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6. Ultrasound‐based sensors to monitor physiological motion

Author

Shenyan Zong, Frank Preiswerk, Cheng-Chieh Cheng, Jeremy S. Bredfeldt, and Bruno Madore

Subjects
Computer science, Movement, Respiratory gating, Article, 030218 nuclear medicine & medical imaging, Motion, 03 medical and health sciences, 0302 clinical medicine, medicine, Humans, Waveform, Physiological motion, Ultrasonography, medicine.diagnostic_test, business.industry, Respiration, Ultrasound, Magnetic resonance imaging, General Medicine, Magnetic Resonance Imaging, Optical tracking, 030220 oncology & carcinogenesis, Breathing, Ultrasonic sensor, business, Biomedical engineering
Abstract

Purpose Medical procedures can be difficult to perform on anatomy that is constantly moving. Respiration displaces internal organs by up to several centimeters with respect to the surface of the body, and patients often have limited ability to hold their breath. Strategies to compensate for motion during diagnostic and therapeutic procedures require reliable information to be available. However, current devices often monitor respiration indirectly, through changes on the outline of the body, and they may be fixed to floors or ceilings, and thus unable to follow a given patient through different locations. Here we show that small ultrasound-based sensors referred to as "organ configuration motion" (OCM) sensors can be fixed to the abdomen and/or chest and provide information-rich, breathing-related signals. Methods By design, the proposed sensors are relatively inexpensive. Breathing waveforms were obtained from tissues at varying depths and/or using different sensor placements. Validation was performed against breathing waveforms derived from magnetic resonance imaging (MRI) and optical tracking signals in five and eight volunteers, respectively. Results Breathing waveforms from different modalities were scaled so they could be directly compared. Differences between waveforms were expressed in the form of a percentage, as compared to the amplitude of a typical breath. Expressed in this manner, for shallow tissues, OCM-derived waveforms on average differed from MRI and optical tracking results by 13.1% and 15.5%, respectively. Conclusion The present results suggest that the proposed sensors provide measurements that properly characterize breathing states. While OCM-based waveforms from shallow tissues proved similar in terms of information content to those derived from MRI or optical tracking, OCM further captured depth-dependent and position-dependent (i.e., chest and abdomen) information. In time, the richer information content of OCM-based waveforms may enable better respiratory gating to be performed, to allow diagnostic and therapeutic equipment to perform at their best.

Published
2021
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7. Proton resonance frequency-based thermometry for aqueous and adipose tissues

Author

Chang-Sheng Mei, Guofeng Shen, and Shenyan Zong

Subjects
Male, Materials science, medicine.diagnostic_test, Phantoms, Imaging, Swine, Phase (waves), Adipose tissue, Water, Magnetic resonance imaging, General Medicine, Thermometry, computer.software_genre, Temperature measurement, Magnetic Resonance Imaging, Imaging phantom, Voxel, Linear regression, medicine, Animals, Protons, computer, Ex vivo, Biomedical engineering
Abstract

PURPOSE The proton resonance frequency (PRF)-based thermometry uses heating-induced phase variations to reconstruct magnetic resonance (MR) temperature maps. However, the measurements of the phase differences may be corrupted by the presence of fat due to its phase being insensitive to heat. The work aims to reconstruct the PRF-based temperature maps for tissues containing fat. METHODS This work proposes a PRF-based method that eliminates the fat's phase contribution by estimating the temperature-insensitive fat vector. A vector in a complex domain represents a given voxel's magnetization from an acquired, complex MR image. In this method, a circle was fit to a time series of vectors acquired from a heated region during a heating experiment. The circle center served as the fat vector, which was then subtracted from the acquired vectors, leaving only the temperature-sensitive vectors for thermal mapping. This work was verified with the gel phantoms of 10%, 15%, and 20% fat content and the ex vivo phantom of porcine abdomen tissue during water-bath heating. It was also tested with an ex vivo porcine tissue during focused ultrasound (FUS) heating. RESULTS A good agreement was found between the temperature measurements obtained from the proposed method and the optical fiber temperature probe in the verification experiments. In the gel phantoms, the linear regression provided a slope of 0.992 and an R2 of 0.994. The Bland-Altman analysis gave a bias of 0.49°C and a 95% confidence interval of ±1.60°C. In the ex vivo tissue, the results of the linear regression and Bland-Altman methods provided a slope of 0.979, an intercept of 0.353, an R2 of 0.947, and a 95% confidence interval of ±3.26°C with a bias of -0.14°C. In FUS tests, a temperature discrepancy of up to 28% was observed between the proposed and conventional PRF methods in ex vivo tissues containing fat. CONCLUSIONS The proposed PRF-based method can improve the accuracy of the temperature measurements in tissues with fat, such as breast, abdomen, prostate, and bone marrow.

