General surface reconstruction for cone-beam multislice spiral computed tomography.

A new family of cone-beam reconstruction algorithm, the General Surface Reconstruction (GSR), is proposed and formulated in this paper for multislice spiral computed tomography (CT) reconstructions. It provides a general framework to allow the reconstruction of planar or nonplanar surfaces on a set of rebinned short-scan parallel beam projection data. An iterative surface formation method is proposed as an example to show the possibility to form nonplanar reconstruction surfaces to minimize the adverse effect between the collected cone-beam projection data and the reconstruction surfaces. The improvement in accuracy of the nonplanar surfaces over planar surfaces in the two-dimensional approximate cone-beam reconstructions is mathematically proved and demonstrated using numerical simulations. The proposed GSR algorithm is evaluated by the computer simulation of cone-beam spiral scanning geometry and various mathematical phantoms. The results demonstrate that the GSR algorithm generates much better image quality compared to conventional multislice reconstruction algorithms. For a table speed up to 100 mm per rotation, GSR demonstrates good image quality for both the low-contrast ball phantom and thorax phantom. All other performance parameters are comparable to the single-slice 180 degrees LI (linear interpolation) algorithm, which is considered the "gold standard." GSR also achieves high computing efficiency and good temporal resolution, making it an attractive alternative for the reconstruction of next generation multislice spiral CT data.

Exact helical reconstruction using native cone-beam geometries.

This paper is about helical cone-beam reconstruction using the exact filtered backprojection formula recently suggested by Katsevich (2002a Phys. Med. Biol. 47 2583-97). We investigate how to efficiently and accurately implement Katsevich's formula for direct reconstruction from helical cone-beam data measured in two native geometries. The first geometry is the curved detector geometry of third-generation multi-slice CT scanners, and the second geometry is the flat detector geometry of C-arms systems and of most industrial cone-beam CT scanners. For each of these two geometries, we determine processing steps to be applied to the measured data such that the final outcome is an implementation of the Katsevich formula. These steps are first described using continuous-form equations, disregarding the finite detector resolution and the source position sampling. Next, techniques are presented for implementation of these steps with finite data sampling. The performance of these techniques is illustrated for the curved detector geometry of third-generation CT scanners, with 32, 64 and 128 detector rows. In each case, resolution and noise measurements are given along with reconstructions of the FORBILD thorax phantom.

A knowledge-based cone-beam x-ray CT algorithm for dynamic volumetric cardiac imaging.

With the introduction of spiral/helical multislice CT, medical x-ray CT began a transition into cone-beam geometry. The higher speed, thinner slice, and wider coverage with multislice/cone-beam CT indicate a great potential for dynamic volumetric imaging, with cardiac CT studies being the primary example. Existing ECG-gated cardiac CT algorithms have achieved encouraging results, but they do not utilize any time-varying anatomical information of the heart, and need major improvements to meet critical clinical needs. In this paper, we develop a knowledge-based spiral/helical multislice/cone-beam CT approach for dynamic volumetric cardiac imaging. This approach assumes the relationship between the cardiac status and the ECG signal, such as the volume of the left ventricle as a function of the cardiac phase. Our knowledge-based cardiac CT algorithm is evaluated in numerical simulation and patient studies. In the patient studies, the cardiac status is estimated initially from ECG data and subsequently refined with reconstructed images. Our results demonstrate significant image quality improvements in cardiac CT studies, giving clearly better clarity of the chamber boundaries and vascular structures. In conclusion, this approach seems promising for practical cardiac CT screening and diagnosis.