Objective: Respiratory motion correction is necessary to quantitative analysis of liver contrast-enhance
ultrasound (CEUS) image sequences. However, traditionally manual selecting reference image would affect the
accuracy of the respiratory motion correction. Methods First, the original high-dimensional ultrasound gray-level
image data was mapped into a two-dimensional space by using Laplacian Eigenmaps (LE). Then, the cluster
analysis was adopted using K-means, and the optimal ultrasound reference image could be gotten for respiratory
motion correction. Finally, this proposed method was validated on 18 CEUS cases of VX2 tumor in rabbit liver,
and the effectiveness of this method was demonstrated. Results After correction, the time-intensity curves
extracted from the region of interest of CEUS image sequences became smoother. Before correction, the average of
total mean structural similarity (TMSSIM) and the average of mean correlation coefficient (MCC) from image
sequences were 0.45±0.11 and 0.67±0.16, respectively. After correction, the two parameters were increased
obviously(P<0.001), and were 0.59±0.11 and 0.81±0.11, respectively. The average of deviation valve (DV) from
image sequences before correction was 92.16±18.12. After correction, the average was reduced to one-third of the
original value. Conclusions: The proposed respiratory motion method could improve the accuracy of the
quantitative analysis of CEUS by using the reference image based on the traditionally manual selection. This
method is operated simply and has a potential in clinical application.
The aim of this study was to evaluate the diagnostic performance of virtual touch tissue imaging (VTI) based on ARFI elastography technique for differentiating malignant from benign thyroid nodules. One hundred pathologically proven thyroid nodules (80 benign, 20 malignant) in 76 participants were recruited in this study. The likelihood of malignancy in the light of VTI features was scored into 6 levels by one experienced sonogist who was blinded to pathological results. In addition, the mean gray value within the thyroid nodule (mGVTN) derived from VTI image was calculated for quantitative analysis. Receiver-operating characteristic curve (ROC) analyses were performed to assess the diagnostic performance of VTI score and mGVTN. The frequency of malignant nodules (11/20) classified between VTI levels 4 to 6 was more than that of benign nodules (6/80) (p <0.001). The mGVTN of malignant nodules (45±23) was significantly lower than that of benign nodules (115±58) (p <0.001), where the range of mGVTN was from 0 to 255. The sensitivity, specificity, accuracy, positive predictive value and negative predictive value of VTI score were 55.0%, 92.5%, 85.0%, 64.7% and 89.2%, respectively. For mGVTN, those values were 70.0%, 90.0%, 86.0%, 63.6% and 92.3%, respectively. In conclusion, the VTI image seemed to be an effective tool in the differential diagnosis of thyroid nodules. The diagnosis performance of mGVTN was almost consistent with that of VTI score, which indicated that the mGVTN as a quantitative parameter might facilitate doctors diagnosing malignant thyroid nodules by VTI.
To reduce the effects of respiratory motion in the quantitative analysis based on liver contrast-enhanced ultrasound (CEUS) image sequencesof single mode. The image gating method and the iterative registration method using model image were adopted to register liver contrast-enhanced ultrasound image sequences of single mode. The feasibility of the proposed respiratory motion correction method was explored preliminarily using 10 hepatocellular carcinomas CEUS cases. The positions of the lesions in the time series of 2D ultrasound images after correction were visually evaluated. Before and after correction, the quality of the weighted sum of transit time (WSTT) parametric images were also compared, in terms of the accuracy and spatial resolution. For the corrected and uncorrected sequences, their mean deviation values (mDVs) of time-intensity curve (TIC) fitting derived from CEUS sequences were measured. After the correction, the positions of the lesions in the time series of 2D ultrasound images were almost invariant. In contrast, the lesions in the uncorrected images all shifted noticeably. The quality of the WSTT parametric maps derived from liver CEUS image sequences were improved more greatly. Moreover, the mDVs of TIC fitting derived from CEUS sequences after the correction decreased by an average of 48.48±42.15. The proposed correction method could improve the accuracy of quantitative analysis based on liver CEUS image sequences of single mode, which would help in enhancing the differential diagnosis efficiency of liver tumors.
Respiratory motion affects the accurate quantification of the hepatic fusion, which does not benefit the early detection
and treatment of hepatic cancers. A new strategy based on template matching is proposed to correct respiratory motion in
the free-breathing time-series contrast-enhanced ultrasound (CEUS) images. Ultrasound machines generally have a
dual-display feature of contrast and tissue windows under contrast-enhanced mode. The tissue window is used to track
the targeted tumor in the contrast window. Therefore, the registration of contrast images is first achieved by the
registration of the corresponding tissue images due to the low variation of grey level in the tissue image. Then, a simple
double-selection method is proposed to select the similar images from a large number of successive matched images via
the global and local threshold setting. Finally, the motion-corrected contrast images can be acquired by using the position
mapping. This strategy was tested on 4 free-breathing CEUS image sequences using the sum of absolute differences
metric. Results showed that the time-intensity curves could be extracted more accurately with this strategy. Moreover,
the quality of curve fitting and the corresponding parametric imaging computed on the motion-corrected sequences was
improved significantly. In conclusion, the image-based strategy can quickly correct the respiratory motion in CEUS
image sequences, which is potentially suitable for the local quantification of hepatic perfusion studies.
Factor analysis is an efficient technique to the analysis of dynamic structures in medical image sequences and recently
has been used in contrast-enhanced ultrasound (CEUS) of hepatic perfusion. Time-intensity curves (TICs) extracted by
factor analysis can provide much more diagnostic information for radiologists and improve the diagnostic rate of focal
liver lesions (FLLs). However, one of the major drawbacks of factor analysis of dynamic structures (FADS) is
nonuniqueness of the result when only the non-negativity criterion is used. In this paper, we propose a new method of
replace-approximation based on apex-seeking for ambiguous FADS solutions. Due to a partial overlap of different
structures, factor curves are assumed to be approximately replaced by the curves existing in medical image sequences.
Therefore, how to find optimal curves is the key point of the technique. No matter how many structures are assumed, our
method always starts to seek apexes from one-dimensional space where the original high-dimensional data is mapped.
By finding two stable apexes from one dimensional space, the method can ascertain the third one. The process can be
continued until all structures are found. This technique were tested on two phantoms of blood perfusion and compared to
the two variants of apex-seeking method. The results showed that the technique outperformed two variants in
comparison of region of interest measurements from phantom data. It can be applied to the estimation of TICs derived
from CEUS images and separation of different physiological regions in hepatic perfusion.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.