Moreover, operation time of PPI decreased 50.4 ± 0.1% in one personal computer (Asus laptop, Shanghai, China) because lots of invalid data were eliminated.
Parametric Perfusion Imaging with Single-pixel Resolution and High Signal to Clutter Ratio Diya Wang, Xuan Yang, Mengnan Xiao, Hong Hu, Hui Zhong, Mingxi Wan* The Key Laboratory of Biomedical Information Engineering of Ministry of Education Department of Biomedical Engineering, School of Life Science and Technology, Xi’an Jiaotong University, Xi’ an 710049, P. R. China Abstract—Parametric perfusion imaging (PPI) based on time-intensity-curves (TICs) can quantify and depict the spatial distribution of tumor perfusion information in liver cancer research. However, accurate diagnosis of PPI is seriously disturbed by the fluctuations of TICs and decreases of coded threshold induced by no-microbubble (MB) regions. Such disturbances, particularly the decrease of signal-to-clutter ratio (SCR) of TICs, are further exacerbated during selecting single-pixel region-of-interest (ROI) to obtain PPI with highest resolution. The objective of this study was to accurately obtain PPI with single-pixel resolution at the smallest ROI by the valid TICs filtration and the SCR enhancement of TICs. First, single-pixel TICs were obtained from the dynamic contrast images of a patient with gallbladder carcinoma liver metastasis. Next, these TICs in no-MB regions were then eliminated due to their low correlation with MB regions. Whereas the valid reserved TICs were filtered by the detrended fluctuation analysis to improve their SCR. Color-coded images were finally obtained based on the perfusion parameters extracted from the reserved TICs. After TICs filtration and denoising, the disturbances from no-MB regions were effectively removed; SCR of TICs was enhanced by 5.49 ± 0.34 dB; and operation time of PPI decreased 50.4 ± 0.1% because lots of invalid data were eliminated. Hot spots distribution and perfusion characteristic of neovascularization in the liver metastases were accurately distinguished and depicted by combining PPI with single-pixel resolution, especially the wash in time and wash out time. Besides, edge features of the invasion area and liver were clearly described without extra segmentation algorithm. It can contribute to accurately make a clinical decision in the liver cancer diagnoses by the single-pixel resolution PPI with comprehensive functional perfusion information. Keywords-parametric perfusion imaging; single pixel resolution; time-intensity curve; filtration; detrended fluctuation analysis; signal-to-clutter ratio.
I.
INTRODUCTION
Due to the strong nonlinear acoustic response of microbubbles (MBs) under insonation, MB contrast agents are frequently used in contrast-enhanced ultrasound (CEUS), which can improve the visualization of microvessels with slow blood flow[1]. Moreover, CEUS perfusion, permitting tissue hemodynamic imaging, can provide significant physiologic and pathologic information [2]. Dynamic CEUS imaging also is an important tool for evaluating and monitoring the tissue pathologic processes [3], and makes it possible to quantify the CEUS perfusion. As a specific-CEUS imaging technique, parametric perfusion imaging (PPI) formed from the dynamic
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CEUS image loops has been proposed and adopted in the clinic, which can quantify and depict the spatial distribution of tissue perfusion information and be of diagnostic value to a wide range of diseases including in liver disease [4]. Currently, PPI has been applied in the identification and classification of chronic hepatitis and liver cirrhosis [5], and in the detection and characterization of the liver tumors and cancer liver metastasis [6]. Indeed, a correct differentiation between benign and malignant focal liver tumors is a crucial index for an accurate diagnosis and appropriate therapeutic planning of liver disease [7]. However, vascularities and microvessel system in liver with complex hemodynamic are a challenge for the accurate diagnosis using PPI. Compared with ignoring the inhomogeneity of blood perfusion in the few regions-of-interest (ROIs) at large sizes (e.g., a ROI of 100 mm2 was placed over the portal vein) in conventional perfusion quantification [5], PPI can quantify blood perfusion in the whole imaging plane. This plane is divided into small equal-sized ROI (e.g., 3×3 pixels or 5×5 pixels) and the detailed characteristic of blood perfusion can be shown by time-intensity curves (TICs) obtained by computing the mean intensity of pixels comprised within those small ROIs at each time point [2]. PPI is always offline implemented using specific sonographic quantification software. Moreover, the smaller size of ROI is