Danny Lee1, Peter B Greer1,2, Chiara Paganelli3, Joanna Jane Ludbrook2, Taeho Kim4, Paul Keall5. 1. School of Mathematical and Physical Sciences, The University of Newcastle, Callaghan, NSW, Australia. 2. Department of Radiation Oncology, Calvary Mater Newcastle, Waratah, NSW, Australia. 3. Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy. 4. Department of Radiation Oncology, Virginia Commonwealth University, Richmond, VA, USA. 5. ACRF Image X Institute, Sydney Medical School, University of Sydney, Sydney, NSW, Australia.
Abstract
PURPOSE: Breathing management can reduce breath-to-breath (intrafraction) and day-by-day (interfraction) variability in breathing motion while utilizing the respiratory motion of internal and external surrogates for respiratory guidance. Audiovisual (AV) biofeedback, an interactive personalized breathing motion management system, has been developed to improve reproducibility of intra- and interfraction breathing motion. However, the assumption of the correlation of respiratory motion between surrogates and tumors is not always verified during medical imaging and radiation treatment. Therefore, the aim of the study was to test the hypothesis that the correlation of respiratory motion between surrogates and tumors is the same under free breathing without guidance (FB) and with AV biofeedback guidance for voluntary motion management. METHODS: For 13 lung cancer patients receiving radiotherapy, 2D coronal and sagittal cine-MR images were acquired across two MRI sessions (pre- and mid-treatment) with two breathing conditions: (a) FB and (b) AV biofeedback, totaling 88 patient measurements. Simultaneously, the external respiratory motion of the abdomen was measured. The internal respiratory motion of the diaphragm and lung tumor was retrospectively measured from 2D coronal and sagittal cine-MR images. The correlation of respiratory motion between surrogates and tumors was calculated using Pearson's correlation coefficient for: (a) abdomen to tumor (abdomen-tumor) and (b) diaphragm to tumor (diaphragm-tumor). The correlations were compared between FB and AV biofeedback using several metrics: abdomen-tumor and diaphragm-tumor correlations with/without ≥5 mm tumor motion range and with/without adjusting for phase shifts between the signals. RESULTS: Compared to FB, AV biofeedback improved abdomen-tumor correlation by 11% (p = 0.12) from 0.53 to 0.59 and diaphragm-tumor correlation by 13% (p = 0.02) from 0.55 to 0.62. Compared to FB, AV biofeedback improved abdomen-tumor correlation by 17% (p = 0.01) and diaphragm-tumor correlation by 15% (p < 0.01) while correcting 0.3 s (p = 0.54) and 0.2 s (p = 0.19) phase shifts, respectively. In addition, AV biofeedback with ≥5 mm tumor motion range, compared to FB improved abdomen-tumor correlation by 14% (p = 0.18) and diaphragm-tumor correlation by 17% (p = 0.01). The highest abdomen-tumor and diaphragm-tumor correlations were found using ≥5 mm tumor motion range and phase shifts, resulting in a 12% improvement in AV biofeedback. CONCLUSIONS: Our results demonstrated that AV biofeedback improves the correlation of respiratory motion between surrogates and the tumor. This suggests a need for AV biofeedback for respiratory guidance utilizing respiratory surrogates during image-guided and MRI-guided radiotherapy in thoracic regions.
PURPOSE: Breathing management can reduce breath-to-breath (intrafraction) and day-by-day (interfraction) variability in breathing motion while utilizing the respiratory motion of internal and external surrogates for respiratory guidance. Audiovisual (AV) biofeedback, an interactive personalized breathing motion management system, has been developed to improve reproducibility of intra- and interfraction breathing motion. However, the assumption of the correlation of respiratory motion between surrogates and tumors is not always verified during medical imaging and radiation treatment. Therefore, the aim of the study was to test the hypothesis that the correlation of respiratory motion between surrogates and tumors is the same under free breathing without guidance (FB) and with AV biofeedback guidance for voluntary motion management. METHODS: For 13 lung cancerpatients receiving radiotherapy, 2D coronal and sagittal cine-MR images were acquired across two MRI sessions (pre- and mid-treatment) with two breathing conditions: (a) FB and (b) AV biofeedback, totaling 88 patient measurements. Simultaneously, the external respiratory motion of the abdomen was measured. The internal respiratory motion of the diaphragm and lung tumor was retrospectively measured from 2D coronal and sagittal cine-MR images. The correlation of respiratory motion between surrogates and tumors was calculated using Pearson's correlation coefficient for: (a) abdomen to tumor (abdomen-tumor) and (b) diaphragm to tumor (diaphragm-tumor). The correlations were compared between FB and AV biofeedback using several metrics: abdomen-tumor and diaphragm-tumor correlations with/without ≥5 mm tumor motion range and with/without adjusting for phase shifts between the signals. RESULTS: Compared to FB, AV biofeedback improved abdomen-tumor correlation by 11% (p = 0.12) from 0.53 to 0.59 and diaphragm-tumor correlation by 13% (p = 0.02) from 0.55 to 0.62. Compared to FB, AV biofeedback improved abdomen-tumor correlation by 17% (p = 0.01) and diaphragm-tumor correlation by 15% (p < 0.01) while correcting 0.3 s (p = 0.54) and 0.2 s (p = 0.19) phase shifts, respectively. In addition, AV biofeedback with ≥5 mm tumor motion range, compared to FB improved abdomen-tumor correlation by 14% (p = 0.18) and diaphragm-tumor correlation by 17% (p = 0.01). The highest abdomen-tumor and diaphragm-tumor correlations were found using ≥5 mm tumor motion range and phase shifts, resulting in a 12% improvement in AV biofeedback. CONCLUSIONS: Our results demonstrated that AV biofeedback improves the correlation of respiratory motion between surrogates and the tumor. This suggests a need for AV biofeedback for respiratory guidance utilizing respiratory surrogates during image-guided and MRI-guided radiotherapy in thoracic regions.
Authors: Evan Liang; Jennifer L Dolan; Eric D Morris; Jonathan Vono; Luisa F Bazan; Mei Lu; Carri K Glide-Hurst Journal: Adv Radiat Oncol Date: 2022-01-04
Authors: Katrinus Keijnemans; Pim T S Borman; Prescilla Uijtewaal; Peter L Woodhead; Bas W Raaymakers; Martin F Fast Journal: Med Phys Date: 2022-06-22 Impact factor: 4.506