Abstract: One example method includes discovering a mapping of software components to source files, mapping code changes of a software build to the software components, collecting metric data for test cases, and based on the mapping, and the collecting, selecting a test set, which may include one or more test cases, that covers all the code changes so that when one or more tests of the test case are run, the tests operate to test all the code changes.

Abstract: A method for acquiring a blood flow volume and a blood flow velocity of a coronary artery is provided. The method comprises: acquiring image information of a coronary artery to obtain geometrical feature data of the coronary artery; obtaining, according to the geometrical feature data of the coronary artery, the total volume V of a reference lumen of the coronary artery; and calculating a blood flow volume Q at coronary artery ostia according to the following formula: Q = K * V 3 4 , wherein when the unit of Q is mm3/s, and when the unit of V is mm3, a value range of K is 5-9.5. A blood flow volume and a blood flow velocity of a coronary artery are obtained by means of image information of the coronary artery. Compared with a method, in the prior art, for estimating a blood flow volume of a coronary artery by means of the size of a cardiac muscle, this method is simpler, and can provide a more accurate boundary condition for image-based hemodynamic calculation.

Abstract: A method for acquiring a blood flow volume and a blood flow velocity of a coronary artery is provided. The method comprises: acquiring image information of a coronary artery to obtain geometrical feature data of the coronary artery; obtaining, according to the geometrical feature data of the coronary artery, the total volume V of a reference lumen of the coronary artery; and calculating a blood flow volume Q at coronary artery ostia according to the following formula: Q = K * V 3 4 , wherein when the unit of Q is mm3/s, and when the unit of V is mm3, a value range of K is 5-9.5. A blood flow volume and a blood flow velocity of a coronary artery are obtained by means of image information of the coronary artery. Compared with a method, in the prior art, for estimating a blood flow volume of a coronary artery by means of the size of a cardiac muscle, this method is simpler, and can provide a more accurate boundary condition for image-based hemodynamic calculation.

Abstract: The present invention discloses a registration method and system for a non-rigid multi-modal medical image. The registration method comprises: obtaining local descriptors of a reference image according to Zernike moments of order 0 and repetition 0 and Zernike moments of order 1 and repetition 1 of the reference image; obtaining local descriptors of a floating image according to Zernike moments of order 0 and repetition 0 and Zernike moments of order 1 and repetition 1 of the floating image; and finally obtaining a registration image according to the local descriptors of the reference image and the floating image. In the present, by using self-similarity of the multi-modal medical image and adopting the Zernike moment based local descriptor, the non-rigid multi-modal medical image registration is thus converted into the non-rigid mono-modal medical image registration, thereby greatly improving its accuracy and robustness.

Abstract: The present invention discloses a registration method and system for a non-rigid multi-modal medical image. The registration method comprises: obtaining local descriptors of a reference image according to Zernike moments of order 0 and repetition 0 and Zernike moments of order 1 and repetition 1 of the reference image; obtaining local descriptors of a floating image according to Zernike moments of order 0 and repetition 0 and Zernike moments of order 1 and repetition 1 of the floating image; and finally obtaining a registration image according to the local descriptors of the reference image and the floating image. In the present, by using self-similarity of the multi-modal medical image and adopting the Zernike moment based local descriptor, the non-rigid multi-modal medical image registration is thus converted into the non-rigid mono-modal medical image registration, thereby greatly improving its accuracy and robustness.