necessary for higher resolution of PPI, but the signal-to-clutter ratio (SCR) of TICs is depressed when the size of ROI decreases [2]. Meanwhile, accurate diagnosis of PPI in liver disease is also seriously disturbed by the fluctuations of TICs and decreases of coded threshold induced by no-MB regions. Such disturbances, particularly the decrease of SCR of TICs, are further exacerbated during selecting single-pixel ROI to obtain PPI with highest resolution. In previous studies, the disturbances induced by no-MB regions are eliminated through manually drawing region of PPI to exclude those regions located in tissue without bolus kinetics [8]. However, it is not convenient for operators and its accuracy is affected by artifact. Moreover, the TIC is fitted using an appropriate indicator dilution mathematical model to suppress the fluctuation of TIC and enhance its corresponding SCR [9], including the lognormal model, gamma variate model, and local density random walk model. Nevertheless, these processes are time consuming and unsuitable for recirculation perfusion. Recently, the TIC is denoised using the weighted moving average filter [2] and the detrended fluctuation analysis [10]. Therefore, PPI with single-pixel resolution of gallbladder carcinoma liver metastasis was accurately obtained using the
2015 IEEE International Ultrasonics Symposium Proceedings
filtration method of valid TICs based on cross correlation analysis and the SCR enhancement of TICs through detrended fluctuation analysis in this study. II.
METHODS AND EXPERIMENTS
A. Algorithms of Parametric perfusion imaging The flow diagram of PPI is shown in Fig. 1, which composed of five parts: data acquisition, filtration of valid TICs, enhancing SCR of TIC, perfusion parameters extraction, and color-coded [2, 7]. First, two types of TICs were acquired from the same dynamic CEUS image loops. One reference TIC was acquired based on the whole imaging plane and 3D matrix of imaging TICs was derived from each ROI with size of a single pixel. In order to obtain accurate PPI without disturbances from the no-MB regions and the fluctuations, valid imaging TICs were then filtrated based on cross correlation analysis and their SCR was further enhanced through detrended fluctuation analysis, of which detailed presentation is described in sections B and C, respectively. Finally, four perfusion parameters of bolus kinetics were extracted from the denoised TIC matrix and then color-coded to quantify and depict the spatial distribution of hemodynamic information in the liver. These parameters included time to peak (TTP), wash in time (WIR), wash out time (WOT), and area under curve (AUC). The calculation of these perfusion parameters has been detailedly described by Gu [2]. Note that the malposition of ROI and the large fluctuations of imaging TICs were caused by respiratory and cardiac-induced motion during the perfusion process [2, 7]. Thus, techniques of image motion correction or motion compensation should be applied before the aforementioned procedure of PPI. However, these techniques were difficult to real-time implement due to their complex algorithms. Therefore, the clip was edited to exclude out-of-plane images and images preceding contrast arrival in the liver [7]. B. Valid TICs filtration The valid TICs filtration was performed based on cross correlation analysis to eliminate the disturbances induced by no-MB regions; otherwise the coded threshold of PPI would be
Figure.1 Flow diagram of parametric perfusion imaging
decreased. Correlation coefficients between the reference TIC and each imaging TIC were calculated by cross correlation analysis and one 2D matrix of correlation coefficient was obtained. Obviously, the TICs in no-MB regions had low correlation coefficients because they located in tissue without bolus kinetics. Thus, those TICs in no-MB regions can be eliminated according to one threshold of correlation coefficient, and the residual TICs were known as the valid TICs. According to our previous experiments, this threshold is always set up a value of 0.3 to 0.4 and it was 0.3 in this study. Additionally, it was beneficial to reduce the operation time of PPI because lots of invalid data were eliminated. Moreover, complex calculation including addition and division was avoided during the acquirement process of imaging TICs, which contributed to the improvement of operational efficiency of PPI. C. Enhancing SCR of TICs The fluctuation of TICs was eliminated by detrended fluctuation analysis in this study. As a nonlinear segmentation polynomial fitting function, it can be used to enhance the SCR of TICs effectively. The details of this method have been reported by Gao [11]. The original valid TIC was partitioned into segments