A spatio-temporal detective quantum efficiency and its application to fluoroscopic systems.

PURPOSE: Fluoroscopic x-ray imaging systems are used extensively in spatio-temporal detection tasks and require a spatio-temporal description of system performance. No accepted metric exists that describes spatio-temporal fluoroscopic performance. The detective quantum efficiency (DQE) is a metric widely used in radiography to quantify system performance and as a surrogate measure of patient "dose efficiency". It has been applied previously to fluoroscopic systems with the introduction of a temporal correction factor. However, the use of a temporally-corrected DQE does not provide system temporal information and it is only valid under specific conditions, many of which are not likely to be satisfied by suboptimal systems. The authors propose a spatio-temporal DQE that describes performance in both space and time and is applicable to all spatio-temporal quantum-based imaging systems. METHODS: The authors define a spatio-temporal DQE (two spatial-frequency axes and one temporal-frequency axis) in terms of a small-signal spatio-temporal modulation transfer function (MTF) and spatio-temporal noise power spectrum (NPS). Measurements were made on an x-ray image intensifier-based bench-top system using continuous fluoroscopy with an RQA-5 beam at 3.9 microR/frame and hardened 50 kVp beam (0.8 mm Cu filtration added) at 1.9 microR/frame. RESULTS: A zero-frequency DQE value of 0.64 was measured under both conditions. Nonideal performance was noted at both larger spatial and temporal frequencies; DQE values decreased by approximately 50% at the cutoff temporal frequency of 15 Hz. CONCLUSIONS: The spatio-temporal DQE enables measurements of decreased temporal system performance at larger temporal frequencies analogous to previous measurements of decreased (spatial) performance. This marks the first time that system performance and dose efficiency in both space and time have been measured on a fluoroscopic system using DQE and is the first step toward the generalized use of DQE on clinical fluoroscopic systems.

A small-signal approach to temporal modulation transfer functions with exposure-rate dependence and its application to fluoroscopic detective quantum efficiency.

The detective quantum efficiency (DQE) is a metric widely used in radiography to quantify system performance and as a surrogate measure of patient "dose efficiency." It has been applied previously to fluoroscopic systems with the introduction of a temporal correction factor. Calculation of this correction factor relies on measurements of the temporal modulation transfer function (MTF). However, the temporal MTF is often exposure-rate dependent, violating a necessary Fourier linearity requirement. The authors show that a Fourier analysis is appropriate for fluoroscopic systems if a "small-signal" approach is used. Using a semitransparent edge, a lag-corrected DQE is described and measured for an x-ray image intensifier-based fluoroscopic system under continuous (non-pulsed) exposure conditions. It was found that results were equivalent for both rising and falling-edge profiles independent of edge attenuation when effective attenuation was in the range of 0.1-0.6. This suggests that this range is appropriate for measuring the small-signal temporal MTF. In general, lag was greatest at low exposure rates. It was also found that results obtained using a falling-edge profile with a radiopaque edge were equivalent to the small-signal results for the test system. If this result is found to be true generally, it removes the need for the small-signal approach. Lag-corrected DQE values were validated by comparison with radiographic DQE values obtained using very long exposures under the same conditions. Lag was observed to inflate DQE measurements by up to 50% when ignored.

Normalization of the modulation transfer function: the open-field approach.

The modulation transfer function (MTF) is widely used to describe the spatial resolution of x-ray imaging systems. The MTF is defined to have a zero-frequency value of unity, and it is common practice to ensure this by normalizing a measured MTF curve by the zero-frequency value. However, truncation of the line spread function (LSF) within a finite region of interest (ROI) results in spectral leakage and causes a reduction in the measured MTF zero-frequency value equal to the area of truncated LSF tails. Subsequent normalization by this value may result in inflated MTF values. We show that open-field normalization with the edge method produces accurate MTF values at all nonzero frequencies without need for further normalization by the zero-frequency value, regardless of ROI size. While both normalization techniques are equivalent for a sufficiently large ROI, a 5% inflation in MTF values was observed for a CsI-based flat-panel system when using a 10 cm ROI. Use of open-field normalization avoids potential inflation caused by zero-frequency normalization.

A moving slanted-edge method to measure the temporal modulation transfer function of fluoroscopic systems.

