Abstract

State-of-the-art optoacoustic tomographic imaging systems have been shown to attain three-dimensional (3D) frame rates of the order of 100 Hz. While such a high volumetric imaging speed is beyond reach for other bio-imaging modalities, it may still be insufficient to accurately monitor some faster events occurring on a millisecond scale. Increasing the 3D imaging rate is usually hampered by the limited throughput capacity of the data acquisition electronics and memory used to capture vast amounts of the generated optoacoustic (OA) data in real time. Herein, we developed a sparse signal acquisition scheme and a total-variation-based reconstruction approach in a combined space–time domain in order to achieve 3D OA imaging at kilohertz rates. By continuous monitoring of freely swimming zebrafish larvae in a 3D region, we demonstrate that the new approach enables significantly increasing the volumetric imaging rate by using a fraction of the tomographic projections without compromising the reconstructed image quality. The suggested method may benefit studies looking at ultrafast biological phenomena in 3D, such as large-scale neuronal activity, cardiac motion, or freely behaving organisms.

© 2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

Full Article  |  PDF Article
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References

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    [Crossref]
  9. L. Li, “Single-impulse panoramic photoacoustic computed tomography of small-animal whole-body dynamics at high spatiotemporal resolution,” Nat. Biomed. Eng. 1, 0071 (2017).
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    [Crossref]
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    [Crossref]
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    [Crossref]
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    [Crossref]
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    [Crossref]
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    [Crossref]
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    [Crossref]
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    [Crossref]
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    [Crossref]
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    [Crossref]
  37. A. Taruttis, E. Herzog, D. Razansky, and V. Ntziachristos, “Real-time imaging of cardiovascular dynamics and circulating gold nanorods with multispectral optoacoustic tomography,” Opt. Express 18, 19592–19602 (2010).
    [Crossref]
  38. X. L. Deán-Ben and D. Razansky, “Localization optoacoustic tomography,” Light Sci. Appl. 7, 18004 (2018).

2018 (1)

X. L. Deán-Ben and D. Razansky, “Localization optoacoustic tomography,” Light Sci. Appl. 7, 18004 (2018).

2017 (12)

S. Gottschalk, T. F. Fehm, X. L. Deán-Ben, V. Tsytsarev, and D. Razansky, “Correlation between volumetric oxygenation responses and electrophysiology identifies deep thalamocortical activity during epileptic seizures,” Neurophotonics 4, 011007 (2017).
[Crossref]

L. Li, “Single-impulse panoramic photoacoustic computed tomography of small-animal whole-body dynamics at high spatiotemporal resolution,” Nat. Biomed. Eng. 1, 0071 (2017).
[Crossref]

X. L. Deán-Ben, S. Gottschalk, G. Sela, S. Shoham, and D. Razansky, “Functional optoacoustic neuro-tomography of calcium fluxes in adult zebrafish brain in vivo,” Opt. Lett. 42, 959–962 (2017).
[Crossref]

J. Aguirre, “Precision assessment of label-free psoriasis biomarkers with ultra-broadband optoacoustic mesoscopy,” Nat. Biomed. Eng. 1, 0068 (2017).
[Crossref]

X. L. Deán-Ben, T. F. Fehm, S. J. Ford, S. Gottschalk, and D. Razansky, “Spiral volumetric optoacoustic tomography visualizes multi-scale dynamics in mice,” Light: Sci. Appl. 6, E1624710 (2017).
[Crossref]

B. Rao, R. Zhang, L. Li, J.-Y. Shao, and L. V. Wang, “Photoacoustic imaging of voltage responses beyond the optical diffusion limit,” Sci. Rep. 7, 2560 (2017).

P. Symvoulidis, A. Lauri, A. Stefanoiu, M. Cappetta, S. Schneider, H. Jia, A. Stelzl, M. Koch, C. C. Perez, A. Myklatun, S. Renninger, A. Chmyrov, T. Lasser, W. Wurst, V. Ntziachristos, and G. G. Westmeyer, NeuBtracker—imaging neurobehavioral dynamics in freely behaving fish, Nat. Method 14, 1079–1082 (2017).