Published
2021

8. Improved PRF-based MR thermometry using k-space energy spectrum analysis

Author

Guofeng Shen, Shenyan Zong, Bruno Madore, and Chang-Sheng Mei

Subjects
Physics, Work (thermodynamics), Pixel, business.industry, Phantoms, Imaging, Spectrum Analysis, Phase (waves), Magnitude (mathematics), k-space, Thermometry, Magnetic Resonance Imaging, Imaging phantom, Article, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, 0302 clinical medicine, Optics, Energy spectrum, Linear regression, Humans, Radiology, Nuclear Medicine and imaging, Protons, business, 030217 neurology & neurosurgery
Abstract

Purpose Proton resonance frequency (PRF) thermometry encodes information in the phase of MRI signals. A multiplicative factor converts phase changes into temperature changes, and this factor includes the TE. However, phase variations caused by B0 and/or B1 inhomogeneities can effectively change TE in ways that vary from pixel to pixel. This work presents how spatial phase variations affect temperature maps and how to correct for corresponding errors. Methods A method called "k-space energy spectrum analysis" was used to map regions in the object domain to regions in the k-space domain. Focused ultrasound heating experiments were performed in tissue-mimicking gel phantoms under two scenarios: with and without proper shimming. The second scenario, with deliberately de-adjusted shimming, was meant to emulate B0 inhomogeneities in a controlled manner. The TE errors were mapped and compensated for using k-space energy spectrum analysis, and corrected results were compared with reference results. Furthermore, a volunteer was recruited to help evaluate the magnitude of the errors being corrected. Results The in vivo abdominal results showed that the TE and heating errors being corrected can readily exceed 10%. In phantom results, a linear regression between reference and corrected temperatures results provided a slope of 0.971 and R2 of 0.9964. Analysis based on the Bland-Altman method provided a bias of -0.0977°C and 95% limits of agreement that were 0.75°C apart. Conclusion Spatially varying TE errors, such as caused by B0 and/or B1 inhomogeneities, can be detected and corrected using the k-space energy spectrum analysis method, for increased accuracy in proton resonance frequency thermometry.

Published
2019

9. PRFS- Based MR Thermometry with Correction for Fat Interference

Author

Huaxin Lu, Guofeng Shen, Shenyan Zong, Hang Liu, Sheng Chen, Shan Qiao, Jiawei Gu, and Han Wang

Subjects
Materials science, Nuclear magnetic resonance, Mr thermometry, Frequency shift, Interference (wave propagation)
Abstract

PRFS-based MR thermometry is inaccurate in tissues containing both fat and water, due to the lack of temperature-induced frequency shift in fat protons. A geometric-model based method was used to correct the conventional PRFS method. Continuous MR thermometry experiments were performed in phantoms, and the results suggested the proposed method can compensate the deviation of measured temperature in fat phantoms and would be useful for applying PRFS-based thermometry in medium containing both fat and water.

Published
2019
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10. International Society for Therapeutic Ultrasound Conference 2016

