Dose reduced digital medical image simulations

Abstract

A method for providing a reduced exposure value for radiographic imaging obtains a set of flat-field images at two or more exposure values and measures the noise power spectra using the flat field images. At least one noise table is generated according to interpolated noise power spectra for a set of predetermined exposure values. Values from the at least one noise table are applied to a clinical image to form a reduced exposure simulation image. A noise mask is generated according to at least one noise table and the exposure values of the reduced exposure simulation image and added to the reduced exposure simulation image. The reduced exposure simulation image is assessed to generate a desired dose reduction factor.

Description

FIELD OF THE INVENTION[0001]This invention generally relates to diagnostic imaging and more particularly relates to a method for simulating a reduced dose digital medical image that can be used for determining a low achievable radiation level for a given diagnostic task.BACKGROUND OF THE INVENTION[0002]While x-rays have significant value for diagnosing the condition of a patient, ionizing X-ray radiation can be harmful to living tissue. Accordingly, with the intent of reducing radiation risks wherever possible, the International Commission on Radiological Protection (ICRP), beginning in 1977, has proposed that a policy of ALARA (As Low As Reasonably Achievable) be adopted for radiological personnel and, more recently, for patients who undergo x-ray imaging.[0003]To address this problem, manufacturers and users of x-ray equipment have expended efforts in developing both threshold settings and procedural techniques that help to reduce exposure levels. For example, technique charts tha...

AI Technical Summary

Benefits of technology

[0014]An advantage of the present invention that it provides a method for reducing x-ray exposure levels using simulation data.

Problems solved by technology

While x-rays have significant value for diagnosing the condition of a patient, ionizing X-ray radiation can be harmful to living tissue.

Incorrectly reducing X-ray exposure levels may result in poor quality images with reduced diagnostic value.

Images produced with too little exposure can be characterized by problems such as excessive graininess and low contrast.

Such images may be difficult to use and could potentially compromise diagnosis.

In some cases, problems such as these require images to be re-taken.

However, this simplified noise model does not appear to adequately characterize noise, which is considered to have a more complex relationship to exposure and to spatial frequency in many cases, as described subsequently in the detailed description of the invention.

As with Hartley, the technique proposed in the Timberg article applies a limited characterization of imaging noise to the problem.

However, for imaging related to living tissue, noise has proven to be more complex and a more comprehensive solution is needed.

However, it has been found that the noise power spectrum (NPS) for radiation images is more complex and that image noise has multiple components, including a number of components that exhibit non-linear response to dose.

While a simple model may be suitable in comparing image noise against a threshold value, however, it is not suited for producing accurate low-dose image simulations from a higher-dose set of images.

As a result, the type of solution proposed in Hartley, Massoumzadeh, or Timberg can be unsuitable in cases where a test image has been captured at low dosage levels, images have a large exposure latitude as, for example, in chest radiography, or where electronic noise of the detector is otherwise a significant factor.

Images