L. Ding, X. L. Deán-Ben, and D. Razansky, “Efficient three-dimensional model-based reconstruction scheme for arbitrary optoacoustic acquisition geometries,” IEEE Trans. Med. Imaging 36, 1858–1867 (2017).
[Crossref]

M. Schloegl, M. Holler, A. Schwarzl, K. Bredies, and R. Stollberger, “Infimal convolution of total generalized variation functionals for dynamic MRI,” Magn. Reson. Med. 78, 142–155 (2017).
[Crossref]

X. L. Deán-Ben, H. López-Schier, and D. Razansky, “Optoacoustic micro-tomography at 100 volumes per second,” Sci. Rep. 7, 6850 (2017).
[Crossref]

P. Symvoulidis, A. Lauri, A. Stefanoiu, M. Cappetta, S. Schneider, H. Jia, A. Stelzl, M. Koch, C. C. Perez, A. Myklatun, and S. Renninger, “NeuBtracker—Imaging neurobehavioral dynamics in freely behaving larva,” Nat. Methods 14, 1079–1082 (2017).
[Crossref]

H. C. Lin, X. L. Deán-Ben, M. Kimm, K. Kosanke, H. Haas, R. Meier, F. Lohöfer, M. Wildgruber, and D. Razansky, “Non-invasive volumetric optoacoustic imaging of cardiac cycles in acute myocardial infarction model in real-time,” Theranostics 7, 4470–4479 (2017).
[Crossref]

2016 (7)

A. Chambolle and T. Pock, “An introduction to continuous optimization for imaging,” Acta Numer. 25, 161–319 (2016).
[Crossref]

S. Arridge, “Accelerated high-resolution photoacoustic tomography via compressed sensing,” Phys. Med. Biol. 61, 8908–8940 (2016).
[Crossref]

X. L. Deán-Ben, G. Sela, A. Lauri, M. Kneipp, V. Ntziachristos, G. G. Westmeyer, S. Shoham, and D. Razansky, “Functional optoacoustic neuro-tomography for scalable whole-brain monitoring of calcium indicators,” Light: Sci. Appl. 5, e16201 (2016).
[Crossref]

T. F. Fehm, X. L. Deán-Ben, S. J. Ford, and D. Razansky, “In vivo whole-body optoacoustic scanner with real-time volumetric imaging capacity,” Optica 3, 1153–1159 (2016).
[Crossref]

K. Sivasubramanian and M. Pramanik, “High frame rate photoacoustic imaging at 7000 frames per second using clinical ultrasound system,” Biomed. Opt. Express 7, 312–323 (2016).
[Crossref]

S. Ermilov, “Three-dimensional optoacoustic and laser-induced ultrasound tomography system for preclinical research in mice: design and phantom validation,” Ultrason. Imaging 38, 77–95 (2016).
[Crossref]

L. Ding, X. L. Deán-Ben, and D. Razansky, “Real-time model-based inversion in cross-sectional optoacoustic tomography,” IEEE Trans. Med. Imaging 35, 1883–1891 (2016).
[Crossref]

2015 (5)

A. P. Jathoul, “Deep in vivo photoacoustic imaging of mammalian tissues using a tyrosinase-based genetic reporter,” Nat. Photonics 9, 239–246 (2015).
[Crossref]

X. L. Deán-Ben, S. J. Ford, and D. Razansky, “High-frame rate four dimensional optoacoustic tomography enables visualization of cardiovascular dynamics and mouse heart perfusion,” Sci. Rep. 5, 10133 (2015).
[Crossref]

Y. Gong, “High-speed recording of neural spikes in awake mice and flies with a fluorescent voltage sensor,” Science 350, 1361–1366 (2015).
[Crossref]

C. G. Graff and E. Y. Sidky, “Compressive sensing in medical imaging,” Appl. Opt. 54, C23–C44 (2015).
[Crossref]

M. Sandbichler, F. Krahmer, T. Berer, P. Burgholzer, and M. Haltmeier, “A novel compressed sensing scheme for photoacoustic tomography,” Siam J. Appl. Math. 75, 2475–2494 (2015).
[Crossref]

