Non-contrast power Doppler ultrasound imaging for early assessment of trans-arterial chemoembolization of liver tumors

Jaime Tierney, Jennifer Baker, Anthony Borgmann, Daniel Brown, Brett Byram, Jaime Tierney, Jennifer Baker, Anthony Borgmann, Daniel Brown, Brett Byram

Abstract

Trans-arterial chemoembolization (TACE) is an important yet variably effective treatment for management of hepatic malignancies. Lack of response can be in part due to inability to assess treatment adequacy in real-time. Gold-standard contrast enhanced computed tomography and magnetic resonance imaging, although effective, suffer from treatment-induced artifacts that prevent early treatment evaluation. Non-contrast ultrasound is a potential solution but has historically been ineffective at detecting treatment response. Here, we propose non-contrast ultrasound with recent perfusion-focused advancements as a tool for immediate evaluation of TACE. We demonstrate initial feasibility in an 11-subject pilot study. Treatment-induced changes in tumor perfusion are detected best when combining adaptive demodulation (AD) and singular value decomposition (SVD) techniques. Using a 0.5 s (300-sample) ensemble size, AD + SVD resulted in a 7.42 dB median decrease in tumor power after TACE compared to only a 0.06 dB median decrease with conventional methods.

Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Example full field of view focused B-mode (top) and SLSC (middle and bottom) images before (left) and after (right) TACE. Potential vessels are indicated in the after TACE SLSC image that are not apparent in the B-mode. Example crop region (orange), tumor (red), and background (yellow) ROIs are displayed on focused full field of view SLSC images in the bottom row. B-mode images are scaled to individual maximums and are displayed on a dB scale. SLSC images are scaled from 0 to the maximum coherence in the image.
Figure 2
Figure 2
Focused B-mode, focused SLSC, and power Doppler images before and after TACE for subjects 10 (a), 9 (b), and 5 (c). Power Doppler (PD) images were made with conventional (Conv.) methods and with adaptive demodulation (AD) and SVD filtering. 27 ms (16 samples) and 0.5 s (300 samples) ensemble sizes were used for the conventional and advanced methods, respectively. SLSC images are scaled from 0 to the maximum coherence in the image. Potential vessels, tumor flow, and decreased flow are indicated in the AD + SVD images that are not apparent in the conventional PD images.
Figure 3
Figure 3
Example dynamic range evaluation for subject 9 after TACE. Histograms are shown on the left for power Doppler (PD) images made with conventional methods (black) (i.e., 16-sample ensemble and IIR filtering) and with adaptive demodulation (AD) and SVD filtering (gray). Histograms were made after log compression. The orange and green dots indicate the dynamic ranges adaptively chosen (middle 70% of the full dynamic range) for the conventional and AD + SVD cases, respectively. Corresponding power Doppler (PD) images are shown on the right on dB scales. PD images were log compressed and cropped to the adaptively selected dynamic ranges.
Figure 4
Figure 4
Change in tumor-to-background contrast for each processing technique: Conventional (Conv.), adaptive demodulation (AD) + Conv., mean phase shift (MPS) + Conv., IIR, AD + IIR, SVD, AD + SVD. The median value for each method is the central mark in each box. The 25th and 75th percentiles are the bottom and top edges of each box, respectively. The bars extending from each box indicate the minimums and maximums, and outliers are marked in red. Statistically significant differences from the conventional method are indicated with *(p < 0.05) and **(p < 0.01).
Figure 5
Figure 5
Average contrast (left) and change in contrast (right) for varying ensemble sizes and for each processing method: IIR (orange), adaptive demodulation (AD) + IIR (purple), SVD (black), and AD + SVD (green). On the left, contrast values before and after TACE are shown as the solid and dashed curves, respectively. On the right, change in contrast for when no tissue filtering is used is shown in teal for reference. The no tissue filter values are negative likely because the lipiodol used during TACE is hyperechoic and becomes structure in the tumor.
Figure 6
Figure 6
Power Doppler images of subject 1 before TACE (top) and after TACE (bottom). Power Doppler images are made using no tissue filtering, IIR filtering, and with adaptive demodulation (AD) and SVD filtering for ensemble sizes of 200, 300, and 400 samples (333 ms, 500 ms, and 667 ms). Focused SLSC images are included for anatomical reference.
Figure 7
Figure 7
Cartoon depiction of the different transmit sequences used in this study. Focused scans acquire a single lateral location of an image at a time and are focused at a single depth. Plane wave scans sacrifice transmit focusing and involve transmitting from all elements at once to insonify the entire field of view. Plane wave synthetic focusing (PWSF) involves transmitting multiple angled plane waves and then summing them to gain transmit focusing throughout the image (i.e., at all depths and lateral locations).

