Multimodal Multiparametric Three-dimensional Image Fusion in Coronary Artery Disease: Combining the Best of Two Worlds

Abstract

Purpose: To allow for comprehensive noninvasive diagnostics of coronary artery disease (CAD) by using three-dimensional (3D) image fusion of CT coronary angiography, CT-derived fractional flow reserve (CT FFR), whole-heart dynamic 3D cardiac MRI perfusion, and 3D cardiac MRI late gadolinium enhancement (LGE).

Materials and methods: Seventeen patients (54 years ± 10 [standard deviation], one female) who underwent cardiac CT and cardiac MRI were included (combined subcohort of three prospective trials). Software facilitating multimodal 3D image fusion was developed. Postprocessing of CT data included segmentation of the coronary tree and heart contours, calculation of CT FFR values, and color coding of the coronary tree according to CT FFR. Postprocessing of cardiac MRI data included segmentation of the left ventricle (LV) in cardiac MRI perfusion and cardiac MRI LGE, co-registration of cardiac MRI to CT data, and projection of cardiac MRI perfusion and LGE values onto the high spatial resolution LV from CT.

Results: Image quality was rated as good to excellent (scores: 2.5-2.6; 3 = excellent). CT coronary angiography revealed significant stenoses in seven of 17 cases (41%). CT FFR was possible in 16 of 17 cases (94%) and showed pathologic flow in seven of 17 cases (41%), six of which coincided with cases revealing significant stenoses at CT coronary angiography. Cardiac MRI perfusion identified eight of 17 patients (47%) with hypoperfusion (ischemic burden of 17% ± 5). Cardiac MRI LGE showed myocardial scar in three of 17 cases (18%, scar burden of 7% ± 4). Conventional two-dimensional readout of CT coronary angiography and cardiac MRI resulted in eight of 17 cases (47%) with uncertain findings. Most of these divergent findings could be solved when adding information from CT FFR and 3D image fusion (six of eight, 75%).

Conclusion: Multimodal 3D cardiac image fusion is feasible and may help with comprehensive noninvasive CAD diagnostics.Supplemental material is available for this article.© RSNA, 2020.

Figure 1:

Data flow diagram illustrates CT and cardiac MRI postprocessing. From CT, segmented heart contour and color-coded coronary tree were derived. From cardiac MRI, cardiac MRI perfusion and cardiac MRI late gadolinium enhancement (LGE) images were derived, segmented, projected onto high-resolution templates of left ventricle (LV), co-registered, and color-coded. Finally, all four resulting volume data sets were rendered in common three-dimensional scene. CT FFR = CT-derived fractional flow reserve.

Figure 2a:

Images demonstrate image-based lighting and Disney’s “principled” reflectance model. (a) Multiple photographs of Andreas Grüntzig catheter laboratory were taken (exemplarily, one panoramic shot is shown). Pictures were assembled to a cube map, which projected entire 720° environment onto six faces of a cube and served as the basis for highly detailed real-world lighting in context of cardiac interventional suite. (b) To demonstrate effect, data from one nonpathologic CT coronary angiography were rendered three times with different surface qualities defined by the “principled” reflectance model. Fully reflective surface mirrors surroundings of catheter laboratory (left); glassy appearance is both reflective and translucent (right); polished red surface texture demonstrates interplay of all rendering aspects (center).

Figure 2b:

Images demonstrate image-based lighting and Disney’s “principled” reflectance model. (a) Multiple photographs of Andreas Grüntzig catheter laboratory were taken (exemplarily, one panoramic shot is shown). Pictures were assembled to a cube map, which projected entire 720° environment onto six faces of a cube and served as the basis for highly detailed real-world lighting in context of cardiac interventional suite. (b) To demonstrate effect, data from one nonpathologic CT coronary angiography were rendered three times with different surface qualities defined by the “principled” reflectance model. Fully reflective surface mirrors surroundings of catheter laboratory (left); glassy appearance is both reflective and translucent (right); polished red surface texture demonstrates interplay of all rendering aspects (center).

Figure 3:

Graph shows consistency of results from conventional two-dimensional (2D) readout, 2D readout including CT-derived fractional flow reserve (CT FFR), and three-dimensional (3D) image fusion. As is usually done in daily clinical routine, multiple imaging modalities were separately analyzed in conventional 2D readout. Areas of hypoperfusion and/or myocardial scar were attributed to one of the main coronary arteries based on their standard supply territories. Using this approach, we found controversial, imprecise, incomplete, inconsistent, and/or incorrect results in 47% of cases. Most of these divergent findings could be solved when adding information from CT FFR and 3D image fusion.

