Literature DB >> 23127085

Audiovisual biofeedback improves diaphragm motion reproducibility in MRI.

Taeho Kim1, Sean Pollock, Danny Lee, Ricky O'Brien, Paul Keall.   

Abstract

PURPOSE: In lung radiotherapy, variations in cycle-to-cycle breathing results in four-dimensional computed tomography imaging artifacts, leading to inaccurate beam coverage and tumor targeting. In previous studies, the effect of audiovisual (AV) biofeedback on the external respiratory signal reproducibility has been investigated but the internal anatomy motion has not been fully studied. The aim of this study is to test the hypothesis that AV biofeedback improves diaphragm motion reproducibility of internal anatomy using magnetic resonance imaging (MRI).
METHODS: To test the hypothesis 15 healthy human subjects were enrolled in an ethics-approved AV biofeedback study consisting of two imaging sessions spaced ∼1 week apart. Within each session MR images were acquired under free breathing and AV biofeedback conditions. The respiratory signal to the AV biofeedback system utilized optical monitoring of an external marker placed on the abdomen. Synchronously, serial thoracic 2D MR images were obtained to measure the diaphragm motion using a fast gradient-recalled-echo MR pulse sequence in both coronal and sagittal planes. The improvement in the diaphragm motion reproducibility using the AV biofeedback system was quantified by comparing cycle-to-cycle variability in displacement, respiratory period, and baseline drift. Additionally, the variation in improvement between the two sessions was also quantified.
RESULTS: The average root mean square error (RMSE) of diaphragm cycle-to-cycle displacement was reduced from 2.6 mm with free breathing to 1.6 mm (38% reduction) with the implementation of AV biofeedback (p-value < 0.0001). The average RMSE of the respiratory period was reduced from 1.7 s with free breathing to 0.3 s (82% reduction) with AV biofeedback (p-value < 0.0001). Additionally, the average baseline drift obtained using a linear fit was reduced from 1.6 mm∕min with free breathing to 0.9 mm∕min (44% reduction) with AV biofeedback (p-value = 0.012). The diaphragm motion reproducibility improvements with AV biofeedback were consistent with the abdominal motion reproducibility that was observed from the external marker motion variation.
CONCLUSIONS: This study was the first to investigate the potential of AV biofeedback to improve the motion reproducibility of internal anatomy using MRI. The study demonstrated the significant improvement in diaphragm motion reproducibility using AV biofeedback combined with MRI. This system can potentially provide clinically beneficial motion management of internal anatomy in MRI and radiotherapy.

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Year:  2012        PMID: 23127085      PMCID: PMC3494729          DOI: 10.1118/1.4761866

Source DB:  PubMed          Journal:  Med Phys        ISSN: 0094-2405            Impact factor:   4.071


  17 in total

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Authors:  Jaewon Yang; Tokihiro Yamamoto; Byungchul Cho; Youngho Seo; Paul J Keall
Journal:  Med Phys       Date:  2012-02       Impact factor: 4.071

2.  Audio-visual biofeedback for respiratory-gated radiotherapy: impact of audio instruction and audio-visual biofeedback on respiratory-gated radiotherapy.

Authors:  Rohini George; Theodore D Chung; Sastry S Vedam; Viswanathan Ramakrishnan; Radhe Mohan; Elisabeth Weiss; Paul J Keall
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3.  Comparative performance of linear and nonlinear neural networks to predict irregular breathing.

Authors:  Martin J Murphy; Sonja Dieterich
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4.  Lung MRI using an MR-compatible active breathing control (MR-ABC).

Authors:  Johannes F T Arnold; Philipp Mörchel; Eckard Glaser; Eberhard D Pracht; Peter M Jakob
Journal:  Magn Reson Med       Date:  2007-12       Impact factor: 4.668

5.  Coronary MRI with a respiratory feedback monitor: the 2D imaging case.

Authors:  Y Wang; P S Christy; F R Korosec; M T Alley; T M Grist; J A Polzin; C A Mistretta
Journal:  Magn Reson Med       Date:  1995-01       Impact factor: 4.668

6.  Is the diaphragm motion probability density function normally distributed?

Authors:  R George; P J Keall; V R Kini; S S Vedam; V Ramakrishnan; R Mohan
Journal:  Med Phys       Date:  2005-02       Impact factor: 4.071

7.  Prediction of overall pulmonary function loss in relation to the 3-D dose distribution for patients with breast cancer and malignant lymphoma.

Authors:  J C Theuws; S L Kwa; A C Wagenaar; Y Seppenwoolde; L J Boersma; E M Damen; S H Muller; P Baas; J V Lebesque
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8.  The management of respiratory motion in radiation oncology report of AAPM Task Group 76.

