To quantify the use of anterior torso skin surface position measurement as a breathing surrogate. Methods: Fourteen patients were scanned 25 times in alternating directions using a free-breathing low-mA fast helical CT protocol. Simultaneously, an abdominal pneumatic bellows was used as a real-time breathing surrogate. The imaged diaphragm dome position was used as a gold standard surrogate, characterized by localizing the most superior points of the diaphragm dome in each lung. These positions were correlated against the bellows signal acquired at the corresponding scan times. The bellows system has been shown to have a slow linear drift, and the bellows-to-CT synchronization process had a small uncertainty, so the drift and time offset were determined by maximizing the correlation coefficient between the craniocaudal diaphragm position and the drift-corrected bellows signal. The corresponding fit was used to model the real-time diaphragm position. To estimate the effectiveness of skin surface positions as surrogates, the anterior torso surface position was measured from the CT scans and correlated against the diaphragm position model. The residual error was defined as the root-mean-square correlation residual with the breathing amplitude normalized to the 5 th to 95 th breathing amplitude percentiles. The fit residual errors were analyzed over the surface for the fourteen studied patients and reported as percentages of the 5 th to 95 th percentile ranges. Results: A strong correlation was measured between the diaphragm motion and the abdominal bellows signal with an average residual error of 9.21% and standard deviation of 3.77%. In contrast, the correlations between the diaphragm position model and patient surface positions varied throughout the torso and from patient to patient. However, a consistently high correlation was found near the abdomen for each patient, and the average minimum residual error relating the skin surface to the diaphragm was 11.8% with a standard deviation of 4.61%. Conclusions: The thoracic patient surface was found to be an accurate surrogate, but the accuracy varied across the surface sufficiently that care would need to be taken to use the surface as an accurate and reliable surrogate. Future studies will use surface imaging to determine surface patch algorithms that utilize the entire chest as well as thoracic and abdominal breathing relationships.
To examine the use of multiple fast-helical free breathing computed tomography (FHFBCT) scans for ventilation measurement. Methods: Ten patients were scanned 25 times in alternating directions using a FHFBCT protocol. Simultaneously, an abdominal pneumatic bellows was used as a real-time breathing surrogate. Regions-of -interest (ROIs) were selected from the upper right lungs of each patient for analysis. The ROIs were first registered using a published registration technique (pTV). A subsequent followup registration employed an objective function with two terms, a ventilationadjusted Hounsfield Unit difference and a conservation-of -mass term labeled ΔΓ that denoted the difference between the deformation Jacobian and the tissue density ratio. The ventilations were calculated voxel-by-voxel as the slope of a first-order fit of the Jacobian as a function of the breathing amplitude. Results: The ventilations of the 10 patients showed different patterns and magnitudes. The average ventilation calculated from the deformation vector fields (DVFs) of the pTV and secondary registration was nearly identical, but the standard deviation of the voxel-to-voxel differences was approximately 0.1. The mean of the 90 th percentile values of ΔΓ was reduced from 0.153 to 0.079 between the pTV and secondary registration, implying first that the secondary registration improved the conservation-of -mass criterion by almost 50% and that on average the correspondence between the Jacobian and density ratios as demonstrated by ΔΓ was less than 0.1. This improvement occurred in spite of the average of the 90th percentile changes in the DVF magnitudes being only 0.58 mm. Conclusions: This work introduces the use of multiple free-breathing CT scans for free-breathing ventilation measurements. The approach has some benefits over the traditional use of 4-dimensional CT (4DCT) or breath-hold scans. The benefit over 4DCT is that FHFBCT does not have sorting artifacts. The benefits over breath-hold scans include the relatively small motion induced by quiet respiration versus deep-inspiration breath hold and the potential for characterizing dynamic breathing processes that disappear during breath hold.
Lung elastography aims at measuring the lung parenchymal tissue elasticity for applications ranging from diagnostic purposes to biomechanically guided deformations. Characterizing the lung tissue elasticity requires four-dimensional (4D) lung motion as an input, which is currently estimated by deformably registering 4D computed tomography (4DCT) datasets. Since 4DCT imaging is widely used only in a radiotherapy treatment setup, there is a need to predict the elasticity distribution in the absence of 4D imaging for applications within and outside of radiotherapy domain. Methods: In this paper, we present a machine learning-based method that predicts the three-dimensional (3D) lung tissue elasticity distribution for a given end-expiration 3DCT. The method to predict the lung tissue elasticity from an end-expiration 3DCT employed a deep neural network that predicts the tissue elasticity for the given CT dataset. For training and validation purposes, we employed fivedimensional CT (5DCT) datasets and a finite element biomechanical lung model. The 5DCT model was first used to generate end-expiration lung geometry, which was taken as the source lung geometry for biomechanical modeling. The deformation vector field pointing from end expiration to end inhalation was computed from the 5DCT model and taken as input in order to solve for the lung tissue elasticity. An inverse elasticity estimation process was employed, where we iteratively solved for the lung elasticity distribution until the model reproduced the ground-truth deformation vector field. The machine learning process uses a specific type of learning process, namely a constrained generalized adversarial neural network (cGAN) that learned the lung tissue elasticity in a supervised manner. The biomechanically estimated tissue elasticity together with the end-exhalation CT was the input for the supervised learning. The trained cGAN generated the elasticity from a given breath-hold CT image. The elasticity estimated was validated in two approaches. In the first approach, a L2-normbased direct comparison was employed between the estimated elasticity and the ground-truth elasticity. In the second approach, we generated a synthetic four-dimensional CT (4DCT0 using a lung biomechanical model and the estimated elasticity and compared the deformations with the groundtruth 4D deformations using three image similarity metrics: mutual Information (MI), structured similarity index (SSIM), and normalized cross correlation (NCC). Results: The results show that a cGAN-based machine learning approach was effective in computing the lung tissue elasticity given the end-expiration CT datasets. For the training data set, we obtained a learning accuracy of 0.44 AE 0.2 KPa. For the validation dataset, consisting of 13 4D datasets, we were able to obtain an accuracy of 0.87 AE 0.4 KPa. These results show that the cGAN-generated elasticity correlates well with that of the underlying ground-truth elasticity. We then integrated the estimated elasticity with the biomechanical model and appl...
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