Figure 4a:

Images of a 65-year-old man (patient 6). (a) Cardiac MRI perfusion shows perfusion deficit of anterior/anterolateral wall attributed to left anterior descending artery/left circumflex artery (*). (b) CT coronary angiography. (c) Coronary angiography, left anterior oblique projection with caudal angulation. (d) Three-dimensional image fusion helped refine diagnosis: perfusion deficits (_*) were most likely caused by narrow first diagonal branch and its first, stented side branch (arrowhead). Retrospectively, denoted lesion could also be found at CT coronary angiography and coronary angiography (arrowheads in b and c, respectively). CT FFR = CT-derived fractional flow reserve, LGE = late gadolinium enhancement.

Figure 4b:

Images of a 65-year-old man (patient 6). (a) Cardiac MRI perfusion shows perfusion deficit of anterior/anterolateral wall attributed to left anterior descending artery/left circumflex artery (*). (b) CT coronary angiography. (c) Coronary angiography, left anterior oblique projection with caudal angulation. (d) Three-dimensional image fusion helped refine diagnosis: perfusion deficits (_*) were most likely caused by narrow first diagonal branch and its first, stented side branch (arrowhead). Retrospectively, denoted lesion could also be found at CT coronary angiography and coronary angiography (arrowheads in b and c, respectively). CT FFR = CT-derived fractional flow reserve, LGE = late gadolinium enhancement.

Figure 4c:

Images of a 65-year-old man (patient 6). (a) Cardiac MRI perfusion shows perfusion deficit of anterior/anterolateral wall attributed to left anterior descending artery/left circumflex artery (*). (b) CT coronary angiography. (c) Coronary angiography, left anterior oblique projection with caudal angulation. (d) Three-dimensional image fusion helped refine diagnosis: perfusion deficits (_*) were most likely caused by narrow first diagonal branch and its first, stented side branch (arrowhead). Retrospectively, denoted lesion could also be found at CT coronary angiography and coronary angiography (arrowheads in b and c, respectively). CT FFR = CT-derived fractional flow reserve, LGE = late gadolinium enhancement.

Figure 4d:

Images of a 65-year-old man (patient 6). (a) Cardiac MRI perfusion shows perfusion deficit of anterior/anterolateral wall attributed to left anterior descending artery/left circumflex artery (*). (b) CT coronary angiography. (c) Coronary angiography, left anterior oblique projection with caudal angulation. (d) Three-dimensional image fusion helped refine diagnosis: perfusion deficits (_*) were most likely caused by narrow first diagonal branch and its first, stented side branch (arrowhead). Retrospectively, denoted lesion could also be found at CT coronary angiography and coronary angiography (arrowheads in b and c, respectively). CT FFR = CT-derived fractional flow reserve, LGE = late gadolinium enhancement.

Figure 5a:

Images of a 58-year-old man (patient 7). Three-dimensional (3D) image fusion (a) alongside CT coronary angiography (b: curved multiplanar reformation of right coronary artery [RCA]). Conventional two-dimensional readout without CT-derived fractional flow reserve (CT FFR) was inconsistent: In cardiac MRI perfusion, inferior/inferoseptal hypoperfusion was found without significant lesion of RCA in CT coronary angiography or coronary angiography. In this case, CT FFR in combination with 3D image fusion helped: inferior/inferoseptal perfusion deficit (_* in a) was most likely caused by diffuse concentric narrowing of RCA (a and b, arrowheads) with pathologic CT FFR. This patient underwent a heart transplant, and diffuse vessel narrowing might have been caused by cardiac allograft vasculopathy. As illustrated in (c), diffuse lumen narrowing can lead to flow restrictions similar to those of focal stenosis of higher degree. LGE = late gadolinium enhancement.