with a length of 2n+1 points, where n is half the () segment size. The neighboring segments ( ) and were overlapped by n+1 points. The overlapped region ( ) ( ), which was calculated using the weighted ( ) and is described by following equation: ()= () (1) × ( )+ × where = 1, ⋯ , + 1, = 1 − / , j = 1,2, and denotes the distance between the point and the centers of ( ) ( ) [11]. Then, the TIC denoising and fitting were and quantitatively evaluated by SCR, as described by the following equations [12]: / ( )d ( )d (2) = 10log where ( ) is the power spectrum of the denoised TIC. and are the cut-off and sampling frequency values of TIC, respectively. D. Clinical experiments Our PPI algorithms were demonstrated by clinical experiments. One typical patient with gallbladder carcinoma liver metastasis was chosen due to his complex hemodynamic in the liver and the slight movement disturbance caused by respiratory and cardiac-induced motion. A convex array transducer 6C1 (Aplio400, Toshiba, Tochigi, Japan) was utilized in the experiments. After the focal liver metastasis was located by B-mode imaging, the contrast-harmonic imaging mode was switched with a work frequency of 2.0 MHz and a low mechanical index of 0.08. Immediately after intravenous bolus of 2.5 mL MBs dilution (SonoVue, Bracco, Milan, Italy) with a concentration of 2×108 bubbles/mL, video data was acquiring during perfusion process at a frame rate of 15 Hz. The data were provided by Beijing Tumor Hospital. This study protocol was approved by the local Research Ethics Committee. All experiments were performed at room temperature and all post-processes in this study were performed with MATLAB (Mathworks Inc., Natick, MA).
Figure.2 Typical dynamic contrast enhanced ultrasound images of a patient with gallbladder carcinoma liver metastasis. Their time from (a) to (b) is pre-injection, 6.67 s, and 13.33 s post-injection, respectively. Their dynamic range is 40 dB.
III.
RESULTS AND DISCUSSIONS
A. Perfusion analysis Typical dynamic CEUS images of a patient with gallbladder carcinoma liver metastasis are shown in Fig. 2. Liver has complex vascularities system. After MBs arrived in arterial phase, portal-venous phase was immediately contrast-enhanced, which were indicated in Fig. 2(b). The vascular tree structure of the liver was readily visible in 13.33s post-injection. During the perfusion process, attention should be paid to the fact that one region near the right hepatic vein was hyperperfused tissue, which indicated by the white-dashed box in Fig. 2(b). Compared with healthy parenchyma, MBs arrived much earlier and left more quickly in this region. In fact, this region was the region of gallbladder carcinoma liver metastasis. B. Valid TICs filtration based on cross correlation analysis Although PPI can differentiate hypoperfused and nonperfused tissue, the accuracy of PPI was disturbed by no-MB regions due to the decrease of color-coded threshold if choosing conventional PPI method. As shown in Fig. 2(a), regions without MBs always appeared inevitably in the selected PPI region, which were indicated by the red-arrows and the white-dashed box, respectively. In order to remove this type disturbance, the valid TICs filtration was performed based on cross correlation analysis. Take AUC as an example, PPI after valid TICs filtration was shown in Fig. 3. Compared with the original correlation coefficient image in Fig. 3(a), residual correlation coefficient image (threshold = 0.3) between reference TIC and imaging TICs at single-pixel ROI indicated that no-MBs regions were eliminated effectively. Based on these residual correlation coefficients in Fig. 3(b), valid TICs (correlation coefficient > 0.3) in regions located in tissue with bolus kinetics were filtrated quickly. Compared with AUC in Fig. 3(c), accurate PPI images were also obtained in Fig. 3(d). Compared with conventional manually excluding method [8], our TICs filtration with simple calculation was not artifact influences and easy to be auto-implemented on CEUS platform. Moreover, operation time of PPI decreased 50.4 ± 0.1% in one personal computer (Asus laptop, Shanghai, China) because lots of invalid data were eliminated. As shown in Fig. 3(c) and (d), edge features of the invasion area and liver were also clearly described without extra segmentation algorithm. C. TICs denosing using detrended fluctuation analysis Because SCR of TICs is decreased when the size of ROI decreases [2], disturbances from the clutter waves and the
Figure.3 AUC perfusion images after valid TICs filtration. (a) Original correlation coefficient image; (b) correlation coefficient image at the threshold of 0.3; (c) and (d) AUC perfusion images based on (a) and (b) correlation coefficient matrixs, respectively.