Author

Brian Fowlkes, Pejman Ghanouni, Narendra Sanghvi, Constantin Coussios, Paul C. Lyon, Michael Gray, Christophoros Mannaris, Marie de Saint Victor, Eleanor Stride, Robin Cleveland, Robert Carlisle, Feng Wu, Mark Middleton, Fergus Gleeson, Jean-Franҫois Aubry, Kim Butts Pauly, Chrit Moonen, Jacob Vortman, Shirley Sharabi, Dianne Daniels, David Last, David Guez, Yoav Levy, Alexander Volovick, Javier Grinfeld, Itay Rachmilevich, Talia Amar, Zion Zibly, Yael Mardor, Sagi Harnof, Michael Plaksin, Yoni Weissler, Shy Shoham, Eitan Kimmel, Omer Naor, Nairouz Farah, Dong-Guk Paeng, Zhiyuan Xu, John Snell, Anders H. Quigg, Matthew Eames, Changzhu Jin, Ashli C. Everstine, Jason P. Sheehan, Beatriz S. Lopes, Neal Kassell, Thomas Looi, Vera Khokhlova, Charles Mougenot, Kullervo Hynynen, James Drake, Michael Slayton, Richard C. Amodei, Keegan Compton, Ashley McNelly, Daniel Latt, John Kearney, David Melodelima, Aurelien Dupre, Yao Chen, David Perol, Jeremy Vincenot, Jean-Yves Chapelon, Michel Rivoire, Wei Guo, Guoxin Ren, Guofeng Shen, Michael Neidrauer, Leonid Zubkov, Michael S. Weingarten, David J. Margolis, Peter A. Lewin, Nathan McDannold, Jonathan Sutton, Natalia Vykhodtseva, Margaret Livingstone, Thiele Kobus, Yong-Zhi Zhang, Michael Schwartz, Yuexi Huang, Nir Lipsman, Jennifer Jain, Martin Chapman, Tejas Sankar, Andres Lozano, Robert Yeung, Christakis Damianou, Nikolaos Papadopoulos, Omer Brokman, Eyal Zadicario, Ori Brenner, David Castel, Shih-Ying Wu, Julien Grondin, Wenlan Zheng, Marc Heidmann, Maria Eleni Karakatsani, Carlos J. Sierra Sánchez, Vincent Ferrera, Elisa E. Konofagou, Marinos Yiannakou, HongSeok Cho, Hwayoun Lee, Mun Han, Jong-Ryul Choi, Taekwan Lee, Sanghyun Ahn, Yongmin Chang, Juyoung Park, Nicholas Ellens, Ari Partanen, Keyvan Farahani, Raag Airan, Alexandre Carpentier, Michael Canney, Alexandre Vignot, Cyril Lafon, Jean-yves Delattre, Ahmed Idbaih, Henrik Odéen, Bradley Bolster, Eun Kee Jeong, Dennis L. Parker, Pooja Gaur, Xue Feng, Samuel Fielden, Craig Meyer, Beat Werner, William Grissom, Michael Marx, Hans Weber, Valentina Taviani, Brian Hargreaves, Jun Tanaka, Kentaro Kikuchi, Ayumu Ishijima, Takashi Azuma, Kosuke Minamihata, Satoshi Yamaguchi, Teruyuki Nagamune, Ichiro Sakuma, Shu Takagi, Mathieu D. Santin, Laurent Marsac, Guillaume Maimbourg, Morgane Monfort, Benoit Larrat, Chantal François, Stéphane Lehéricy, Mickael Tanter, Gesthimani Samiotaki, Shutao Wang, Camilo Acosta, Eliza R. Feinberg, Zsofia I. Kovacs, Tsang-Wei Tu, Georgios Z. Papadakis, William C. Reid, Dima A. Hammoud, Joseph A. Frank, Zsofia i. Kovacs, Saejeong Kim, Neekita Jikaria, Michele Bresler, Farhan Qureshi, Jingjing Xia, Po-Shiang Tsui, Hao-Li Liu, Juan C. Plata, Bragi Sveinsson, Vasant A. Salgaonkar, Matthew Adams, Chris Diederich, Eugene Ozhinsky, Matthew D. Bucknor, Viola Rieke, Andrew Mikhail, Lauren Severance, Ayele H. Negussie, Bradford Wood, Martijn de Greef, Gerald Schubert, Mario Ries, Megan E. Poorman, Mary Dockery, Vandiver Chaplin, Stephanie O. Dudzinski, Ryan Spears, Charles Caskey, Todd Giorgio, Marcia M. Costa, Efthymia Papaevangelou, Anant Shah, Ian Rivens, Carol Box, Jeff Bamber, Gail ter Haar, Scott R. Burks, Matthew Nagle, Ben Nguyen, Blerta Milo, Nhan M. Le, Shaozhen Song, Kanheng Zhou, Ghulam Nabi, Zhihong Huang, Shmuel Ben-Ezra, Shani Rosen, Senay Mihcin, Jan Strehlow, Ioannis Karakitsios, Nhan Le, Michael Schwenke, Daniel Demedts, Paul Prentice, Sabrina Haase, Tobias Preusser, Andreas Melzer, Jean-Louis Mestas, Kamel Chettab, Gustavo Stadthagen Gomez, Charles Dumontet, Bettina Werle, Fabrice Marquet, Pierre Bour, Fanny Vaillant, Sana Amraoui, Rémi Dubois, Philippe Ritter, Michel Haïssaguerre, Mélèze Hocini, Olivier Bernus, Bruno Quesson, Amit Livneh, Dan Adam, Justine Robin, Bastien Arnal, Mathias Fink, Mathieu Pernot, Tatiana D. Khokhlova, George R. Schade, Yak-Nam Wang, Wayne Kreider, Julianna Simon, Frank Starr, Maria Karzova, Adam Maxwell, Michael R. Bailey, Jonathan E. Lundt, Steven P. Allen, Jonathan R. Sukovich, Timothy Hall, Zhen Xu, Philip