References

    1. Bray F, et al. Global cancer statistics 2018: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: a cancer journal for clinicians. 2018;68:394–424.
    1. Lobo L, et al. Unresectable hepatocellular carcinoma: Radioembolization versus chemoembolization: A systematic review and meta-analysis. Cardiovascular and Interventional Radiology Society of Europe. 2016;39:1580–1588. doi: 10.1007/s00270-016-1426-y.
    1. Gaba RC, et al. Transcatheter therapy for hepatic malignancy: Standardization of terminology and reporting criteria. Journal of Vascular Interventional Radiology. 2016;27:457–473. doi: 10.1016/j.jvir.2015.12.752.
    1. Lammer J, et al. Prospective randomized study of doxorubicin-eluting-bead embolization in the treatment of hepatocellular carcinoma: Results of the precision v study. Cardiovascular and Interventional Radiology Society of Europe. 2010;33:41–52. doi: 10.1007/s00270-009-9711-7.
    1. Nam K, et al. Evaluation of hepatocellular carcinoma transarterial chemoembolization using quantitative analysis of 2d and 3d real-time contrast enhanced ultrasound. Biomedical Physics and Engineering Express. 2018;4:035039. doi: 10.1088/2057-1976/aabb14.
    1. Zhao Y, et al. Which criteria applied in multi-phasic ct can predict early tumor response in patients with hepatocellular carcinoma treated using conventional tace: Recist, mrecist, easl or qeasl? Cardiovascular and interventional radiology. 2018;41:433–442. doi: 10.1007/s00270-017-1829-4.
    1. Haywood, N. et al. Does the degree of hepatocellular carcinoma tumor necrosis following transarterial chemoembolization impact patient survival? Journal of oncology2016 (2016).
    1. Brown DB, et al. Quality improvement guidelines for transhepatic arterial chemoembolization, embolization, and chemotherapeutic infusion for hepatic malignancy. Journal of Vascular and Interventional Radiology. 2012;23:287–294. doi: 10.1016/j.jvir.2011.11.029.
    1. Kono Y, et al. Contrast-enhanced ultrasound as a predictor of treatment efficacy within 2 weeks after transarterial chemoembolization of hepatocellular carcinoma. Journal of Vascular and Interventional Radiology. 2007;18:57–65. doi: 10.1016/j.jvir.2006.10.016.
    1. Sparchez Z, et al. Contrast enhanced ultrasonography in assessing the treatment response to transarterial chemoembolization in patients with hepatocellular carcinoma. Medical Ultrasound. 2016;18:96–102.
    1. Moschouris H, et al. Unenhanced and contrast-enhanced ultrasonography during hepatic transarterial embolization and chemoembolization with drug-eluting beads. Cardiovascular Interventional Radiology. 2010;33:1215–1222. doi: 10.1007/s00270-010-9918-7.
    1. Kubota K, et al. Evaluation of hepatocellular carcinoma after treatment with transcatheter arterial chemoembolization: comparison of lipiodol-ct, power doppler sonography, and dynamic mri. Abdominal Imaging. 2001;26:184–190. doi: 10.1007/s002610000139.
    1. Heimdal A, Torp H. Ultrasound doppler measurements of low velocity blood flow: limitations due to clutter signals from vibrating muscles. IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control. 1997;44:873–881. doi: 10.1109/58.655202.
    1. Tremblay-Darveau C, Williams R, Milot L, Bruce M, Burns PN. Combined perfusion and doppler imaging using plane-wave nonlinear detection and microbubble contrast agents. IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control. 2014;61:1988–2000. doi: 10.1109/TUFFC.2014.006573.
    1. Rubin JM, Bude RO, Carson PL, Bree RL, Adler RS. Power doppler us: a potentially useful alternative to mean frequency-based color doppler us. Radiology. 1994;190:853–856. doi: 10.1148/radiology.190.3.8115639.