Figure 5b:

Images of a 58-year-old man (patient 7). Three-dimensional (3D) image fusion (a) alongside CT coronary angiography (b: curved multiplanar reformation of right coronary artery [RCA]). Conventional two-dimensional readout without CT-derived fractional flow reserve (CT FFR) was inconsistent: In cardiac MRI perfusion, inferior/inferoseptal hypoperfusion was found without significant lesion of RCA in CT coronary angiography or coronary angiography. In this case, CT FFR in combination with 3D image fusion helped: inferior/inferoseptal perfusion deficit (_* in a) was most likely caused by diffuse concentric narrowing of RCA (a and b, arrowheads) with pathologic CT FFR. This patient underwent a heart transplant, and diffuse vessel narrowing might have been caused by cardiac allograft vasculopathy. As illustrated in (c), diffuse lumen narrowing can lead to flow restrictions similar to those of focal stenosis of higher degree. LGE = late gadolinium enhancement.

Figure 5c:

Images of a 58-year-old man (patient 7). Three-dimensional (3D) image fusion (a) alongside CT coronary angiography (b: curved multiplanar reformation of right coronary artery [RCA]). Conventional two-dimensional readout without CT-derived fractional flow reserve (CT FFR) was inconsistent: In cardiac MRI perfusion, inferior/inferoseptal hypoperfusion was found without significant lesion of RCA in CT coronary angiography or coronary angiography. In this case, CT FFR in combination with 3D image fusion helped: inferior/inferoseptal perfusion deficit (_* in a) was most likely caused by diffuse concentric narrowing of RCA (a and b, arrowheads) with pathologic CT FFR. This patient underwent a heart transplant, and diffuse vessel narrowing might have been caused by cardiac allograft vasculopathy. As illustrated in (c), diffuse lumen narrowing can lead to flow restrictions similar to those of focal stenosis of higher degree. LGE = late gadolinium enhancement.

Figure 6a:

Images of a 56-year-old man (patient 10). Three-dimensional image fusion (a) and conventional two-dimensional images (b: curved multiplanar CT coronary angiography reformation of left anterior descending artery [LAD], c: cardiac MRI late gadolinium enhancement [LGE]). Because of subtotal occlusion of proximal LAD (arrowheads in a and b corresponding to each other), surrounding hypoperfusion can be seen, while myocardial viability is preserved in proximal supply territory (scar transmurality ≤50%). Because of total occlusion of distal LAD, large area of transmural scar has evolved in distal supply territory (_* in a and c). Because of proximal stenosis of left circumflex artery (LCx), segmentation algorithm failed to follow course of this vessel (a, dotted line used to visualize presumed course). However, it can be seen that restricted LCx flow is causing myocardial hypoperfusion (a, lateral heart wall) but has not yet caused myocardial scarring of larger extent. CT FFR = CT-derived fractional flow reserve.

Figure 6b:

Images of a 56-year-old man (patient 10). Three-dimensional image fusion (a) and conventional two-dimensional images (b: curved multiplanar CT coronary angiography reformation of left anterior descending artery [LAD], c: cardiac MRI late gadolinium enhancement [LGE]). Because of subtotal occlusion of proximal LAD (arrowheads in a and b corresponding to each other), surrounding hypoperfusion can be seen, while myocardial viability is preserved in proximal supply territory (scar transmurality ≤50%). Because of total occlusion of distal LAD, large area of transmural scar has evolved in distal supply territory (_* in a and c). Because of proximal stenosis of left circumflex artery (LCx), segmentation algorithm failed to follow course of this vessel (a, dotted line used to visualize presumed course). However, it can be seen that restricted LCx flow is causing myocardial hypoperfusion (a, lateral heart wall) but has not yet caused myocardial scarring of larger extent. CT FFR = CT-derived fractional flow reserve.

Figure 6c:

Images of a 56-year-old man (patient 10). Three-dimensional image fusion (a) and conventional two-dimensional images (b: curved multiplanar CT coronary angiography reformation of left anterior descending artery [LAD], c: cardiac MRI late gadolinium enhancement [LGE]). Because of subtotal occlusion of proximal LAD (arrowheads in a and b corresponding to each other), surrounding hypoperfusion can be seen, while myocardial viability is preserved in proximal supply territory (scar transmurality ≤50%). Because of total occlusion of distal LAD, large area of transmural scar has evolved in distal supply territory (_* in a and c). Because of proximal stenosis of left circumflex artery (LCx), segmentation algorithm failed to follow course of this vessel (a, dotted line used to visualize presumed course). However, it can be seen that restricted LCx flow is causing myocardial hypoperfusion (a, lateral heart wall) but has not yet caused myocardial scarring of larger extent. CT FFR = CT-derived fractional flow reserve.