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

Vedam, Sastry; Kini, V. R.; Keall, P.; Ramakrishnan, Viswanathan; Mostafavi, Hassan; Mohan, Radhe

doi:10.1118/1.1558675

Cited by 297 publications

(256 citation statements)

References 48 publications

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“…This suggests that VBF would need to be implemented throughout the entire radiation therapy workflow. Our findings were contrary to those obtained by Vedam et al, (25) who studied five lung cancer patients with fluoroscopy and found that diaphragm motion was comparable with and without VBF. Another study showed VBF reduced respiratory amplitude by up to 40% (26) .…”

Section: Discussioncontrasting

confidence: 99%

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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Section: Discussioncontrasting

confidence: 99%

Impact of incorporating visual biofeedback in 4D MRI

Kim

Price

et al. 2016

J Applied Clin Med Phys

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“…Note that the development of our framework is independent of the data used, be it data collected with an external marker placed on the abdomen during breathing or internal tumour motion data obtained from a fluoroscopic marker. Ideally, in the optimization, a treatment planner would use data describing actual tumour motion induced by breathing, or external data plus the appropriate parameters to correlate internal motion to the external marker (Vedam et al 2003, Gierga et al 2005, Tsunashima et al 2004.…”

Section: From Patient Data To a Model Of Uncertaintymentioning

confidence: 99%

A robust approach to IMRT optimization

2006

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Managing uncertainty is a major challenge in radiation therapy treatment planning, including uncertainty induced by intrafraction motion, which is particularly important for tumours in the thorax and abdomen. Common methods to account for motion are to introduce a margin or to convolve the static dose distribution with a motion probability density function. Unlike previous work in this area, our development does not assume that the patient breathes according to a fixed distribution, nor is the patient required to breathe the same way throughout the treatment. Despite this generality, we create a robust optimization framework starting from the convolution method that is robust to fluctuations in breathing motion, yet spares healthy tissue better than a margin solution. We describe how to generate the data for our model using breathing motion data and we test our model on a computer phantom using data from real patients. In our numerical results, the robust solution delivers approximately 38% less dose to the healthy tissue than the margin solution, while providing the same level of protection against breathing uncertainty.

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“…1) from the abdominal marker with the largest respiratory amplitude. The NPM component along the Y axis of abdominal markers mostly reflects the trend changes in respiration (motion of the diaphragm [13]) as physical motion of the patient downwards in the vertical direction is restricted due to the bed and it is unlikely the patients would sustain a vertical motion upwards off the bed. The following parameters are used to quantify respiratory motion:…”

Section: Respiratory Motion Parametersmentioning

confidence: 99%

Quantitative study of rigid-body and respiratory motion of patients undergoing stress and rest cardiac SPECT imaging

Mukherjee

Johnson

McNamara

et al. 2008

2008 IEEE Nuclear Science Symposium Conference Record

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We report patient motion in 110 Tl-201 cardiac perfusion SPECT studies in 66 patients. The imaging consisted of emission followed by sequential transmission imaging during which motion tracking with a visual tracking system (VTS) was performed. We investigated the extent, time, and frequency of respiratory and rigid-body motion in these patients. We also determined whether the motion occurred gradually or in sudden jumps, whether it was sustained, and if it occurred along one or more axes predominantly. We then studied the differences in respiratory and body motion (BM), if any, between stress versus rest imaging groups, male versus female subjects, and exercise versus pharmacological stress groups. We found that 23% of the studies had sustained motion (> 4min.) of between 3-6 mm, and 5% had sustained motion larger than 6 mm during emission imaging. In terms of respiratory motion, 13% showed a downward trend of the respiratory baseline of more than 6 mm during emission imaging. Also, in 9% of the studies, the average position of patients was displaced by more than 3 mm between emission and transmission imaging phases. Both of these motions may lead to misalignment of the attenuation map. In hypothesis testing of grouped studies, it was determined that stress and rest imaging did not show any significant differences in body motion but did in respiratory motion associated with a change in respiration following stress. Exercise-stress studies showed a larger extent of respiratory motion than the pharmacologically induced stress studies. Significant differences in body and respiratory motion of male and female groups were also observed. A visual assessment of the reconstructed slices in the studies with measured motion was made to investigate the impact of the motion. Illustrative example studies are included.

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

Cited by 297 publications

References 48 publications

Impact of incorporating visual biofeedback in 4D MRI

Impact of incorporating visual biofeedback in 4D MRI

A robust approach to IMRT optimization

Quantitative study of rigid-body and respiratory motion of patients undergoing stress and rest cardiac SPECT imaging

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