2014 (1)

M. Holler and K. Kunisch, “On infimal convolution of TV-type functionals and applications to video and image reconstruction,” SIAM J. Imaging Sci. 7, 2258–2300 (2014).
[Crossref]

2013 (4)

X. L. Deán-Ben, A. Ozbek, and D. Razansky, “Volumetric real-time tracking of peripheral human vasculature with GPU-accelerated three-dimensional optoacoustic tomography,” IEEE Trans. Med. Imaging 32, 2050–2055 (2013).
[Crossref]

A. Buehler, M. Kacprowicz, A. Taruttis, and V. Ntziachristos, “Real-time handheld multispectral optoacoustic imaging,” Opt. Lett. 38, 1404–1406 (2013).
[Crossref]

R. A. Kruger, C. M. Kuzmiak, R. B. Lam, D. R. Reinecke, S. P. Del Rio, and D. Steed, “Dedicated 3d photoacoustic breast imaging,” Med. Phys. 40, 113301 (2013).
[Crossref]

D. Queirós, X. L. Deán-Ben, A. Buehler, D. Razansky, A. Rosenthal, and V. Ntziachristos, “Modeling the shape of cylindrically focused transducers in three-dimensional optoacoustic tomography,” J. Biomed. Opt. 18, 076014 (2013).
[Crossref]

2010 (2)

A. Rosenthal, D. Razansky, and V. Ntziachristos, “Fast semi-analytical model-based acoustic inversion for quantitative optoacoustic tomography,” IEEE Trans. Med. Imaging 29, 1275–1285 (2010).
[Crossref]

A. Taruttis, E. Herzog, D. Razansky, and V. Ntziachristos, “Real-time imaging of cardiovascular dynamics and circulating gold nanorods with multispectral optoacoustic tomography,” Opt. Express 18, 19592–19602 (2010).
[Crossref]

2008 (1)

R. Baraniuk, M. Davenport, R. DeVore, and M. Wakin, “A simple proof of the restricted isometry property for random matrices,” Constr. Approx. 28, 253–263 (2008).
[Crossref]

2006 (2)

D. L. Donoho, “Compressed sensing,” IEEE Trans. Inf. Theory 52, 1289–1306 (2006).
[Crossref]

E. J. Candès, J. Romberg, and T. Tao, “Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information,” IEEE Trans. Inf. Theory 52, 489–509 (2006).
[Crossref]

2002 (1)

G. Paltauf, J. A. Viator, S. A. Prahl, and S. L. Jacques, “Iterative reconstruction algorithm for optoacoustic imaging,” J. Acoust. Soc. Am. 112, 1536–1544 (2002).
[Crossref]

1992 (1)

L. I. Rudin, S. Osher, and E. Fatemi, “Nonlinear total variation based noise removal algorithms,” Phys. D Nonlinear Phenom. 60, 259–268 (1992).
[Crossref]

Aguirre, J.

J. Aguirre, “Precision assessment of label-free psoriasis biomarkers with ultra-broadband optoacoustic mesoscopy,” Nat. Biomed. Eng. 1, 0068 (2017).
[Crossref]

Arridge, S.

S. Arridge, “Accelerated high-resolution photoacoustic tomography via compressed sensing,” Phys. Med. Biol. 61, 8908–8940 (2016).
[Crossref]

Baraniuk, R.

R. Baraniuk, M. Davenport, R. DeVore, and M. Wakin, “A simple proof of the restricted isometry property for random matrices,” Constr. Approx. 28, 253–263 (2008).
[Crossref]

Berer, T.

M. Sandbichler, F. Krahmer, T. Berer, P. Burgholzer, and M. Haltmeier, “A novel compressed sensing scheme for photoacoustic tomography,” Siam J. Appl. Math. 75, 2475–2494 (2015).
[Crossref]

Bredies, K.

