A computed tomography (CT) scanner includes an x-ray tube mounted on a rotatable gantry that rotates around an examination region about a longitudinal or z-axis. The x-ray tube emits radiation that traverses the examination region and a subject or object therein. A detector array subtends an angular arc opposite the examination region from the x-ray tube. The detector array detects radiation that traverses the examination region (and the subject or object therein) and generates projection data indicative thereof. A reconstructor reconstructs the projection data and generates volumetric image data indicative thereof.
Unfortunately, CT scanners emit ionizing radiation and thus expose the patient to ionizing radiation, which may increase risk of cancer. Generally, the radiation dose deposited in the patient depends on multiple factors, including, but not limited to, tube current (mAs), tube voltage (kVp), exposure time (for helical scans), and slice thickness and spacing (for axial scans). As such, the deposited dose can be reduced by adjusting one or more of the above. However, image noise is inversely proportional to radiation dose, and thus reducing radiation dose not only reduces the dose deposited in the patient but also increases image noise in the acquired data, which is propagated to the images during reconstruction, reducing image quality (i.e., noisier images), which may degrade the diagnostic value of the procedure.
One approach to mitigating the increased noise is to use a statistical iterative reconstruction technique such as the Maximum Likelihood (ML) approach with a strong penalty on the noise in the image (e.g., a “penalized likelihood” or “regularization” approach). However, for low-contrast objects, e.g., where the contrast level is near the noise level, the regularization results in edges that may appear sharp in a single realization of noise, but actually contain a large amount of uncertainty due to the noise. This can be seen in FIG. 1, in which profiles 102 represents a sharp FBP reconstruction of a low contrast region of a scanned object and profiles 104 and 106 represent regularized ML iterative reconstructions of the low contrast region of the scanned object. As can be seen, the ML the profiles 104 and 106 have lower resolution than the FBP profile 102, and the mean of ML profiles 104 and 106 is less sharp than the FBP profile 102.
For multiple realizations of the noise, the heavily regularized images exhibit a contrast-dependent resolution in which, in the mean image, the regularized image will have a resolution less than but closer to that of a conventional filtered-backprojection (FBP) image with a very smooth filter in low-contrast regions while retaining a much sharper resolution in high contrast regions. Unfortunately, the forward and back projection operations of a regularized-likelihood iterative reconstruction are computationally expensive, even with acceleration of parallel hardware such graphic processing units (GPUs). As a consequence, iterative regularized-likelihood based reconstructions come with a large computational cost, which may hinder or prohibit their use in routine medical diagnostic practice.