    1. Adler RS, Rubin JM, Fowlkes JB, Carson PL, Pallister JE. Ultrasonic estimation of tissue perfusion: a stochastic approach. Ultrasound in medicine & biology. 1995;21:493–500. doi: 10.1016/0301-5629(94)00135-Z.
    1. Chen J-F, Fowlkes JB, Carson PL, Rubin JM, Adler RS. Autocorrelation of integrated power doppler signals and its application. Ultrasound in medicine & biology. 1996;22:1053–1057. doi: 10.1016/S0301-5629(96)00141-X.
    1. Rubin JM, et al. Fractional moving blood volume: estimation with power doppler us. Radiology. 1995;197:183–190. doi: 10.1148/radiology.197.1.7568820.
    1. Hoeks A, Van de Vorst J, Dabekaussen A, Brands P, Reneman R. An efficient algorithm to remove low frequency doppler signals in digital doppler systems. Ultrasonic imaging. 1991;13:135–144. doi: 10.1177/016173469101300202.
    1. Thomas L, Hall A. An improved wall filter for flow imaging of low velocity flow. IEEE Ultrasonics Symposium Proceedings. 1994;3:1701–1704. doi: 10.1109/ULTSYM.1994.401918.
    1. Kadi AP, Loupas T. On the performance of regression and step-initialized iir clutter filters for color doppler systems in diagnostic medical ultrasound. IEEE transactions on ultrasonics, ferroelectrics, and frequency control. 1995;42:927–937. doi: 10.1109/58.464825.
    1. Torp H. Clutter rejection filters in color flow imaging: A theoretical approach. IEEE transactions on ultrasonics, ferroelectrics, and frequency control. 1997;44:417–424. doi: 10.1109/58.585126.
    1. Bjaerum S, Torp H, Kristoffersen K. Clutter filter design for ultrasound color flow imaging. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control. 2002;49:204–216. doi: 10.1109/58.985705.
    1. Bjaerum S, Torp H, Kristoffersen K. Clutter filters adapted to tissue motion in ultrasound color flow imaging. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control. 2002;49:693–704. doi: 10.1109/TUFFC.2002.1009328.
    1. Ledoux LA, Brands PJ, Hoeks AP. Reduction of the clutter component in doppler ultrasound signals based on singular value decomposition: a simulation study. Ultrasonic imaging. 1997;19:1–18. doi: 10.1177/016173469701900101.
    1. Gallippi C, Trahey G. Adaptive clutter filtering via blind source separation for two-dimensional ultrasonic blood velocity measurement. Ultrasonic Imaging. 2002;24:193–214. doi: 10.1177/016173460202400401.
    1. Lovstakken L, Bjaerum S, Kristoffersen K, Haaverstad R, Torp H. Real-time adaptive clutter rejection filtering in color flow imaging using power method iterations. IEEE transactions on ultrasonics, ferroelectrics, and frequency control. 2006;53:1597–1608. doi: 10.1109/TUFFC.2006.1678188.
    1. Yu ACH, Lovstakken L. Eigen-based clutter filter design for ultrasound color flow imaging: a review. IEEE transactions on ultrasonics, ferroelectrics, and frequency control. 2010;57:1096–1111. doi: 10.1109/TUFFC.2010.1521.
    1. Mace E, et al. Functional ultrasound imaging of the brain: theory and basic principles. IEEE transactions on ultrasonics, ferroelectrics, and frequency control. 2013;60:492–506. doi: 10.1109/TUFFC.2013.2592.
    1. Montaldo G, Tanter M, Bercoff J, Benech N, Fink M. Coherent plane-wave compounding for very high frame rate ultrasonography and transient elastography. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control. 2009;56:489–506. doi: 10.1109/TUFFC.2009.1067.
    1. Li YL, Hyun D, Abou-Elkacem L, Willmann JK, Dahl J. Visualization of small-diameter vessels by reduction of incoherent reverberation with coherent flow power doppler. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control. 2016;63:1878–1889. doi: 10.1109/TUFFC.2016.2616112.