M. Schloegl, M. Holler, A. Schwarzl, K. Bredies, and R. Stollberger, “Infimal convolution of total generalized variation functionals for dynamic MRI,” Magn. Reson. Med. 78, 142–155 (2017).
[Crossref]

Buehler, A.

D. Queirós, X. L. Deán-Ben, A. Buehler, D. Razansky, A. Rosenthal, and V. Ntziachristos, “Modeling the shape of cylindrically focused transducers in three-dimensional optoacoustic tomography,” J. Biomed. Opt. 18, 076014 (2013).
[Crossref]

A. Buehler, M. Kacprowicz, A. Taruttis, and V. Ntziachristos, “Real-time handheld multispectral optoacoustic imaging,” Opt. Lett. 38, 1404–1406 (2013).
[Crossref]

Burgholzer, P.

M. Sandbichler, F. Krahmer, T. Berer, P. Burgholzer, and M. Haltmeier, “A novel compressed sensing scheme for photoacoustic tomography,” Siam J. Appl. Math. 75, 2475–2494 (2015).
[Crossref]

Candès, E. J.

E. J. Candès, J. Romberg, and T. Tao, “Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information,” IEEE Trans. Inf. Theory 52, 489–509 (2006).
[Crossref]

Cappetta, M.

P. Symvoulidis, A. Lauri, A. Stefanoiu, M. Cappetta, S. Schneider, H. Jia, A. Stelzl, M. Koch, C. C. Perez, A. Myklatun, S. Renninger, A. Chmyrov, T. Lasser, W. Wurst, V. Ntziachristos, and G. G. Westmeyer, NeuBtracker—imaging neurobehavioral dynamics in freely behaving fish, Nat. Method 14, 1079–1082 (2017).

P. Symvoulidis, A. Lauri, A. Stefanoiu, M. Cappetta, S. Schneider, H. Jia, A. Stelzl, M. Koch, C. C. Perez, A. Myklatun, and S. Renninger, “NeuBtracker—Imaging neurobehavioral dynamics in freely behaving larva,” Nat. Methods 14, 1079–1082 (2017).
[Crossref]

Chambolle, A.

A. Chambolle and T. Pock, “An introduction to continuous optimization for imaging,” Acta Numer. 25, 161–319 (2016).
[Crossref]

Chmyrov, A.

P. Symvoulidis, A. Lauri, A. Stefanoiu, M. Cappetta, S. Schneider, H. Jia, A. Stelzl, M. Koch, C. C. Perez, A. Myklatun, S. Renninger, A. Chmyrov, T. Lasser, W. Wurst, V. Ntziachristos, and G. G. Westmeyer, NeuBtracker—imaging neurobehavioral dynamics in freely behaving fish, Nat. Method 14, 1079–1082 (2017).

Davenport, M.

R. Baraniuk, M. Davenport, R. DeVore, and M. Wakin, “A simple proof of the restricted isometry property for random matrices,” Constr. Approx. 28, 253–263 (2008).
[Crossref]

Deán-Ben, X. L.

X. L. Deán-Ben and D. Razansky, “Localization optoacoustic tomography,” Light Sci. Appl. 7, 18004 (2018).

X. L. Deán-Ben, S. Gottschalk, G. Sela, S. Shoham, and D. Razansky, “Functional optoacoustic neuro-tomography of calcium fluxes in adult zebrafish brain in vivo,” Opt. Lett. 42, 959–962 (2017).
[Crossref]

S. Gottschalk, T. F. Fehm, X. L. Deán-Ben, V. Tsytsarev, and D. Razansky, “Correlation between volumetric oxygenation responses and electrophysiology identifies deep thalamocortical activity during epileptic seizures,” Neurophotonics 4, 011007 (2017).
[Crossref]

X. L. Deán-Ben, T. F. Fehm, S. J. Ford, S. Gottschalk, and D. Razansky, “Spiral volumetric optoacoustic tomography visualizes multi-scale dynamics in mice,” Light: Sci. Appl. 6, E1624710 (2017).
[Crossref]

