Head Motion Correction Based on Filtered Backprojection in Helical CT Scanning.

Head motion may unexpectedly occur during a CT scan. It thereby results in motion artifacts in a reconstructed image and may lead to a false diagnosis or a failure of diagnosis. To alleviate this motion problem, as a hardware approach, increasing the gantry rotation speed or using an immobilization device is usually considered. These approaches, however, cannot completely resolve the motion problem. Hence, motion estimation (ME) and compensation for it have been explored as a software approach instead. In this paper, adopting the latter approach, we propose a head motion correction algorithm in helical CT scanning, based on filtered backprojection (FBP). For the motion correction, we first introduce a new motion-compensated (MC) reconstruction scheme based on FBP, which is applicable to helical scanning. We then estimate the head motion parameters by using an iterative nonlinear optimization algorithm, or the L-BFGS. Note here that an objective function for the optimization is defined on reconstructed images in each iteration, which are obtained by using the proposed MC reconstruction scheme. Using the estimated motion parameters, we then obtain the final MC reconstructed image. Using numerical and physical phantom datasets along with simulated head motions, we demonstrate that the proposed algorithm can provide significantly improved quality to MC reconstructed images by alleviating motion artifacts.

Head motion correction based on filtered backprojection for x-ray CT imaging.

PURPOSE: For head x-ray CT imaging, the head needs to remain motionless during the scan. In clinical practice, however, head motion is sometimes unavoidable depending on the patient. The motion can occur abruptly during the scan and can be unpredictable. It thereby causes motion artifacts such as tissue blurring or doubled edges around the skull area. To mitigate this problem, we propose a 3D head motion estimation (ME) and compensation algorithm based on filtered backprojection. METHODS: If a patient moves his or her head during the scan, a motion-corrupted sinogram is obtained. Modeling the head motion as a 3D rigid transformation, we develop a motion-compensated (MC) reconstruction algorithm based on the FDK algorithm. To determine the head motion of a rigid transformation, we propose two optimization-based ME schemes depending on the degree of head motion, both of which are performed by updating motion parameters and the corresponding MC reconstructed image alternatively until the proposed cost function is minimized for the MC reconstructed image. In particular, to improve the robustness in the case of large motion, we propose attaching a fiducial marker to the head so that more reliable motion parameters can be initialized by determining the marker position, before the optimization. To evaluate the proposed algorithm, a numerical phantom with realistic, continuous, and smoothly varying motion, and a moving physical phantom are used with a gantry rotation time of 1 s. RESULTS: In the simulation using a numerical phantom and in the experiment using a physical phantom, the proposed algorithm provides well-restored 3D motion-compensated images in both cases of small and large motion. In particular, in the case of large motion of the physical phantom, using a fiducial marker, we obtain remarkable improvement of image quality in cerebral arteries and a lesion as well as the skull. Quantitative evaluations using the image sharpness and root-mean-square error also show noticeable improvement of image quality in both simulations and experiments. CONCLUSIONS: We propose a framework for head motion correction in an axial CT scan, which consists of motion estimation and compensation steps. Two image-based ME algorithms for rigid motion tracking are developed according to the degree of head motion. The estimated motion information is then used for MC image reconstruction. Both motion estimation and compensation algorithms are based on computationally efficient filtered backprojection. Excellent performance of the proposed framework is illustrated by means of simulations using a numerical phantom and experiments using a physical phantom.

Defining a new annotation object for DICOM image: a practical approach.

In this article, we present a new way of creating annotation objects for DICOM images, using the redundant data channel. Various types of annotations, including types containing color information, are possible and annotation objects can overlap the original DICOM image on a screen. Annotation objects can be created easily using a digital pen. Scanned images used in an electronic patient record can be added to objects. Although there are various ways of manipulating annotation objects, such as insertion, addition and modification of annotation objects in the DICOM image, the original clinical image is not affected because a redundant data channel is used for the annotation. The proposed method is expected to be very useful to medium and small clinics that cannot afford picture archiving and communication systems, as the DICOM standard makes provision for the annotation of clinical images in various ways.