Correction of scatter in megavoltage cone-beam CT

Abstract

The role of scatter in a cone-beam computed tomography system using the therapeutic beam of a medical linear accelerator and a commercial electronic portal imaging device (EPID) is investigated. A scatter correction method is presented which is based on a superposition of Monte Carlo generated scatter kernels. The kernels are adapted to both the spectral response of the EPID and the dimensions of the phantom being scanned. The method is part of a calibration procedure which converts the measured transmission data acquired for each projection angle into water-equivalent thicknesses. Tomographic reconstruction of the projections then yields an estimate of the electron density distribution of the phantom. It is found that scatter produces cupping artefacts in the reconstructed tomograms. Furthermore, reconstructed electron densities deviate greatly (by about 30%) from their expected values. The scatter correction method removes the cupping artefacts and decreases the deviations from 30% down to about 8%.

Deep Scatter Estimation (DSE): Accurate Real-Time Scatter Estimation for X-Ray CT Using a Deep Convolutional Neural Network

“…The scatter propagation kernel accounts for the spatial distribution of the scattered X-rays and depends on several parameters such as the acquisition geometry, the spectral distribution of X-rays and the object itself. Different approaches to set the scatter source term and the scatter propagation kernel have been proposed [2,11,14,15,18,21,23,[31][32][33][34]. Basically, they can be divided into model-based and MC-based approaches.…”

Dose Recalculation and the Dose-Guided Radiation Therapy (DGRT) Process Using Megavoltage Cone-Beam CT

“…As with most cone-beam systems, a typical cupping artifact, resulting from scatter and beam hardening, is present in the images obtained from our MVCBCT system (18). In addition, a missing data artifact also appears if the section of the patient being imaged is larger than the field-of-view of the system.…”

Experimental Study of Intracranial Hematoma Detection with Flat Panel Detector C-Arm CT

“…The correction approach implemented here estimates the primary radiation from the projection data by using an iterative method involving a multiplicative correction step, 19 and further improvements by using iterative techniques, hardware approaches, or a combination of the 2 are possible. [20][21][22][23][24][25][26][27][28][29][30] The number of views taken over a partial-circle scan can also be a limiting factor for image quality if insufficient angular sampling (ie, too large an angle between views) occurs. The Feldkamp reconstruction algorithm 31 with a generalized Parker weighting scheme, 32 acquiring projections over at least 180°plus the fan angle of the x-ray beam, with 538 views spaced 0.4°apart, provided images that were free of view aliasing on the midplane.…”