In modern spiral CT devices, patients move through a ring-shaped tunnel (gantry) at a specific speed, while the X-ray tube assembly-detector combination continuously rotates around their body. Mathematical procedures calculate the attenuation coefficient in the cross-section plane as well as the spatial distribution of density from the attenuation of the radiation as it passes through the body. These measuring values, or raw data, are then used to reconstruct clinical images at different spatial planes, such as axial, frontal, sagittal etc. The standard reconstruction method currently in use is Filtered Back Projection (FBP), an algorithm that converts the raw data into image data with filtering and back projection to the image plane. This process involves a compromise between spatial image resolution or image quality, and image noise. The dose must be increased to lower the image noise and achieve better image quality.
Iterative reconstruction was first described in the 1970s as a promising method to generate clinical images with low noise. The image generation process of this procedure includes a "correction loop", in which the sectional images are calculated in stages by a gradual approximation to the actual density distribution. For this purpose, the system makes an assumption about the density distribution of the tissue slices to be examined and calculates an output image. New, synthetic projection data are generated from this output image and compared to the actual, "real" raw measuring data. If they don't match, the system will calculate a corresponding correction image to correct the output image. In a next step, the system will again synthesize the projection data and compare them to the measured raw data. This iteration continues until a specified abort criterion is met. After this process, the corrected image shows improved spatial image resolution in highcontrast regions, while the image noise in low-contrast areas is reduced. The image becomes softer in tissue regions with homogeneous density, while high-contrast tissue boundaries are maintained. As a consequence, image resolution and image noise are no longer tied to one another. One problem associated with the method is the fact that the measuring system of the CT device must be precisely modeled mathematically during the computation of the synthetic projection data, which requires immense computing power. In addition, a large number of iterations is required. As a consequence, the calculation time for reconstruction and the computing capacity requirements increase to such an extent that the procedure cannot be practically applied in clinical settings.
Until recently, the so-called "statistical iterative reconstruction" was considered a solution. It avoids the exact mathematical modeling of the measuring system and drastically reduces the number of iterations to avoid lengthy computing times. A large portion of noise is removed on the basis of a simple statistical correction model, which only focuses on the noise properties of the measuring data. This aggressive method accelerates the lower-noise reconstruction of the images considerably, but generates sectional images that may differ so substantially from the results of the standard FBP that radiologists are often disturbed by the texture.
In contrast to "statistical iterative reconstruction", the reconstruction algorithm Iterative Reconstruction in Image Space (IRIS) by Siemens Healthcare uses a different approach to accelerate the image reconstruction. The core of the innovative approach is the fact that all raw data information is transferred from the slow-processing raw data area to the more efficient image data area in the first reconstruction cycle. The resulting "master image" contains finest details, but also significant image noise, which is removed in the subsequent iterative steps in the image data area. In this manner, the image is gradually cleared of image noise and artifacts in small iterative steps that do not affect the high spatial image resolution. This eliminates the need for timeconsuming back projections. The novel approach allows Siemens experts to simply construct a highly precise reflection of the actual properties of the final image from the raw data of a CT scan with relatively little computing effort. IRIS, which allows scanning with up to 60 percent less radiation, can reach the same signal to noise ratio as Filtered Back Projection (FBP) with a full dose.
As a consequence, the new algorithm is able to significantly reduce the radiation dose without quality losses. As an alternative, the iterative reconstruction method by Siemens can also be used to substantially increase the image quality of reconstructed images with the same dose. This was confirmed by U. Joseph Schoepf, MD, Professor of Radiology and Cardiology, Director of CT Research and Development at the Medical University of South Carolina: "Iterative Reconstruction in Image Space lets me save up to 60 percent of the radiation dose in a number of routine applications, while maintaining the usual excellent image quality."
"Radiation protection and dose reduction in CT have been top priorities of Siemens Healthcare ever since the company came out with the first computed tomograph (CT) in 1974. We have already introduced a series of technical innovations to our CT systems that contribute to dose reduction," explained Dr. Sami Atiya, CEO Computed Tomography at Siemens Healthcare. "With IRIS we can significantly reduce radiation exposure in virtually all CT examinations."
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About Siemens Healthcare
The Siemens Healthcare Sector is one of the world's largest suppliers to the healthcare industry and a trendsetter in medical imaging, laboratory diagnostics, medical information technology and hearing aids. Siemens is the only company to offer customers products and solutions for the entire range of patient care from a single source - from prevention and early detection to diagnosis, and on to treatment and aftercare. By optimizing clinical workflows for the most common diseases, Siemens also makes healthcare faster, better and more cost-effective. Siemens Healthcare employs some 49,000 employees worldwide and operates in over 130 countries. In fiscal year 2008 (to September 30), the Sector posted revenue of 11.2 billion euros and profit of 1.2 billion euros. For further information, please visit www.siemens.com/healthcare.