Figure.4 Single-pixel TICs in carcinoma area, hepatic vein and lob. (a) Positions of three typical single-pixel ROI; (b) Original TICs; and (c) TICs after filtered by DFA
fluctuation of TIC on the accuracy of PPI were exacerbated during selecting single-pixel ROI to obtain imaging TICs from the dynamic CEUS images. As shown in Fig. 4, three typical single-pixel TICs in carcinoma area, hepatic vein and lob are obtained from the dynamic CEUS images. Compared with the TICs after filtered by detrended fluctuation analysis in Fig. 4(c), the original valid TICs were seriously disturbed by the fluctuation of TICs and had low SCR. After TICs denosing, the disturbances from the clutter and fluctuation of TICs were effectively removed by detrended fluctuation analysis; and the SCR of TICs was enhanced by 5.49 ± 0.34 dB. Although TICs were still always fitted using bolus indicator models to acquire the theoretic TICs and the noise was effectively suppressed, the recirculation perfusion information was lost in liver and the parameters of models were also difficult to be obtained. However, there were not aforementioned deficiencies and had one advantage without phase lag during TICs denosing using detrended fluctuation analysis [2]. Moreover, compared with the three typical TICs in Fig. 4(c), hemodynamic differences between healthy parenchyma and focal tissue were indicated clearly, which was
IV.
CONCLUSION
In this study, after valid TICs filtration and denoising, the disturbances from no-MB regions were effectively removed; SCR of TICs was enhanced by 5.49 ± 0.34 dB; and operation time of PPI decreased 50.4 ± 0.1% because lots of invalid data were eliminated. Hot spots distribution and perfusion characteristic of neovascularization in the carcinoma invasion area were accurately distinguished and depicted by combining PPI with single-pixel resolution, especially the WIT and WOT. Besides, edge features of the invasion area and liver were clearly described without extra segmentation algorithm. It can contribute to accurately make a clinical decision in the liver diagnoses by the single-pixel resolution PPI with comprehensive functional perfusion information. ACKNOWLEDGMENT This work was supported by the National Natural Science Foundation of China (Grant No. 81127901 and 81471671) from Ministry of Science and Technology. REFERENCES [1]
Figure.5 PPI imaes with single-pixel resolution. (a) and (b) WIT and its partial enlarged details; (c) and (d) TTP and its partial enlarged details; (e) and (f) WOT and its partial enlarged details.
similar to the results of the previous studies [7]. The valid TICs with high SCR was conducive to the performance of accurate PPI with single-pixel resolution. D. PPI with single-pixel resolution Due to the complex hemodynamic distribution in liver, the spatial distribution of quantitative properties of tumor perfusion with high resolution is more meaningful and highly required in liver cancer research. Nevertheless, an automatic pixel-by-pixel PPI method was not first used in CEUS imaging but in magnetic resonance imaging [7], because PPI with single-pixel resolution was limited by the lower SCR of TICs in CEUS imaging. In fact, averaging signal within larger ROI had the effect of denoising. As shown in Fig. 5, three typical PPI images with single-pixel resolution including WIT, TTP, and WOT were obtained after denosing and valid filtration of TICs. Perfusion details and hemodynamic changes in the liver metastasis were clearly illustrated in Fig. 5(b, d, f). Hot spots distribution and perfusion characteristic of neovascularization in the liver metastases were accurately distinguished and depicted by combining PPI with single-pixel resolution, especially the WIT and WOT. Compared with healthy liver parenchyma, these PPI images indicated that MBs appear hyper-enhanced initially and hypo-enhanced later in the liver metastases [7]. These finds based on PPI technique with single-pixel resolution suggested that this tumor was malignant cancer and its angiogenesis grew activity, which was similar to the results in the previous studies [7].
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