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Purpose: A method to reduce noise resulting from the use of higher resolution x‐ray detectors being developed to meet the demands of image guided vascular interventions is demonstrated. Methods: New direct detectors based on amorphous Se can have MTFs that remain high even at their Nyquist frequency. Since such detectors can be made with smaller pixels than may be required for even the high resolution requirements of many neurovascular applications, the resulting over‐sampled images can be convolved with various functions to lower the noise. The effect on resolution can then be compared with a simple pixel binning. The general method proposed by Cunningham et al as the apodized‐aperture pixel (AAP) design was compared with the Result for various simple 1D convolution spread functions with the effects on total noise and MTF compared to that of the binning case for a 25 μm aSe CMOS detector's MTF published by the University of Waterloo group lead by KS Karim. Results: Assuming a white noise image, various convolution kernels resulted in similar reductions of total standard deviation. Detailed comparisons were made with the simple the 2x binning case. While reducing noise, the over‐sampling convolution method using simple convolution low pass filters did not show advantage over 2x binning with regard to modifying the MTF; however, significant improvement was evidenced for the more complex sinc function used in the AAP design. Conclusion: As higher resolution detectors are being developed to meet the increasing demands for improved images to guide finer vascular interventions, use of super‐sampled aSe detectors where resolution is maintained with reduced noise may well fill this requirement. Partial support from NIH Grant R01EB002873 and Toshiba Medical Systems Equipment Grant

Purpose: ROI fluoroscopy involves the use of an x‐ray beam attenuator with higher attenuation in the periphery than the center thus allowing for dose reduction to the patient. This study presents the design considerations for placing an x‐ray ROI attenuator made of copper inside the collimator assembly of an angiographic c‐arm. Methods: The two important considerations for the design of the attenuator are the size of the ROI and the attenuation (and hence thickness of the material) needed outside the ROI. An attenuation of 80% outside the ROI, and none inside the ROI was assumed. To calculate the thickness, exposures were measured for different thicknesses of copper at various kVp's and different inherent filtration of the system. Attenuation percentage was calculated from these readings and the thickness of copper was determined. The field‐of‐view (FOV) requirement depends on the type of procedure: smaller for a neurovascular intervention and larger for a cardiac procedure. An average FOV of 33% of 21cm × 21cm at 100cm SID with a circular ROI was assumed to calculate the diameter of the ROI in the attenuator. Results: For kVp's ranging from 80 to 90, with an added filtration of 0.2mm copper, to get an average attenuation of 80%, 0.7mm of copper was needed for the thickness of the attenuator. The attenuator was placed 13cm from the focal spot and the diameter of the ROI at this distance was calculated to be 10mm. Conclusion: The ROI attenuator can be mounted inside the beam limiting mechanism of the c‐arm. This allows for the flexibility in the usage of this technique during fluoroscopic interventions, thus achieving patient‐dose reduction. Since the attenuation for copper varies with varying kVp, different masks for different kVp's are to be used for brightness equalization.

Purpose: CMOS‐based aSe detectors compared to CsI‐TFT‐based flat panels have the advantages of higher spatial sampling due to smaller pixel size and decreased blurring characteristic of direct rather than indirect detection. For systems with such detectors, the limiting factor degrading image resolution then becomes the focal‐spot geometric unsharpness. This effect can seriously limit the use of such detectors in areas such as cone beam computed tomography, clinical fluoroscopy and angiography. In this work a technique to remove the effect of focal‐spot blur is presented for a simulated aSe detector. Method: To simulate images from an aSe detector affected with focal‐spot blur, first a set of high‐resolution images of a stent (FRED from Microvention, Inc.) were acquired using a 75µm pixel size Dexela‐Perkin‐Elmer detector and averaged to reduce quantum noise. Then the averaged image was blurred with a known Gaussian blur at two different magnifications to simulate an idealized focal spot. The blurred images were then deconvolved with a set of different Gaussian blurs to remove the effect of focal‐spot blurring using a threshold‐based, inverse‐filtering method. Results: The blur was removed by deconvolving the images using a set of Gaussian functions for both magnifications. Selecting the correct function resulted in an image close to the original; however, selection of too wide a function would cause severe artifacts. Conclusion: Experimentally, focal‐spot blur at different magnifications can be measured using a pin hole with a high resolution detector. This spread function can be used to deblur the input images that are acquired at corresponding magnifications to correct for the focal spot blur. For CBCT applications, the magnification of specific objects can be obtained using initial reconstructions then corrected for focal‐spot blurring to improve resolution. Similarly, if object magnification can be determined such correction may be applied in fluoroscopy and angiography.

Purpose: The sensitivity of a new 3D Multi‐View Reconstruction (MVR) angiography technique to the projection angles used is evaluated by comparing 3D centerlines calculated from combinations of three projections acquired from two imaging systems with that from micro‐Cone Beam CT (μCBCT), which is taken as truth. Method and Materials: A 3D centerline of a contrast‐filled carotid vessel phantom was reconstructed from image data acquired using a custom‐made μCBCT system with a microangiographic (MA) detector (45 μm pixels, 4.5 cm field‐of‐view (FOV)). Projection images of the same phantom were also acquired using the MA and an image intensifier (II) detector system (120 μm pixels, 4.5 in FOV) on a C‐arm x‐ray unit. The MVR technique was used to compute 3D centerlines for 12 combinations of projection angles. Each 3D MVR centerline was aligned with the μCBCT “true” 3D centerline using a Procrustes technique, and a root‐mean‐square (RMS) deviation was calculated. Results: The average RMS deviation for the MA‐MVR centerlines is 25 μm with a standard deviation of 3 μm over the 12 different projection‐angle combinations, whereas the average RMS deviation for the II‐MVR centerlines is 41 μm with a standard deviation of 4 μm over these same combinations. The RMS deviation as a percent of the internal vessel diameter, 0.75 mm, is 3.3% for the MA and 5.5% for the II and appears to be independent of view selection. Conclusion: For the MVR technique, the improved resolution of the MA resulted in improved centerline determination compared to the II system. For both detectors, the selection of a particular projection set had little effect on the RMS centerline deviation. The low RMS deviations for both detectors indicate that the MVR technique can provide accurate 3D centerlines. (Partial support from NIH Grants R01‐NS43924, R01‐EB02873, R01‐HL52567, R01‐EB02916, and Toshiba Medical Systems Corporation).