    1. Tierney J, Coolbaugh C, Towse T, Byram B. Adaptive clutter demodulation for non-contrast ultrasound perfusion imaging. IEEE Transactions on Medical Imaging. 2017;36:1979–1991. doi: 10.1109/TMI.2017.2714901.
    1. Demene C, et al. Spatiotemporal clutter filtering of ultrafast ultrasound data highly increases doppler and fultrasound sensitivity. IEEE Transactions on Medical Imaging. 2015;34:2271–2285. doi: 10.1109/TMI.2015.2428634.
    1. Kim M, Abbey CK, Hedhli J, Dobrucki LW, Insana MF. Expanding acquisition and clutter filter dimensions for improved perfusion sensitivity. IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control. 2017;64:1429–1438. doi: 10.1109/TUFFC.2017.2719942.
    1. Kim M, Zhu Y, Hedhli J, Dobrucki LW, Insana MF. Multidimensional clutter filter optimization for ultrasonic perfusion imaging. IEEE transactions on ultrasonics, ferroelectrics, and frequency control. 2018;65:2020–2029. doi: 10.1109/TUFFC.2018.2868441.
    1. Song P, Manduca A, Trzasko JD, Chen S. Ultrasound small vessel imaging with block-wise adaptive local clutter filtering. IEEE Transactions on Medical Imaging. 2017;36:251–262. doi: 10.1109/TMI.2016.2605819.
    1. Tierney, J. et al. Combining slow flow techniques with adaptive demodulation for improved perfusion ultrasound imaging without contrast. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control (2019).
    1. Tierney, J., George, M., Coolbaugh, C., Towse, T. & Byram, B. C. Combining adaptive demodulation with singular value decomposition filtering for improved non-contrast perfusion ultrasound imaging. In Proc. SPIE 10580, Medical Imaging 2018: Ultrasonic Imaging and Tomography (Houston, TX, USA, 2018).
    1. Yiu BY, Tsang IK, Alfred C. Gpu-based beamformer: Fast realization of plane wave compounding and synthetic aperture imaging. IEEE transactions on ultrasonics, ferroelectrics, and frequency control. 2011;58:1698–1705. doi: 10.1109/TUFFC.2011.1999.
    1. Chen C, Hendriks GA, van Sloun RJ, Hansen HH, de Korte CL. Improved plane-wave ultrasound beamforming by incorporating angular weighting and coherent compounding in fourier domain. IEEE transactions on ultrasonics, ferroelectrics, and frequency control. 2018;65:749–765. doi: 10.1109/TUFFC.2018.2811865.
    1. Chernyakova T, et al. Fourier-domain beamforming and structure-based reconstruction for plane-wave imaging. IEEE transactions on ultrasonics, ferroelectrics, and frequency control. 2018;65:1810–1821. doi: 10.1109/TUFFC.2018.2856301.
    1. Rosenzweig S, Palmeri M, Nightingale K. Gpu-based real-time small displacement estimation with ultrasound. IEEE transactions on ultrasonics, ferroelectrics, and frequency control. 2011;58:399–405. doi: 10.1109/TUFFC.2011.1817.
    1. Lok, U.-W. et al. Parallel implementation of randomized singular value decomposition and randomized spatial downsampling for real time ultrafast microvessel imaging on a multi-core cpus architecture. In 2018 IEEE International Ultrasonics Symposium (IUS), 1–4 (IEEE, 2018).
    1. Pung L, et al. The role of cone-beam ct in transcatheter arterial chemoembolization for hepatocellular carcinoma: a systematic review and meta-analysis. Journal of Vascular and Interventional Radiology. 2017;28:334–341. doi: 10.1016/j.jvir.2016.11.037.
    1. Lediju MA, Trahey GE, Byram BC, Dahl JJ. Short-lag spatial coherence of backscattered echoes: Imaging characteristics. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control. 2011;58:1377–1388. doi: 10.1109/TUFFC.2011.1957.

Source: PubMed

Sponsors and Collaborators

Medical Conditions

Drug Interventions

3
Subscribe