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SUMMARYThe purpose of the study was to review the prevalence of significant extracoronary findings in patients who underwent multislice CT coronary angiography examinations and coronary artery calcium scoring examinations. We reviewed the reports of 295 consecutive patients who underwent multislice CT coronary angiography examinations and 140 consecutive patients who had separate coronary calcium scoring examinations from September 2004 to March 2006 in our department's radiology information system. Additional investigations carried out as a result of these findings were also recorded. Fifty-six (19%) out of 295 patients had significant extracoronary findings on coronary CT angiography requiring clinical or radiological follow up. There were 60 significant extracoronary findings. These included 24 patients who had pulmonary abnormalities, 4 who had mediastinal abnormalities, 20 who had upper abdominal abnormalities and 5 who had non-coronary cardiac abnormalities. Three patients had both pulmonary and upper abdominal abnormalities. Eleven (8%) out of 140 patients had significant pulmonary, breast, mediastinal, upper abdominal and cardiac abnormalities on coronary artery calcium scoring examinations, yielding a total of 12 significant findings. In our experience, 19% of the patients who underwent multislice CT coronary angiography and 8% of the patients who underwent coronary artery calcium scoring examinations had significant extracoronary findings requiring follow up. It is therefore imperative for the reporting physician to review the entire examination after the coronary arteries have been assessed. The prevalence of extracoronary findings on these examinations may be of significance, resulting in additional 'hidden costs' if widespread 'screening' is adopted.

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This paper presents a FPGA-based method for on-board detection and matching of the feature points. With the proposed method, a parallel processing model and a pipeline structure are presented to ensure a high frame rate at processing speed, but with a low power consumption. To save the FPGA resources and increase the processing speed, a model which combines the modified SURF detector and a BRIEF descriptor, is presented as well. Three pairs of images with different land coverages are used to evaluate the performance of FPGA-based implementation. The experiment results demonstrate that (1) when the image pairs with artificial features (such as buildings and roads), the performance of FPGA-based implementation is better than those image pairs with natural features (such as woods); (2) the proposed FPGA-based method is capable of ensuring the processing speed at a high frame rate, such as the speed of can achieve 304 fps under a 100 MHz clock frequency. The speedup of the proposed implementation is about 27 times higher than that when using the PC-based implementation.

Although some researchers have proposed the Field Programmable Gate Array (FPGA) architectures of Feature From Accelerated Segment Test (FAST) and Binary Robust Independent Elementary Features (BRIEF) algorithm, there is no consideration of image data storage in these traditional architectures that will result in no image data that can be reused by the follow-up algorithms. This paper proposes a new FPGA architecture that considers the reuse of sub-image data. In the proposed architecture, a remainder-based method is firstly designed for reading the sub-image, a FAST detector and a BRIEF descriptor are combined for corner detection and matching. Six pairs of satellite images with different textures, which are located in the Mentougou district, Beijing, China, are used to evaluate the performance of the proposed architecture. The Modelsim simulation results found that: (i) the proposed architecture is effective for sub-image reading from DDR3 at a minimum cost; (ii) the FPGA implementation is corrected and efficient for corner detection and matching, such as the average value of matching rate of natural areas and artificial areas are approximately 67% and 83%, respectively, which are close to PC’s and the processing speed by FPGA is approximately 31 and 2.5 times faster than those by PC processing and by GPU processing, respectively.

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