Respiratory motion degrades anatomic position reproducibility during imaging, necessitates larger margins during radiotherapy planning and causes errors during radiation delivery. Computed tomography (CT) scans acquired synchronously with the respiratory signal can be used to reconstruct 4D CT scans, which can be employed for 4D treatment planning to explicitly account for respiratory motion. The aim of this research was to develop, test and clinically implement a method to acquire 4D thoracic CT scans using a multislice helical method. A commercial position-monitoring system used for respiratory-gated radiotherapy was interfaced with a third generation multislice scanner. 4D cardiac reconstruction methods were modified to allow 4D thoracic CT acquisition. The technique was tested on a phantom under different conditions: stationary, periodic motion and non-periodic motion. 4D CT was also implemented for a lung cancer patient with audio-visual breathing coaching. For all cases, 4D CT images were successfully acquired from eight discrete breathing phases, however, some limitations of the system in terms of respiration reproducibility and breathing period relative to scanner settings were evident. Lung mass for the 4D CT patient scan was reproducible to within 2.1% over the eight phases, though the lung volume changed by 20% between end inspiration and end expiration (870 cm3). 4D CT can be used for 4D radiotherapy, respiration-gated radiotherapy, 'slow' CT acquisition and tumour motion studies.

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Accurate modeling of the respiratory cycle is important to account for the effect of organ motion on dose calculation for lung cancer patients. The aim of this study is to evaluate the accuracy of a respiratory model for lung cancer patients. Lujan et al. [Med. Phys. 26(5), 715-720 (1999)] proposed a model, which became widely used, to describe organ motion due to respiration. This model assumes that the parameters do not vary between and within breathing cycles. In this study, first, the correlation of respiratory motion traces with the model f(t) as a function of the parameter n (n = 1, 2, 3) was undertaken for each breathing cycle from 331 four-minute respiratory traces acquired from 24 lung cancer patients using three breathing types: free breathing, audio instruction, and audio-visual biofeedback. Because cos2 and cos4 had similar correlation coefficients, and cos2 and cos1 have a trigonometric relationship, for simplicity, the cos1 value was consequently used for further analysis in which the variations in mean position (z0), amplitude of motion (b) and period (tau) with and without biofeedback or instructions were investigated. For all breathing types, the parameter values, mean position (z0), amplitude of motion (b), and period (tau) exhibited significant cycle-to-cycle variations. Audio-visual biofeedback showed the least variations for all three parameters (z0, b, and tau). It was found that mean position (z0) could be approximated with a normal distribution, and the amplitude of motion (b) and period (tau) could be approximated with log normal distributions. The overall probability density function (pdf) of f(t) for each of the three breathing types was fitted with three models: normal, bimodal, and the pdf of a simple harmonic oscillator. It was found that the normal and the bimodal models represented the overall respiratory motion pdfs with correlation values from 0.95 to 0.99, whereas the range of the simple harmonic oscillator pdf correlation values was 0.71 to 0.81. This study demonstrates that the pdfs of mean position (z0), amplitude of motion (b), and period (tau) can be used for sampling to obtain more realistic respiratory traces. The overall standard deviations of respiratory motion were 0.48, 0.57, and 0.55 cm for free breathing, audio instruction, and audio-visual biofeedback, respectively.

Respiratory motion during intensity modulated radiation therapy (IMRT) causes two types of problems. First, the clinical target volume (CTV) to planning target volume (PTV) margin needed to account for respiratory motion means that the lung and heart dose is higher than would occur in the absence of such motion. Second, because respiratory motion is not synchronized with multileaf collimator (MLC) motion, the delivered dose is not the same as the planned dose. The aims of this work were to evaluate these problems to determine (a) the effects of respiratory motion and setup error during breast IMRT treatment planning, (b) the effects of the interplay between respiratory motion and multileaf collimator (MLC) motion during breast IMRT delivery, and (c) the potential benefits of breast IMRT using breath-hold, respiratory gated, and 4D techniques. Seven early stage breast cancer patient data sets were planned for IMRT delivered with a dynamic MLC (DMLC). For each patient case, eight IMRT plans with varying respiratory motion magnitudes and setup errors (and hence CTV to PTV margins) were created. The effects of respiratory motion and setup error on the treatment plan were determined by comparing the eight dose distributions. For each fraction of these plans, the effect of the interplay between respiratory motion and MLC motion during IMRT delivery was simulated by superimposing the respiratory trace on the planned DMLC leaf motion, facilitating comparisons between the planned and expected dose distributions. When considering respiratory motion in the CTV-PTV expansion during breast IMRT planning, our results show that PTV dose heterogeneity increases with respiratory motion. Lung and heart doses also increase with respiratory motion. Due to the interplay between respiratory motion and MLC motion during IMRT delivery, the planned and expected dose distributions differ. This difference increases with respiratory motion. The expected dose varies from fraction to fraction. However, for the seven patients studied and respiratory trace used, for no breathing, shallow breathing, and normal breathing, there were no statistically significant differences between the planned and expected dose distributions. Thus, for breast IMRT, intrafraction motion degrades treatment plans predominantly by the necessary addition of a larger CTV to PTV margin than would be required in the absence of such motion. This motion can be limited by breath-hold, respiratory gated, or 4D techniques.

The aim of this research was to investigate the effectiveness of a novel audio-visual biofeedback respiratory training tool to reduce respiratory irregularity. The audiovisual biofeedback system acquires sample respiratory waveforms of a particular patient and computes a patient-specific waveform to guide the patient's subsequent breathing. Two visual feedback models with different displays and cognitive loads were investigated: a bar model and a wave model. The audio instructions were ascending/descending musical tones played at inhale and exhale respectively to assist in maintaining the breathing period. Free-breathing, bar model and wave model training was performed on ten volunteers for 5 min for three repeat sessions. A total of 90 respiratory waveforms were acquired. It was found that the bar model was superior to free breathing with overall rms displacement variations of 0.10 and 0.16 cm, respectively, and rms period variations of 0.77 and 0.33 s, respectively. The wave model was superior to the bar model and free breathing for all volunteers, with an overall rms displacement of 0.08 cm and rms periods of 0.2 s. The reduction in the displacement and period variations for the bar model compared with free breathing was statistically significant (p = 0.005 and 0.002, respectively); the wave model was significantly better than the bar model (p = 0.006 and 0.005, respectively). Audiovisual biofeedback with a patient-specific guiding waveform significantly reduces variations in breathing. The wave model approach reduces cycle-to-cycle variations in displacement by greater than 50% and variations in period by over 70% compared with free breathing. The planned application of this device is anatomic and functional imaging procedures and radiation therapy delivery.

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Four-dimensional CT images are generally sorted through a post-acquisition procedure correlating images with a time-synchronized external respiration signal. The patient's ability to maintain reproducible respiration is the limiting factor during 4D CT, where artifacts occur in approximately 85% of scans with current technology. To reduce these artifacts and their subsequent effects during radiotherapy planning, a method for improved 4D CT image acquisition that relies on gating 4D CT acquisition based on the real time monitoring of the respiration signal has been proposed. The respiration signal and CT data acquisition are linked, such that data from irregular breathing cycles, which cause artifacts, are not acquired by gating CT acquisition by the respiratory signal. A proof-of-principle application of the respiratory regularity gated 4D CT method using patient respiratory signals demonstrates the potential of this method to reduce artifacts currently found in 4D CT scans. Numerical simulations indicate a potential reduction in motion within a respiratory phase bin by 20-40% depending on tolerances chosen. Additional advantages of the proposed method are dose reduction by eliminating unnecessary oversampling and obviating the need for post-processing to create the 4D CT data set.

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