart: lateral/medial apex, lateral/medial tricuspid valve (TV), lateral/medial annulus and origin of left/right coronary arteries (OCA). b: Mean values and standard deviation (error bars) of the displacement of landmarks between end-inspiration and end-expiration (RM_insp-exp) and the model accuracy (E_MA). c,d: The grayscale image shows an oblique plane through the heart at end-inspiration. c: The white contours were obtained from the same plane but in end-expiration exhibiting significant displacements not just in the apex (dashed arrow) but also in the area of the origin of the coronary arteries (arrow heads). d: Applying the obtained 3D affine transformation to the end-expiration image corrects for these displacements. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]

FIG 4

Results of the patient scan. a,b: Affine parameters describing translation and scaling in anterior-posterior (AP), foot-head (FH) and right-left (RL) direction (star: measured, line: third polynomial fit). c,d: Snapshots showing the segmented dynamic roadmap at end-inspiration (Insp) and end-expiration (Exp) (LA/RA left/right atrium, LV/RV left/right ventricle). The dominant motion components are a translation and scaling in FH. e: The measured positions (black dots) and the positions predicted by the obtained motion model (red line) of the selected landmarks displayed on the segmented RA and LA. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]