Optimizing the x-ray photon energy for digital radiographic imaging systems

In this study, we investigated which photon energy results in the lowest patient does when the image contrast to nosie ratio (CNR) is kept constant. This optimum photon energy was obtained for a range of patient sizes, as well as for the detection of lesions with atomic number ranging from 6.5 to 53. Mono-energetic photons from 20 keV to 140 keV were investigated with an x-ray detector that had 100 percent quantum detection efficiency. Patients were modeled as slabs of water with a thickness that ranged from 5 to 30 cm. Image contrast was computed from the x-ray attenuation of small lesions consisting of low Z materials as well as higher Z materials. Relative values of the CNR as a function of the x-ray photon energy were obtained by assuming that the image noise was proportional to the square root of the incident number of x-ray photons under scatter free conditions. The energy imparted to the patient as a function of photon energy was obtained using published data of the absorbed percentage of the incident energy fluence. For each patient thickness and lesion composition, the CNR was kept constant by appropriate adjustment of the x-ray beam intensity, and the corresponding x-ray photon energy that resulted in the minimum patient dose was determined. The optimum photon energy for detection ga low Z lesion increased monotonically from approximately 62 keV for small patients to approximately 78 keV for large patients. For high Z lesions, optimum photon energies were approximately 34 keV for small patients, and increased to approximately 40 keV for large patients. The optimum photon energy was found to vary by about a factor of two over the range of patient thickness investigated. The optimum photon energy also varied by a factor of two for the range of detection tasks investigated.

[1]  M Danos,et al.  Image information content and patient exposure. , 1978, Medical physics.

[2]  J. Bushberg.  The Essential Physics of Medical Imaging , 2001 .

[3]  S. Kheddache,et al.  In search of optimum chest radiography techniques. , 1993, The British journal of radiology.

[4]  D. Dance,et al.  The physical performance of different x-ray contrast agents: calculations using a Monte Carlo model of the imaging chain. , 1995, Physics in medicine and biology.

[5]  R G Zamenhof,et al.  The optimization of signal detectability in digital fluoroscopy. , 1982, Medical physics.

[6]  G S Shaber,et al.  Dual-energy mammography: a detector analysis. , 1990, Medical physics.

[7]  H Bernstein,et al.  An approach to specifying a minimum dose system for mammography using multiparameter optimization techniques. , 1985, Medical physics.

[8]  J Yorkston,et al.  Signal, noise power spectrum, and detective quantum efficiency of indirect-detection flat-panel imagers for diagnostic radiology. , 1998, Medical physics.

[9]  J A Seibert,et al.  Monte Carlo simulation of the scattered radiation distribution in diagnostic radiology. , 1988, Medical physics.

[10]  Andrzej Krol,et al.  Signal-to-noise ratio and radiation dose as a function of photon energy in mammography , 1998, Medical Imaging.

[11]  D R Dance,et al.  Schemes for the optimization of chest radiography using a computer model of the patient and x-ray imaging system. , 2001, Medical physics.

[12]  J M Boone.  Parametrized x-ray absorption in diagnostic radiology from Monte Carlo calculations: implications for x-ray detector design. , 1992, Medical physics.

[13]  J. H. Hubbell,et al.  Photon cross sections, attenuation coefficients, and energy absorption coefficients from 10 keV to 100 GeV , 1969 .

[14]  J A Seibert,et al.  A Monte Carlo study of x-ray fluorescence in x-ray detectors. , 1999, Medical physics.