May, Daniel W. Lin, Charlotte Constans, Thomas Deffieux, Jean-Francois Aubry, Eun-Joo Park, Yun Deok Ahn, Soo Yeon Kang, Dong-Hyuk Park, Jae Young Lee, J. Vidal-Jove, E. Perich, A. Ruiz, A. Jaen, N. Eres, M. Alvarez del Castillo, Rachel Myers, James Kwan, Christian Coviello, Cliff Rowe, Calum Crake, Sean Finn, Edward Jackson, Antonios Pouliopoulos, Caiqin Li, Marc Tinguely, Meng-Xing Tang, Valeria Garbin, James J. Choi, Lisa Folkes, Michael Stratford, Sandra Nwokeoha, Tong Li, Navid Farr, Samantha D’Andrea, Kayla Gravelle, Hong Chen, Donghoon Lee, Joo Ha Hwang, Sophie Tardoski, Jacqueline Ngo, Evelyne Gineyts, Jean-Pau Roux, Philippe Clézardin, Allegra Conti, Rémi Magnin, Matthieu Gerstenmayer, François Lux, Olivier Tillement, Sébastien Mériaux, Stefania Della Penna, Gian Luca Romani, Erik Dumont, Tao Sun, Chanikarn Power, Eric Miller, Oleg Sapozhnikov, Sergey Tsysar, Petr V. Yuldashev, Victor Svet, Dongli Li, Antonio Pellegrino, Nik Petrinic, Clive Siviour, Antoine Jerusalem, Peter V. Yuldashev, Bryan W. Cunitz, Barbrina Dunmire, Claude Inserra, Matthieu Guedra, Cyril Mauger, Bruno Gilles, Maxim Solovchuk, Tony W. H. Sheu, Marc Thiriet, Yufeng Zhou, Esra Neufeld, Christian Baumgartner, Davnah Payne, Adamos Kyriakou, Niels Kuster, Xu Xiao, Helen McLeod, Christopher Dillon, Allison Payne, Vera A. Khokhova, Ilya Sinilshchikov, Yulia Andriyakhina, Andrey Rybyanets, Natalia Shvetsova, Alex Berkovich, Igor Shvetsov, Caroline J. Shaw, John Civale, Dino Giussani, Christoph Lees, Valery Ozenne, Solenn Toupin, Vasant Salgaonkar, Elena Kaye, Sebastien Monette, Majid Maybody, Govindarajan Srimathveeravalli, Stephen Solomon, Amitabh Gulati, Mario Bezzi, Jürgen W. Jenne, Thomas Lango, Michael Müller, Giora Sat, Christine Tanner, Stephan Zangos, Matthias Günther, Au Hoang Dinh, Emilie Niaf, Flavie Bratan, Nicolas Guillen, Rémi Souchon, Carole Lartizien, Sebastien Crouzet, Olivier Rouviere, Yang Han, Thomas Payen, Carmine Palermo, Steve Sastra, Kenneth Olive, Johanna M. van Breugel, Maurice A. van den Bosch, Benjamin Fellah, Denis Le Bihan, Luis Hernandez-Garcia, Charles A. Cain, Erasmia Lyka, Delphine Elbes, Chunhui Li, Satoshi Tamano, Hayato Jimbo, Shin Yoshizawa, Keisuke Fujiwara, Kazunori Itani, Shin-ichiro Umemura, Dan Stoianovici, Zulfadhli Zaini, Ryo Takagi, Shenyan Zong, Ron Watkins, Aurea Pascal-Tenorio, Peter Jones, Kim Butts-Pauly, Donna Bouley, Yazhu Chen, Chung-Yin Lin, Han-Yi Hsieh, Kuo-Chen Wei, Camille Garnier, Gilles Renault, Reza Seifabadi, Emmanuel Wilson, Avinash Eranki, Peter Kim, Dennis Lübke, Peter Huber, Joachim Georgii, Caroline V. Dresky, Julian Haller, Pavel Yarmolenko, Karun Sharma, Haydar Celik, Guofeng Li, Weibao Qiu, Hairong Zheng, Meng-Yen Tsai, Po-Chun Chu, Taylor Webb, Urvi Vyas, Matthew Walker, Jidan Zhong, Adam C. Waspe, Mojgan Hodaie, Feng-Yi Yang, Sin-Luo Huang, Yuval Zur, Benny Assif, Christian Aurup, Hermes Kamimura, Antonio A. Carneiro, Sven Rothlübbers, Julia Schwaab, Graeme Houston, Haim Azhari, Noam Weiss, Jacob Sosna, S. Nahum Goldberg, Victor Barrere, Kee W. Jang, Bobbi Lewis, Xiaotong Wang, Visa Suomi, David Edwards, Zahary Larrabee, Arik Hananel, Boaz Rafaely, Rasha Elaimy Debbiny, Carmel Zeltser Dekel, Michael Assa, George Menikou, Petros Mouratidis, José A. Pineda-Pardo, Marta Del Álamo de Pedro, Raul Martinez, Frida Hernandez, Silvia Casas, Carlos Oliver, Patricia Pastor, Lidia Vela, Jose Obeso, Paul Greillier, Ali Zorgani, Stefan Catheline, Vyacheslav Solovov, Michael O. Vozdvizhenskiy, Andrew E. Orlov, Chueh-Hung Wu, Ming-Kuan Sun, Tiffany T. Shih, Wen-Shiang Chen, Fabrice Prieur, Arnaud Pillon, Valerie Cartron, Patrick Cebe, Nathalie Chansard, Maxime Lafond, Pauline Muleki Seya, Jean-Christophe Bera, Tanguy Boissenot, Elias Fattal, Alexandre Bordat, Helene Chacun, Claire Guetin, Nicolas Tsapis, Kazuo Maruyama, Johan Unga, Ryo Suzuki, Cécile Fant, Bernadette Rogez, Mercy Afadzi, Ola Finneng Myhre, Siri Vea, Astrid Bjørkøy, Petros Tesfamichael Yemane, Annemieke van Wamel, Sigrid Berg, Rune Hansen, Bjørn Angelsen, and Catharina Davies

