Quantitative Analysis of Respiration-Related Movement for Abdominal Artery in Multiphase Hepatic CT
Quantitative Analysis of Respiration- Related Movement for Abdominal Artery in Multiphase Hepatic CT
Yang-Hsien Lin 0 2 3
Shih-Min Huang 1 2 3
Chin-Yi Huang 0 2 3
Yun-Niang Tu 0 2 3
Tzung-Chi 2 3
0 Department of Diagnostic Radiology, Peng Hu Hospital, Ministry of Health and Welfare , Peng Hu City, Taiwan,
1 Department of Radiology, China Medical University Hospital , Taichung City, Taiwan, 3. Department of Biomedical Imaging and Radiological Science, China Medical University , Taichung City, Taiwan, 4. Department of Biomedical Informatics, Asia University , Taichung City , Taiwan
2 Funding: This study was financially supported by the school project of China Medical University, Taiwan, and by Taiwan Ministry of Health and Welfare Clinical Trial and Research Center of Excellence (DOH102-TD-B-111-004). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript
3 Editor: Qinghui Zhang, University of Nebraska Medical Center , United States of America
Objectives: Respiration-induced motion in the liver causes potential errors on the measurement of contrast medium in abdominal artery from multiphase hepatic CT scans. In this study, we investigated the use of hepatic CT images to quantitatively estimate the abdominal artery motion due to respiration by optical flow method. Materials and Methods: A total of 132 consecutive patients were included in our patient cohort. We apply the optical flow method to compute the motion of the abdominal artery due to respiration. Results: The minimum and maximum displacement of the abdominal artery motion were 0.02 and 30.87 mm by manual delineation, 0.03 and 40.75 mm calculated by optical flow method, respectively. Both high consistency and correlation between the present method and the physicians' manual delineations were acquired with the regression equation of movement, y50.81x+0.25, r50.95, p,0.001. Conclusion: We estimated the motion of abdominal artery due to respiration using the optical flow method in multiphase hepatic CT scans and the motion estimations were validated with the visualization of physicians. The quantitative analysis of respiration-related movement of abdominal artery could be used for motion correction in the measurement of contrast medium passing though abdominal artery in multiphase CT liver scans.
Multiphase dynamic computed tomography (MDCT) imaging with intravenous
injection of contrast medium is essential for the diagnosis of hepatocellular
carcinoma (HCC) and in the follow-up examination for patients undergoing
treatment of liver tumors . Multiphase hepatic CT scan using intravenous
infusion of a contrast medium for the examination of the entire liver during
unenhanced phase, arterial phase, portal venous phase and delayed phase has been
proven to improve the evaluation of the hemodynamics of hepatic tumors and for
the differential diagnosis of hepatic tumors . Clinically, the automated
bolustracking programs (e.g. Smart Prep) monitoring the intervenous contrast
enhancement detects the contrast medium moving in abdominal artery on
multiphase CT liver scans . Meanwhile, the respiration-induced motion in the
liver causes potential errors on the measurement of contrast medium in
abdominal artery from multiphase hepatic CT scans . The inaccurate
Hounsfield unit measurement in moving abdominal artery resulted from
respiration was shown in Fig. 1 as an example.
The respiratory motion of the upper abdominal arteries in vascular
interventions was investigated and reported in several studies . Strategies to
compensate for breathing motion include the use of 4D imaging techniques,
gating technique and liver immobilization. The deformable registration to
calculate the trajectories of abdominal organs using 4D CT for motion estimation
has been described by Hallman et al. . The gating techniques using real-time
position management and active breathing control to limit the uncertainty
contributed by respiration for PET imaging and radiotherapy were reported in
previous studies . These studies successfully utilized the temporal
information to resolve the respiratory effects in imaging. However, studies
investigating the quantification of respiratory motion and its influences on
dynamic blood flow measurement using multiphase hepatic CT scan is still absent.