Lag in fluoroscopic systems introduces a frame-averaging effect that reduces measurements of image noise and incorrectly inflates measurements of the detective quantum efficiency (DQE). A correction can be implemented based on measurements of the temporal modulation transfer function (MTF). We introduce a method of measuring the temporal MTF under fluoroscopic conditions using a moving slanted edge, a generalization of the slanted-edge method used to measure the (spatial) MTF, providing the temporal MTF of the entire imaging system. The method uses a single x-ray exposure, constant edge velocity, and assumes spatial and temporal blurring are separable. The method was validated on a laboratory x-ray image intensifier (XRII) system by comparison with direct measurements of the XRII optical response, showing excellent agreement over the entire frequency range tested (+/- 100 Hz). With proper access to linearized data and continuous fluoroscopy, this method can be implemented in a clinical setting on both XRII and flat-panel detectors. It is shown that the temporal MTF of the CsI-based validation system is a function of exposure rate. The rising-edge response showed more lag than the falling edge, and the temporal MTF decreased with decreasing exposure rate. It is suggested that a small-signal approach, in which the range of exposure rates is restricted to a linear range by using a semitransparent moving edge, would be appropriate for measuring the DQE of these systems.

Semi-automated and automated glioma grading using dynamic susceptibility-weighted contrast-enhanced perfusion MRI relative cerebral blood volume measurements.

OBJECTIVE: Despite the established role of MRI in the diagnosis of brain tumours, histopathological assessment remains the clinically used technique, especially for the glioma group. Relative cerebral blood volume (rCBV) is a dynamic susceptibility-weighted contrast-enhanced perfusion MRI parameter that has been shown to correlate to tumour grade, but assessment requires a specialist and is time consuming. We developed analysis software to determine glioma gradings from perfusion rCBV scans in a manner that is quick, easy and does not require a specialist operator. METHODS: MRI perfusion data from 47 patients with different histopathological grades of glioma were analysed with custom-designed software. Semi-automated analysis was performed with a specialist and non-specialist operator separately determining the maximum rCBV value corresponding to the tumour. Automated histogram analysis was performed by calculating the mean, standard deviation, median, mode, skewness and kurtosis of rCBV values. All values were compared with the histopathologically assessed tumour grade. RESULTS: A strong correlation between specialist and non-specialist observer measurements was found. Significantly different values were obtained between tumour grades using both semi-automated and automated techniques, consistent with previous results. The raw (unnormalised) data single-pixel maximum rCBV semi-automated analysis value had the strongest correlation with glioma grade. Standard deviation of the raw data had the strongest correlation of the automated analysis. CONCLUSION: Semi-automated calculation of raw maximum rCBV value was the best indicator of tumour grade and does not require a specialist operator. ADVANCES IN KNOWLEDGE: Both semi-automated and automated MRI perfusion techniques provide viable non-invasive alternatives to biopsy for glioma tumour grading.

Sci-Sat AM(1): Imaging-07: Open field normalization: How to avoid inflation to MTF and DQE values caused by zero-frequency normalization.

PURPOSE: To show that the novel open-field normalization technique prevents a common error in calculation of the detective quantum efficiency (DQE) caused by zero-frequency normalization of the modulation transfer function (MTF). METHOD AND MATERIALS: Models describing zero-frequency and open-field normalization were used to derive the resulting measured MTF, noise power spectrum (NPS) and DQE using a finite region of interest (ROI) of image data. Simulated one-dimensional images containing Gaussian blur were used to model a deterministic system and to calculate the resultant values. Measurements were made using both zero-frequency and open-field normalization with ROIs ranging in size from 1-10 cm. RESULTS: Use of a finite ROI results in truncation of the system line-spread function (LSF) causing the zero-frequency value of the measured MTF to be less than the true MTF value of unity, and causes spectral leakage in both the MTF and NPS. Zero-frequency normalization of the MTF inflates values at all non-zero frequencies. Since no zero-frequency normalization is performed on the NPS, this causes inflated DQE values. Simulated results show a 6% inflation of DQE values for a ROI of 10 cm, which increases as the ROI is reduced. Open-field normalization accurately determines MTF and NPS (and thus DQE) values at all frequencies away from zero frequency. CONCLUSION: Open-field normalization measurements provide a good estimate of the true MTF and DQE. This approach should be used to avoid a common error in DQE calculations that is not obvious and inflates DQE calculations by 5-20%.