We present a feasibility study on extracting the respiratory surrogate signal (RSS) from cone-beam computed tomography (CBCT) projections using a supervised convolutional neural network (CNN) model. Determining the intrinsic RSS instead of using an external surrogate signal, provided by optical tracing hardware such as the Real-time Position Management (RPM) system, has the advantage that it spares patient setup time and hence permits faster 4D CBCT acquisition. Another convenience of such an approach is that it can be applied retrospectively without special preparation or equipment. For the implementation, we made use of the MONAI open source library. Our model is based on a modified version of the MONAI regressor class. We trained the model using CBCT projections with the corresponding RSS as recorded by an external marker block using the RPM system. The model is to deduce the RSS given the CBCT. Using a specific dataset with CBCT from anesthetized animals breathing with the help of a mechanical ventilator, results show a good correlation between the actual and predicted RSS. Unlike the Amsterdam Shroud algorithm, our method shows promising results to predict the normalized amplitude of the breathing signal. Further work could extend the model to permit RSS prediction for its online use in radiation therapy or detection of sudden motion deteriorating CBCT image quality. To conclude, we have made a first step towards proving the concept which consists in using a deep learning model to extract the RSS out of acquired CBCT projection images. The approach is promising but requires more work for robustness, i.e. for sufficient accuracy both in the frequency and normalized amplitude extraction.
Polychromatic reconstruction is a promising technique for quantitative cone-beam computed tomography in radiation therapy. In this study, we have implemented polychromatic forward projection into our reconstruction framework to directly reconstruct relative electron density volumes without the need for additional HU calibration. The underlying spectral model takes beam hardening into account by design. Thereby this extended reconstruction framework is a natural step in the direction of spectral imaging, albeit without any hardware modifications. Reconstructed relative electron density volumes from phantom scans show sufficiently good agreement with ground truth for photon dose calculation; relative errors for most inserts are below 3%. We also demonstrate beam hardening artifact reduction in virtual monoenergetic images obtained from polychromatic reconstruction as compared to an established iterative reconstruction using water-based correction. Similarly, polychromatic reconstruction shows potential for mitigating metal artifacts in a clinical scan acquired for a patient with bilateral hip implants.
We propose an algorithm for periodic motion estimation and compensation in the case of a slowly rotating gantry, e.g., as is the case in cone beam CT. The main target application is abdomen imaging, which is quite challenging because of the absence of high-contrast features. The algorithm is based on minimizing a cost functional, which consists of the data fidelity term, the optical flow constraint term, and regularization terms. To find the appropriate solution we change the constraint strength and regularization strength parameters during the minimization. Results of experiments with simulated and clinical data demonstrate promising performance.
To improve the accuracy of motion vector fields (MVFs) required for respiratory motion compensated (MoCo)
CT image reconstruction without increasing the computational complexity of the MVF estimation approach,
we propose a MVF upsampling method that is able to reduce the motion blurring in reconstructed 4D images.
While respiratory gating improves the temporal resolution, it leads to sparse view sampling artifacts. MoCo
image reconstruction has the potential to remove all motion artifacts while simultaneously making use of 100%
of the rawdata. However the MVF accuracy is still below the temporal resolution of the CBCT data acquisition.
Increasing the number of motion bins would increase reconstruction time and amplify sparse view artifacts, but
not necessarily the accuracy of MVF. Therefore we propose a new method to upsample estimated MVFs and
use those for MoCo. To estimate the MVFs, a modified version of the Demons algorithm is used. Our proposed
method is able to interpolate the original MVFs up to a factor that each projection has its own individual MVF.
To validate the method we use an artificially deformed clinical CT scan, with a breathing pattern of a real patient,
and patient data acquired with a TrueBeamTM4D CBCT system (Varian Medical Systems). We evaluate our
method for different numbers of respiratory bins, each again with different upsampling factors. Employing our
upsampling method, motion blurring in the reconstructed 4D images, induced by irregular breathing and the
limited temporal resolution of phase–correlated images, is substantially reduced.
Kilo-voltage cone-beam computed tomography (CBCT) plays an important role in image guided radiation therapy (IGRT) by providing 3D spatial information of tumor potentially useful for optimizing treatment planning. In current IGRT CBCT system, reconstructed images obtained with analytic algorithms, such as FDK algorithm and its variants, may contain artifacts. In an attempt to compensate for the artifacts, we investigate optimization-based reconstruction algorithms such as the ASD-POCS algorithm for potentially reducing arti- facts in IGRT CBCT images. In this study, using data acquired with a physical phantom and a patient subject, we demonstrate that the ASD-POCS reconstruction can significantly reduce artifacts observed in clinical re- constructions. Moreover, patient images reconstructed by use of the ASD-POCS algorithm indicate a contrast level of soft-tissue improved over that of the clinical reconstruction. We have also performed reconstructions from sparse-view data, and observe that, for current clinical imaging conditions, ASD-POCS reconstructions from data collected at one half of the current clinical projection views appear to show image quality, in terms of spatial and soft-tissue-contrast resolution, higher than that of the corresponding clinical reconstructions.
We propose an adapted method of our previously published five-dimensional (5D) motion compensation (MoCo)
algorithm1, developed for micro-CT imaging of small animals, to provide for the first time motion artifact-free
5D cone-beam CT (CBCT) images from a conventional flat detector-based CBCT scan of clinical patients. Image
quality of retrospectively respiratory- and cardiac-gated volumes from flat detector CBCT scans is deteriorated
by severe sparse projection artifacts. These artifacts further complicate motion estimation, as it is required for
MoCo image reconstruction. For high quality 5D CBCT images at the same x-ray dose and the same number of
projections as todays 3D CBCT we developed a double MoCo approach based on motion vector fields (MVFs)
for respiratory and cardiac motion. In a first step our already published four-dimensional (4D) artifact-specific
cyclic motion-compensation (acMoCo) approach is applied to compensate for the respiratory patient motion.
With this information a cyclic phase-gated deformable heart registration algorithm is applied to the respiratory
motion-compensated 4D CBCT data, thus resulting in cardiac MVFs. We apply these MVFs on double-gated
images and thereby respiratory and cardiac motion-compensated 5D CBCT images are obtained. Our 5D MoCo
approach processing patient data acquired with the TrueBeam 4D CBCT system (Varian Medical Systems). Our
double MoCo approach turned out to be very efficient and removed nearly all streak artifacts due to making use
of 100% of the projection data for each reconstructed frame. The 5D MoCo patient data show fine details and
no motion blurring, even in regions close to the heart where motion is fastest.
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