Multiphase hepatic CT imaging consists of consecutive images with contrast
medium moving in abdominal artery and possess complete temporal information.
In the present study, we investigated the use of hepatic CT images to
quantitatively estimate the abdominal artery motion due to respiration by
deformable image registration. The presented motion estimations are evaluated
and compared by the physicians visualization. The quantitative motion
estimation can be assessed in motion correction for clinical diagnosis of HCC
Materials and Methods
In this retrospective study, we included patients who underwent
contrastenhanced MDCT of the liver scan for the initial diagnosis or follow-up of
confirmed HCC between July 2013 and December 2013. A total of 132 consecutive
Fig. 1. An example to show the clinical challenge on flow measurements in the abdominal artery with respiratory motion. (A) the ROI of abdominal
artery at baseline phase of multiphase hepatic CT (B) the first scan of the monitor phase CT with ROI drawn at the baseline phase. (C) (D) (E) the
consecutive CT scans in monitor phase showing the abdominal artery moving around with the inaccurate location (red arrows) while using the static ROI.
(F) the flow measurement of contrast medium through the artery. Illustration of the misalignment of abdominal artery during the bolus tracking process.
A series of CT scan images detecting the contrast medium through the artery at the same single slice, but the respiratory motion causes the inaccurate
locations by ROIs (red arrows).
patients (84 men and 52 women; age range, 3588; mean, 63 years) were identified
and selected (Table 1). All patient identifiers were removed from the images for
the study. This study was approved by the Institutional Review Board of China
Medical University, Taiwan, and informed consent was waived due to the
retrospective nature of the study.
The 132 selected cases underwent contrast-enhanced abdominal helical 64-MDCT
examination using Smart Prep (GE Healthcare Lightspeed VCT, Waukesha,
Wisconsin), which included 639 axial CT images at abdominal artery. The region
of interest (ROI) cursor for Smart Prep was placed in the abdominal artery and
the threshold of ROI was set to 130 HU for the starting scan phase.
The protocols of the baseline and monitor phases were operated at 120 kV tube
voltage, 40 mA tube current and 0.6 second rotation time to reconstruction the
CT images with 5 mm slice thickness and 0.82 mm/pixel spatial resolution.
Monitoring delay time and inter scan time were set to 10 and 3 second
respectively in the monitor phase. The iodinated contrast media was
Note all the data were presented as mean SD or n (%).
Abbreviation HCC5hepatocellular carcinoma.
administrated at the rate of 33.5 (ml/s) with an automatic power injector
(Optivantage DH; Mallinckrodt Imaging Solutions, Hazelwood, Mo) in all
patients. The volume of contrast agent was calculated on the basis of patients
body weight, with the total range from 80 to 120 mL. Multiphase CT scan
protocol is presented in Fig. 2.
Definition of the Barycenter of Vessel Section
The data sets were randomized and interpreted independently by two radiologists
with 12 (YN Tu) and 15 (CY Huang) years of post-training experience at
interpreting CT images delineate the contour of the abdominal artery. The scans
displayed at standard window settings (width, 350 HU; level, 40 HU) for
definition the location of the artery. Manual delineation was performed on Image
J (http://rsb.info.nih.gov/ij/) and the barycentric coordinates C of the artery were
calculated as following:
where C denotes the barycenter in the section, n is the total pixel number in the
ROI, and (xi, yi) was the coordinates of the ith pixel in the ROI.
Therefore, the direction and displacement of the abdominal artery could be
calculated from successive barycenter, the equation was presented as:
where C1 and C2 are the barycenter from successive cross-section image, and (a2
a1, b2b1) was the change of barycenter in horizontal and ventricle axis.