Subjects
0301 basic medicine, medicine.medical_specialty, Therapeutic ultrasound, business.industry, Tel aviv, medicine.medical_treatment, 02 engineering and technology, 021001 nanoscience & nanotechnology, 03 medical and health sciences, 030104 developmental biology, Ophthalmology, medicine, Radiology, Nuclear Medicine and imaging, Medical physics, 0210 nano-technology, business
Published
2017
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11. Methods to improve the precision of positioning in the MR-guided focused ultrasound surgery system

Author

Shenyan Zong, Xiongfei Qu, Guofeng Shen, and Hongyang Mao

Subjects
business.industry, Computer science, medicine.medical_treatment, Cancer, medicine.disease, Focused ultrasound surgery, Ablation, 01 natural sciences, High-intensity focused ultrasound, 030218 nuclear medicine & medical imaging, Ultrasonic imaging, 03 medical and health sciences, 0302 clinical medicine, 0103 physical sciences, medicine, Computer vision, Artificial intelligence, business, 010301 acoustics, Mri guided, Simulation
Abstract

MR-guided pHIFU (phased High Intensity Focused Ultrasound) system can realize non-invasive ablation of tumors. In order to use MR as the guidance, there must be a precise transfer matrix between the MR and HIFU system. In this paper, some methods are proposed to improve the precision of positioning. One image-processing algorithm is used to automatically pick up the points. Furthermore, this paper proposes a new temperature positioning method based on multiple foci heating mode, and use optimal selection algorithm to reduce errors. In terms of results, using the automatic algorithm only took about 10 seconds to get all the points coordinates. It was shorter than the artificial method, which took at least one minute. It also proved that the optimal selection algorithm could reduce errors. The standard deviation using artificial method was 6.5899, and the standard deviation of automatic method reduced to 1.8445.

Published
2016
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