A clinical validation study for the feasibility and reliability of three-dimensional ultrasound-ultrasound automatic image registration

Figures & data

Table 1. Influential factors and grouping.

Group	Variable influential factors	Fixed influential factors
Control	Acquiring a pair of identical data with no influential factors	Right liver anterior lobe, end expiration, prostration, volumetric angle 90°
Liver lobe	Acquiring a pair of identical data in different liver lobes	End expiration, prostration, volumetric angle 90°
Respiration	The data for the pair were separately acquired at end inspiration and end expiration	Right liver anterior lobe, prostration, volumetric angle 90°
Posture	The data for the pair were separately acquired at prostration and right anterior oblique	Right liver anterior lobe, end expiration, volumetric angle 90°
Angle	The data for the pair were separately acquired at volumetric angle 90° and 45°	Right liver anterior lobe, end expiration, prostration

Figure 1. Flow diagram of the registration validation software.

Figure 2. (A–C) Basic 3D ultrasonic images of the anterior right liver lobe in the transverse, coronal and sagittal planes. (D) Vessel tree recognition. (E) Vessel tree segmentation and extraction.

Figure 3. (A) Vessel centre lines recognition in the anterior right liver lobe. (B) Vessel centrelines extraction and bifurcations automatically marked as small spheres.

Figure 4. (A, B) Reference image and floating images with vessel centrelines and bifurcation marks, respectively. (C) Registration based on the vessel centrelines; the vessel centrelines of the reference image and the floating images primarily overlapped.

Figure 5. (A) Reference image of the anterior right liver lobe in the transverse, sagittal and coronal planes. (B) Floating image of the anterior right liver lobe in the identical acquisition conditions as A. (C) Fusion image of A and B; the vessel trees of A and B mainly overlapped, and the registration error distance was 1.23 mm. This result indicated that the image fusion of the 3D VT-based automatic registration was successful.

Figure 6. (A) Reference image of the anterior right liver lobe in the transverse, sagittal and coronal planes acquired at end inspiration. (B) Floating image of the anterior right liver lobe acquired at end expiration with the same acquisition conditions as in A. (C) Fusion image of A and B; the vessel trees of A and B did not overlap, and the registration error distance was 14.67 mm. This result indicated that the image fusion of the 3D VT-based automatic registration was a failure.

Table 2. The success rate and accuracy of 3D VT-based automatic registration in the liver lobe group.

Liver lobe group	Image pair	Success rate	Registration error distance (median, range, mm)
Left liver external lobe	10	80% (8/10)	1.55 (1.16∼4.24)
Left liver internal lobe	10	80% (8/10)	2.40 (1.44∼4.51)
Right liver posterior lobe	10	100% (10/10)	1.57 (0.76∼2.67)
Right liver anterior lobe	10	100% (10/10)	1.22 (0.57∼1.88)
p Value		0.217	0.006

Table 3. The success rate and accuracy of 3D VT-based automatic registration with other influential factors.

Influential factors	Success rate	p Value*	Registration error distance (median, range, mm)	p Value**
Control group	100% (10/10)		1.22 (0.57∼1.88)
Respiration group	70% (7/10)	0.211	2.40 (1.12∼4.71)	0.055
Posture group	40% (4/10)	0.011	3.01 (2.59∼4.49)	0.002
Angle group	30% (3/10)	0.003	0.89 (0.81∼2.55)	0.937

*Significance of differences between the success rates for other influential factors and the control group.

**Significance of differences between the registration error distances of other influential factors and the control group.