In conventional tomosynthesis, the x-ray source or detector move relative to the patient so that anatomy at a target depth is focused and other anatomy is blurred. We propose a real-time single frame tomosynthesis design using a distributed source array and a large flat-panel detector. Each element in the source array energizes simultaneously, and the beam is collimated down so that it passes through isocenter and is received in a small sector of the detector. The detector receives multiple non-overlapping x-ray images simultaneously, and averages these to blur anatomy outside the target depth. Reconstruction occurs at the readout rate of the detector, typically 30 frames per second. Single frame tomosynthesis therefore increases temporal resolution at the expense of field of view and number of views. An application of single frame tomosynthesis is the monitoring of lung tumors during stereotactic body radiotherapy (SBRT). External biplane fluoroscopic systems, presently used for management of cranial lesions, could be repurposed with tomosynthesis at moderate cost. In a reader study with two radiation oncologists evaluating 60 simulated cases of lung SBRT, 90% were deemed acceptable for motion management with tomosynthesis compared to 53% with fluoroscopy. We constructed a prototype system using four portable x-ray sources and a fixed collimator and frame and imaged an anthropomorphic lung phantom with a spherical lung nodule embedded, and found that the prototype system showed displayed the lung nodule with better contrast than fluoroscopy.
Adaptive radiotherapy is an effective procedure for the treatment of cancer, where the daily anatomical changes in the patient are quantified, and the dose delivered to the tumor is adapted accordingly. Deformable Image Registration (DIR) inaccuracies and delays in retrieving and registering on-board cone beam CT (CBCT) image datasets from the treatment system with the planning kilo Voltage CT (kVCT) have limited the adaptive workflow to a limited number of patients. In this paper, we present an approach for improving the DIR accuracy using a machine learning approach coupled with biomechanically guided validation. For a given set of 11 planning prostate kVCT datasets and their segmented contours, we first assembled a biomechanical model to generate synthetic abdominal motions, bladder volume changes, and physiological regression. For each of the synthetic CT datasets, we then injected noise and artifacts in the images using a novel procedure in order to mimic closely CBCT datasets. We then considered the simulated CBCT images for training neural networks that predicted the noise and artifact-removed CT images. For this purpose, we employed a constrained generative adversarial neural network, which consisted of two deep neural networks, a generator and a discriminator. The generator produced the artifact-removed CT images while the discriminator computed the accuracy. The deformable image registration (DIR) results were finally validated using the model-generated landmarks. Results showed that the artifact-removed CT matched closely to the planning CT. Comparisons were performed using the image similarity metrics, and a normalized cross correlation of >0.95 was obtained from the cGAN based image enhancement. In addition, when DIR was performed, the landmarks matched within 1.1 +/- 0.5 mm. This demonstrates that using an adversarial DNN-based CBCT enhancement, improved DIR accuracy bolsters adaptive radiotherapy workflow.
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