Authors:  Paul J Keall; Gig S Mageras; James M Balter; Richard S Emery; Kenneth M Forster; Steve B Jiang; Jeffrey M Kapatoes; Daniel A Low; Martin J Murphy; Brad R Murray; Chester R Ramsey; Marcel B Van Herk; S Sastry Vedam; John W Wong; Ellen Yorke
Journal:  Med Phys       Date:  2006-10       Impact factor: 4.071

9.  Quantifying the predictability of diaphragm motion during respiration with a noninvasive external marker.

Authors:  S S Vedam; V R Kini; P J Keall; V Ramakrishnan; H Mostafavi; R Mohan
Journal:  Med Phys       Date:  2003-04       Impact factor: 4.071

10.  Respiratory biofeedback during CT-guided procedures.

Authors:  Julia K Locklin; Jeff Yanof; Alfred Luk; Zoltan Varro; Alexandru Patriciu; Bradford J Wood
Journal:  J Vasc Interv Radiol       Date:  2007-06       Impact factor: 3.464

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  13 in total

1.  Motion management strategies and technical issues associated with stereotactic body radiotherapy of thoracic and upper abdominal tumors: A review from NRG oncology.

Authors:  Edward D Brandner; Indrin J Chetty; Tawfik G Giaddui; Ying Xiao; M Saiful Huq
Journal:  Med Phys       Date:  2017-04-20       Impact factor: 4.071

2.  The potential of positron emission tomography for intratreatment dynamic lung tumor tracking: a phantom study.

Authors:  Jaewon Yang; Tokihiro Yamamoto; Samuel R Mazin; Edward E Graves; Paul J Keall
Journal:  Med Phys       Date:  2014-02       Impact factor: 4.071

3.  Effects of audio coaching and visual feedback on the stability of respiration during radiotherapy.

Authors:  Fumiya Baba; Satoshi Tanaka; Yoshinori Nonogaki; Shinji Hasegawa; Minami Nishihashi; Shiho Ayakawa; Maho Yamada; Yuta Shibamoto
Journal:  Jpn J Radiol       Date:  2016-06-17       Impact factor: 2.374

4.  Technical Note: Evaluation of audiovisual biofeedback smartphone application for respiratory monitoring in radiation oncology.

Authors:  Dante P I Capaldi; Tomi F Nano; Hao Zhang; Lawrie B Skinner; Lei Xing
Journal:  Med Phys       Date:  2020-10-10       Impact factor: 4.071

5.  Audiovisual biofeedback improves motion prediction accuracy.

Authors:  Sean Pollock; Danny Lee; Paul Keall; Taeho Kim
Journal:  Med Phys       Date:  2013-04       Impact factor: 4.071

6.  Audiovisual biofeedback breathing guidance for lung cancer patients receiving radiotherapy: a multi-institutional phase II randomised clinical trial.

Authors:  Sean Pollock; Ricky O'Brien; Kuldeep Makhija; Fiona Hegi-Johnson; Jane Ludbrook; Angela Rezo; Regina Tse; Thomas Eade; Roland Yeghiaian-Alvandi; Val Gebski; Paul J Keall
Journal:  BMC Cancer       Date:  2015-07-18       Impact factor: 4.430

7.  Comparison of visual biofeedback system with a guiding waveform and abdomen-chest motion self-control system for respiratory motion management.

Authors:  Yujiro Nakajima; Noriyuki Kadoya; Takayuki Kanai; Kengo Ito; Kiyokazu Sato; Suguru Dobashi; Takaya Yamamoto; Yojiro Ishikawa; Haruo Matsushita; Ken Takeda; Keiichi Jingu
Journal:  J Radiat Res       Date:  2016-02-27       Impact factor: 2.724

8.  An interactive videogame designed to improve respiratory navigator efficiency in children undergoing cardiovascular magnetic resonance.

Authors:  Sean M Hamlet; Christopher M Haggerty; Jonathan D Suever; Gregory J Wehner; Jonathan D Grabau; Kristin N Andres; Moriel H Vandsburger; David K Powell; Vincent L Sorrell; Brandon K Fornwalt
Journal:  J Cardiovasc Magn Reson       Date:  2016-09-06       Impact factor: 5.364

9.  A time-varying seasonal autoregressive model-based prediction of respiratory motion for tumor following radiotherapy.

Authors:  Kei Ichiji; Noriyasu Homma; Masao Sakai; Yuichiro Narita; Yoshihiro Takai; Xiaoyong Zhang; Makoto Abe; Norihiro Sugita; Makoto Yoshizawa
Journal:  Comput Math Methods Med       Date:  2013-06-10       Impact factor: 2.238

10.  First clinical implementation of audiovisual biofeedback in liver cancer stereotactic body radiation therapy.

Authors:  Sean Pollock; Regina Tse; Darren Martin; Lisa McLean; Gwi Cho; Robin Hill; Sheila Pickard; Paul Aston; Chen-Yu Huang; Kuldeep Makhija; Ricky O'Brien; Paul Keall
Journal:  J Med Imaging Radiat Oncol       Date:  2015-08-06       Impact factor: 1.735

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