Estimation of Motion Vector Field
Optical flow method (OFM), an image intensity gradient based method that is
capable to accurately calculate the motion vector field (MVF) from an image
sequence, estimates the local motion based upon local derivatives in the sequence
of images. We apply the 2-dimensional optical flow algorithm attributed to Horn
Fig. 2. Multiphase CT scan protocol consists baseline, monitor and scan phases. Baseline phase choose a single slice to place ROI, and monitor
phase measures and records the mean intensity of ROI while contrast injecting IV contrast. Scan phase was triggered when enhancement reach the
and Schunck  to compute the MVF of the abdominal artery which includes
anterior-posterior (AP) and lateral (LAT) displacements for each pixel in CT
images. This algorithm combined the gradient constraint with a global
smoothness term to constrain the estimated velocity field. The iterative equation,
shown as the following, is used to calculate the MVF:
where n denotes the iteration number, which was 100 typically in all estimations,
v(n) is the average displacement derived from the surrounding pixels, f is the
image intensity, and a is the weighting factor, which is empirically set at 5 for the
In this study, v represents the movement velocity (mm/sec) based on contrast
changes with interscan time for the anterior-posterior (AP) and lateral (LAT)
directions. The total movement for each voxel within the abdominal artery is
defined as pffiffiffiffiffiffi2ffiffizffiffiffiffiffiLffiffiffiffiffiffiTffiffiffi2ffi. The OFM for motion estimation was validated in
previous studies that compared the OFM measurements with simulations and
visual inspection .
Quantitative analysis of the displacement of the abdominal artery was based on
manual delineation and optical flow method on the CT monitoring phase. First,
correlation of measurements by the two observers was determined with the
Pearson correlation coefficient. Furthermore, the agreement and consistency of
two methods were evaluated in this study. In the statistical analysis, the MedCalc
software (MedCalc Software, Mariakerke, Belgium) was used to compare the
results between the manual delineation and OFM. Passing-Bablock regression and
Bland-Altman plot were used to evaluate the correlation and agreement between
the manual and automatic estimations.
Rotation time and tube current were set 0.6 second and 40 mA for image
acquisition. Radiation exposure dose is 24 mAs with a CTDIvol of 2.81 mGy for
each slice on patients. The net mean number of scans for helical CT was 5 for each
patient. Mean CTDIvol was 14.05 mGy based on SmartPrep.
Motion estimation of the abdominal artery with the ROIs was shown in Table 2.
No significant differences were observed in both AP and LAT displacements
between two radiologists. In this study, the minimum and maximum
displacement of the abdominal artery motion were 0.02 and 30.87 mm by manual
delineation, 0.03 and 40.75 mm calculated by OFM, respectively (Table 3). The
visualizations of the respiratory motion and MVF measurements in color scale for
a selected patient on the axial view are presented in Fig. 3. The correlation
between the OFM and the physicians manual delineations is shown in Fig. 4A
with the regression equation of movement, y50.81x+0.25, r50.95, p,0.001 and
the Bland-Altman plot for OFM and the physicians manual delineations is
illustrated in Fig. 4B. Both high consistency and correlation between the two
methods were acquired.
Motion studies and their synchronization with hepatic artery motion provide
insights into interpretation of HCC diagnosis from multiphase hepatic CT.
Respiratory motion during data acquisition in CT inevitably leads to artefacts
which jeopardizes the accuracy in the bolus tracking in the abdominal CT where
vessel shifts and deforms during the respiratory cycle. Shallow breathing has also
been proposed to be preferable for longer CT perfusion protocols in order to
minimize breathing artefacts for patients who have difficulties holding their
breath, especially in liver perfusion studies where tissue texture renders image
registration challenging . Smart Prep is an essential bolus tracking technique
to visually monitor the contrast enhance level for technologists or radiologists.
Despite Smart Prep ameliorate the limitation of conventional CT imaging,
numerous factors would affect the quality of enhanced hepatic CT images.
