The impact of audio-visual biofeedback on 4D PET images: Results of a phantom study

Yang, Jaewon; Yamamoto, Tokihiro; Cho, Byungchul; Seo, Youngho; Keall, Paul J.

doi:10.1118/1.3679012

Cited by 18 publications

(22 citation statements)

References 49 publications

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“…17 The benefit of reducing irregular respiration using AV biofeedback has also demonstrated to be beneficial in improving motion reproducibility for respiratory-gated radiotherapy 18 in addition to reducing blurring artifacts in 4D PET (Ref. 13) and CT. 19 This study has further investigated the benefits of AV biofeedback by examining its impact on the accuracy of motion prediction; this has not been studied before and is the novel concept of this study. Given the findings from previous studies, in this study we test the hypothesis that AV biofeedback improves motion prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…9 Increasing the size of the PTV also increases the dose delivered to the healthy surrounding tissue leading to a considerable increase in radiation-related toxicity [10][11][12] and dose delivered to the healthy surrounding tissue. 7,[13][14][15] If patient breathing could be managed to minimize respiratory irregularities, motion management efficiency would be maximized. Such a respiratory management technique is the audiovisual (AV) biofeedback system.…”

Section: Introductionmentioning

confidence: 99%

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Audiovisual biofeedback improves motion prediction accuracy

et al. 2013

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Purpose:The accuracy of motion prediction, utilized to overcome the system latency of motion management radiotherapy systems, is hampered by irregularities present in the patients' respiratory pattern. Audiovisual (AV) biofeedback has been shown to reduce respiratory irregularities. The aim of this study was to test the hypothesis that AV biofeedback improves the accuracy of motion prediction. Methods: An AV biofeedback system combined with real-time respiratory data acquisition and MR images were implemented in this project. One-dimensional respiratory data from (1) the abdominal wall (30 Hz) and (2) the thoracic diaphragm (5 Hz) were obtained from 15 healthy human subjects across 30 studies. The subjects were required to breathe with and without the guidance of AV biofeedback during each study. The obtained respiratory signals were then implemented in a kernel density estimation prediction algorithm. For each of the 30 studies, five different prediction times ranging from 50 to 1400 ms were tested (150 predictions performed). Prediction error was quantified as the root mean square error (RMSE); the RMSE was calculated from the difference between the real and predicted respiratory data. The statistical significance of the prediction results was determined by the Student's t-test. Results: Prediction accuracy was considerably improved by the implementation of AV biofeedback. Of the 150 respiratory predictions performed, prediction accuracy was improved 69% (103/150) of the time for abdominal wall data, and 78% (117/150) of the time for diaphragm data. The average reduction in RMSE due to AV biofeedback over unguided respiration was 26% (p < 0.001) and 29% (p < 0.001) for abdominal wall and diaphragm respiratory motion, respectively. Conclusions: This study was the first to demonstrate that the reduction of respiratory irregularities due to the implementation of AV biofeedback improves prediction accuracy. This would result in increased efficiency of motion management techniques affected by system latencies used in radiotherapy.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Audiovisual biofeedback improves motion prediction accuracy

et al. 2013

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“…However, 17 out of 40 paired evaluations had better image quality scores when VBF was used. The incorporation of a respiratory guiding system has shown similar improvements in image quality for 4D CT, 3D MR, and 4D PET 27 , 28 , 29 . VBF tended to improve image quality for intermediate phases (i.e., mid‐exhale [i.e., 25%] and EI).…”

Section: Discussionmentioning

confidence: 89%

Impact of incorporating visual biofeedback in 4D MRI

Kim

Price

et al. 2016

J Applied Clin Med Phys

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Precise radiation therapy (RT) for abdominal lesions is complicated by respiratory motion and suboptimal soft tissue contrast in 4D CT. 4D MRI offers improved contrast although long scan times and irregular breathing patterns can be limiting. To address this, visual biofeedback (VBF) was introduced into 4D MRI. Ten volunteers were consented to an IRB‐approved protocol. Prospective respiratory‐triggered, T2‐weighted, coronal 4D MRIs were acquired on an open 1.0T MR‐SIM. VBF was integrated using an MR‐compatible interactive breath‐hold control system. Subjects visually monitored their breathing patterns to stay within predetermined tolerances. 4D MRIs were acquired with and without VBF for 2‐ and 8‐phase acquisitions. Normalized respiratory waveforms were evaluated for scan time, duty cycle (programmed/acquisition time), breathing period, and breathing regularity (end‐inhale coefficient of variation, EI‐COV). Three reviewers performed image quality assessment to compare artifacts with and without VBF. Respiration‐induced liver motion was calculated via centroid difference analysis of end‐exhale (EE) and EI liver contours. Incorporating VBF reduced 2‐phase acquisition time (4.7±1.0 and 5.4±1.5trueprefixmin with and without VBF, respectively) while reducing EI‐COV by 43.8%±16.6%. For 8‐phase acquisitions, VBF reduced acquisition time by 1.9±1.6trueprefixmin and EI‐COVs by 38.8%±25.7% despite breathing rate remaining similar (11.1±3.8 breaths/min with vs. 10.5±2.9 without). Using VBF yielded higher duty cycles than unguided free breathing (34.4%±5.8% vs. 28.1%±6.6%, respectively). Image grading showed that out of 40 paired evaluations, 20 cases had equivalent and 17 had improved image quality scores with VBF, particularly for mid‐exhale and EI. Increased liver excursion was observed with VBF, where superior–inferior, anterior–posterior, and left–right EE‐EI displacements were 14.1±5.8, 4.9±2.1, and 1.5±1.0 mm, respectively, with VBF compared to 11.9±4.5, 3.7±2.1, and 1.2±1.4 mm without. Incorporating VBF into 4D MRI substantially reduced acquisition time, breathing irregularity, and image artifacts. However, differences in excursion were observed, thus implementation will be required throughout the RT workflow.PACS number(s): 87.55.‐x, 87.61.‐c, 87.19.xj

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“…The exceptional use of six-bin gated CT was allowed for patient 8 because ACT failed to cover the tumor motion trajectory (>20 mm). PET images were reconstructed through the Ordered Subset Expectation Maximization (OSEM) algorithm (21 subsets, 2 iterations, and 6 mm FWHM Gaussian post filter) with the voxel size of 2.3 × 2.3 × 3.75 mm 3 .…”

Section: D-pet and 4d-ct Imagingmentioning

confidence: 99%

“…However, 4D imaging may fail to reduce artifacts if breathing is irregular during data acquisition due to inadequate respiratory motion sampling for image reconstruction [2], [3]. For this reason, respiratory training or coaching is a commonly used technique to reduce irregularity in breathing cycles.…”

Section: Introductionmentioning

confidence: 99%