L. Ding, X. L. Deán-Ben, and D. Razansky, “Efficient three-dimensional model-based reconstruction scheme for arbitrary optoacoustic acquisition geometries,” IEEE Trans. Med. Imaging 36, 1858–1867 (2017).
[Crossref]

H. C. Lin, X. L. Deán-Ben, M. Kimm, K. Kosanke, H. Haas, R. Meier, F. Lohöfer, M. Wildgruber, and D. Razansky, “Non-invasive volumetric optoacoustic imaging of cardiac cycles in acute myocardial infarction model in real-time,” Theranostics 7, 4470–4479 (2017).
[Crossref]

X. L. Deán-Ben, H. López-Schier, and D. Razansky, “Optoacoustic micro-tomography at 100 volumes per second,” Sci. Rep. 7, 6850 (2017).
[Crossref]

L. Ding, X. L. Deán-Ben, and D. Razansky, “Real-time model-based inversion in cross-sectional optoacoustic tomography,” IEEE Trans. Med. Imaging 35, 1883–1891 (2016).
[Crossref]

X. L. Deán-Ben, G. Sela, A. Lauri, M. Kneipp, V. Ntziachristos, G. G. Westmeyer, S. Shoham, and D. Razansky, “Functional optoacoustic neuro-tomography for scalable whole-brain monitoring of calcium indicators,” Light: Sci. Appl. 5, e16201 (2016).
[Crossref]

T. F. Fehm, X. L. Deán-Ben, S. J. Ford, and D. Razansky, “In vivo whole-body optoacoustic scanner with real-time volumetric imaging capacity,” Optica 3, 1153–1159 (2016).
[Crossref]

X. L. Deán-Ben, S. J. Ford, and D. Razansky, “High-frame rate four dimensional optoacoustic tomography enables visualization of cardiovascular dynamics and mouse heart perfusion,” Sci. Rep. 5, 10133 (2015).
[Crossref]

X. L. Deán-Ben, A. Ozbek, and D. Razansky, “Volumetric real-time tracking of peripheral human vasculature with GPU-accelerated three-dimensional optoacoustic tomography,” IEEE Trans. Med. Imaging 32, 2050–2055 (2013).
[Crossref]

D. Queirós, X. L. Deán-Ben, A. Buehler, D. Razansky, A. Rosenthal, and V. Ntziachristos, “Modeling the shape of cylindrically focused transducers in three-dimensional optoacoustic tomography,” J. Biomed. Opt. 18, 076014 (2013).
[Crossref]

Del Rio, S. P.

R. A. Kruger, C. M. Kuzmiak, R. B. Lam, D. R. Reinecke, S. P. Del Rio, and D. Steed, “Dedicated 3d photoacoustic breast imaging,” Med. Phys. 40, 113301 (2013).
[Crossref]

DeVore, R.

R. Baraniuk, M. Davenport, R. DeVore, and M. Wakin, “A simple proof of the restricted isometry property for random matrices,” Constr. Approx. 28, 253–263 (2008).
[Crossref]

Ding, L.

L. Ding, X. L. Deán-Ben, and D. Razansky, “Efficient three-dimensional model-based reconstruction scheme for arbitrary optoacoustic acquisition geometries,” IEEE Trans. Med. Imaging 36, 1858–1867 (2017).
[Crossref]

L. Ding, X. L. Deán-Ben, and D. Razansky, “Real-time model-based inversion in cross-sectional optoacoustic tomography,” IEEE Trans. Med. Imaging 35, 1883–1891 (2016).
[Crossref]

Donoho, D. L.

D. L. Donoho, “Compressed sensing,” IEEE Trans. Inf. Theory 52, 1289–1306 (2006).
[Crossref]

Ermilov, S.

S. Ermilov, “Three-dimensional optoacoustic and laser-induced ultrasound tomography system for preclinical research in mice: design and phantom validation,” Ultrason. Imaging 38, 77–95 (2016).
[Crossref]

Fatemi, E.