Respiratory problem is not only a problem in diagnostic radiology but also a
conspicuous issue in radiological therapy. In clinical practice, the ROI is necessary
to be placed on the detected vessel as small as possible to avoid the interference
caused by respiratory motion at monitoring phase. However, the flow information
is limited by the small ROI which is only part of abdominal artery. In this study,
an automatic method of quantitative respiration estimation using OFM is
successfully validated by physicians visualization. With quantitative respiration,
the motion correction for multiphase hepatic CT scan is achievable, which
improves the accuracy of contrast measurements and acquires the contrast
enhancement in the dynamic hepatic CT images. To our best knowledge, the
present study is among the first to report relative quantifications of respiratory
motion of the hepatic artery using multiphase hepatic CT. The quantitative results
provided the average movement of ROI was 1.983.46 mm, which was
considered small motion. However, the maximum movement was 40.75 mm
(Table 3). Even though a very small ROI was placed on the detected vessel to
avoid the interference caused by respiratory motion at monitor phase, the large
ROI motion causes that the incorrect location for abdominal artery was placed in
the corresponding ROI in other phases and the measurement of contrast
information is inaccurate. Therefore, we still suggest that the motion correction of
Note all the data were presented as mean SD and its range (mm).
Abbreviation AP5anterior to posterior view, LAT5lateral view, OFM5Optical flow method, MD5Manual delineate by physician.
Fig. 3. An example of the optical flow method estimation of motion vector field in abdominal artery on axial views. (A) the source image (B) the
target image taken 3 seconds after the source image. (C) the respiratory motion estimation represented by motion vector field overlaid on target images. The
red and yellow colors indicate the high movement in abdominal artery.
abdominal artery during the multiphase hepatic CT scan is needed to provide
accurate objective diagnostic sign of hepatic tumors.
Free breathing makes the organ shift on CT scans. Respiratory motion makes
the vessel moves dramatically on AP direction and mildly on LAT direction,
relatively (Table 2 and Table 3). With calcification aorta, the ROI measurement in
the abdominal artery occasionally fails due to respiratory motion. Currently, there
is no general consensus amongst radiologists as to the optimal bolus tracking
technique for multiphase hepatic CT examinations. Both methods constant
shallow breathing and multiple breath-hold sequenceshave been utilized in
previously published trials . As breath-hold in CT scan is impossible on
uncontrollable patients, respiratory correction via optical flow is a new insight to
improve ROI missing in bolus tracking.
Manual delineation of abdominal artery was independently performed by two
radiologists based on standard window (window level: 40; window width: 350) CT
Fig. 4. Passing-Bablock diagram and Bland-Altman plot shown the regression and consistency based on manual delineation (MD) and optical
flow method (OF) results. As MD method was the gold-standard, OF demonstrates high correlation with MD.
images. Pearson correlations illustrated the statistically insignificance of delineated
area (p,0.05) and artery displacement (p,0.05). Thus, the manual delineation
would not affect the accuracy in this study.
Multiphase hepatic CT scan for the examination of the entire liver, including
unenhanced phase, arterial phase, portal venous phase and delayed phase, was
considered a long image acquisition. Patient movement during the imaging scan is
another clinical issue besides the respiration. In present study, the motions due to
respiration and patient movements were taken into account in motion estimation
calculated by OFM. The OFM displacement is derived from the distribution of the
apparent velocities of movement in the brightness patterns between the images.
The dispersion effects from the patient movement may lead to errors in the
estimation when the different motion models are included.
We estimated the motion of abdominal artery due to respiration using the optical
flow method in multiphase hepatic CT scans and the motion estimations were
validated with the visualization of physicians. Our results could be used for
motion correction in the measurement of contrast medium passing though
abdominal artery in multiphase CT liver scans. The correct hemodynamic
information of in abdominal artery may facilitate the clinical diagnosis of HCC.
Conceived and designed the experiments: YL. Performed the experiments: YL SH
CH YT. Analyzed the data: YL TH. Contributed reagents/materials/analysis tools:
YL SH TH. Contributed to the writing of the manuscript: YL TH.
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