L. I. Rudin, S. Osher, and E. Fatemi, “Nonlinear total variation based noise removal algorithms,” Phys. D Nonlinear Phenom. 60, 259–268 (1992).
[Crossref]

Fehm, T. F.

S. Gottschalk, T. F. Fehm, X. L. Deán-Ben, V. Tsytsarev, and D. Razansky, “Correlation between volumetric oxygenation responses and electrophysiology identifies deep thalamocortical activity during epileptic seizures,” Neurophotonics 4, 011007 (2017).
[Crossref]

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P. Symvoulidis, A. Lauri, A. Stefanoiu, M. Cappetta, S. Schneider, H. Jia, A. Stelzl, M. Koch, C. C. Perez, A. Myklatun, and S. Renninger, “NeuBtracker—Imaging neurobehavioral dynamics in freely behaving larva,” Nat. Methods 14, 1079–1082 (2017).
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Supplementary Material (1)

NameDescription
» Visualization 1       Freely moving zebrafish larva captured at 1.6 kHz rate by 3D optoacoustic tomography.

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Figures (5)

Fig. 1.
Fig. 1. Schematic description of the DAQ protocol. (a) Division of sensors into acquisition groups. (b) Temporal division of the acquisition groups.
Fig. 2.
Fig. 2. Numerical simulations of the TV-based reconstructions of a moving absorbing sphere showcasing spatial resolution degradation as a function of the relative motion speed. The data is presented for the TV algorithm using 16 consecutive frames with 32 random channels per frame ( TV × 16 ), four consecutive frames with 128 random channels per frame ( TV × 4 ), and one frame ( TV × 1 ) with 128 or 32 random channels. The reconstructed relative sphere size, i.e., the reconstructed full width at half-maximum (d) for the longest dimension divided by the actual sphere diameter, is plotted against its relative speed of motion measured as the ratio between the inter-frame displacement of the sphere and its diameter.
Fig. 3.
Fig. 3. Comparison between the TV and BP reconstructions for sparsely sampled data. The ground truth image represents an absorbing sphere with 3D parabolic absorption distribution. (a) 3D reconstructions of an absorbing sphere with normalized velocity υ ¯ = 0.1 made with the full 512 channel data (BP 512ch and TV × 1 512ch) versus 32 random channels (BP 32ch and TV × 16 32ch). (b) The corresponding CNR of the images reconstructed with the different algorithms plotted against the number of consecutive frames.
Fig. 4.
Fig. 4. Comparison between 3D BP and the proposed TV-based CS reconstruction. Maximal intensity projection (MIP) images are shown. The vertical axis represents the acquisition frame rate, which is inversely proportional to the number of random detector positions used per frame (single laser pulse). Different reconstruction methods are shown on the horizontal axis. TV × L corresponds to the TV-based reconstruction employing L consecutive frames.
Fig. 5.
Fig. 5. Spatio-temporal resolution performance of the TV-based CS approach. Two consecutive images of a swimming larva rendered at different frame rates are superimposed in red and green. While a significant shift in the larva position is clearly visible for frames recorded at 100 Hz acquisition rate, the two consecutive images progressively overlap as the frame rate increases. The inter-frame motion of the left eye, marked with blue arrows, is plotted against the frame rate. The red arrows indicate parts of the fish that moved significantly between the two consecutive frames.

Equations (8)

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p i = C i Ah i ,
p = CA tot h ,
h sol = argmin h { 1 2 p m C A h 2 2 + λ h TV } ,
h TV = ( h x ) 2 + ( h y ) 2 + ( h z ) 2 + ( h t ) 2 1 ,
a h TV = div ( ( h x , h y , h z , h t ) a 2 + ( h x ) 2 + ( h y ) 2 + ( h z ) 2 + ( h t ) 2 ) ,
h k + 1 = h k γ k f ( h k ) ,
f ( h ) = A tot T C T ( p m CA tot h ) + λ a h TV ,
h TV = w s ( ( h x ) 2 + ( h y ) 2 + ( h z ) 2 ) + w t ( h t ) 2 1 .

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