A maximum intensity projection (MIP) is a computer visualization method for 3D data that projects in the visualization plane the voxels with maximum intensity that fall in the way of parallel rays traced from the viewpoint to the plane of projection. This implies that two MIP renderings from opposite viewpoints are symmetrical images.
This technique is computationally fast, but the 2D results do not provide a good sense of depth of the original data. To improve the sense of 3D, animations are usually rendered of several MIP frames in which the viewpoint is slightly changed from one to the other, thus creating the illusion of rotation. This helps the viewer's perception to find the relative 3D positions of the object components. However, since the projection is orthographic the viewer cannot distinguish between left or right, front or back and even if the object is rotating clockwise or anti-clockwise.
MIP is used for the detection of lung nodules in lung cancer screening programs which utilise computed tomography scans. MIP enhances the 3D nature of these nodules, making them stand out from pulmonary bronchi and vasculature.
MIP imaging was invented for use in Nuclear Medicine by Jerold Wallis, MD, in 1988, and subsequently published in IEEE Transactions in Medical Imaging [1]. In the setting of Nuclear Medicine, it was originally called MAP (Maximum Activity Projection). Additional information can be found in other articles by the same author [2], [3].
Use of depth weighting during production of rotating cines of MIP images can avoid the problem of difficulty of distinguishing right from left, and clockwise vs anti-clockwise rotation. MIP imaging is used routinely by physicians in interpreting Positron Emission Tomography (PET) or Magnetic Resonance Angiography studies.
This technique is computationally fast, but the 2D results do not provide a good sense of depth of the original data. To improve the sense of 3D, animations are usually rendered of several MIP frames in which the viewpoint is slightly changed from one to the other, thus creating the illusion of rotation. This helps the viewer's perception to find the relative 3D positions of the object components. However, since the projection is orthographic the viewer cannot distinguish between left or right, front or back and even if the object is rotating clockwise or anti-clockwise.
MIP is used for the detection of lung nodules in lung cancer screening programs which utilise computed tomography scans. MIP enhances the 3D nature of these nodules, making them stand out from pulmonary bronchi and vasculature.
MIP imaging was invented for use in Nuclear Medicine by Jerold Wallis, MD, in 1988, and subsequently published in IEEE Transactions in Medical Imaging [1]. In the setting of Nuclear Medicine, it was originally called MAP (Maximum Activity Projection). Additional information can be found in other articles by the same author [2], [3].
Use of depth weighting during production of rotating cines of MIP images can avoid the problem of difficulty of distinguishing right from left, and clockwise vs anti-clockwise rotation. MIP imaging is used routinely by physicians in interpreting Positron Emission Tomography (PET) or Magnetic Resonance Angiography studies.
Maximum intensity projection (MIP) is a volume rendering technique which is used to extract high-intensity structures from volumetric scalar data. At each pixel the highest data value encountered along the corresponding viewing ray is determined. MIP is commonly used to extract vascular structures from medical MRI data sets, i.e., angiography. The usual way to compensate for the loss of spatial and occlusion information in MIP images is to view the data from different view points by rotating them. As the generation of a MIP is usually non-interactive, this is done by calculating multiple images offline and playing them back as an animation. In this paper a novel algorithm is proposed which is capable of interactively generating Maximum Intensity Projection images even on low-end hardware using parallel projection. Two methods for preprocessing data and removing voxels which will due to their neighborhood never contribute to a MIP are discussed. The remaining voxels are stored in a way which guarantees optimal cache coherency regardless of the viewing direction. For use on low-end hardware, a preview-mode is included which renders only the more significant parts of the volume during user interaction. Furthermore, we demonstrate the usability of our data structure for extensions of the MIP technique like MIP with depth-shading and local maximum intensity projection (LMIP).
The maximum intensity projection (MIP) is a popularly used algorithm for display of MRA images, but its performance has not been rigorously analyzed before. In this paper, four measures are proposed for the performance of the MIP algorithm and the quality of images projected from three-dimensional (3-D) data, which are vessel voxel projection probability, vessel detection probability, false vessel probability, and vessel-tissue contrast-to-noise ratio (CNR). As side products, vessel-missing probability, vessel receiver operating characteristics (ROC's), and mean number of false vessels are also studied. Based on the assumptions that the intensities of vessel, tissue, and noise along a projection path are independent Gaussian, these measures are derived and obtained all in closed forms. All the measures are functions of explicit parameters: vessel-to-tissue noise ratio (VTNR) and CNR of 3 D data prior to the MIP, vessel diameter, and projection length. It is shown that the MIP algorithm increases the CNR of large vessels whose CNR prior to the MIP is high and whose diameters are large. The increase in CNR increases with projection path length. On the other hand, all the proposed measures indicate that the small vessels that have low CNR prior to the MIP and small diameters suffer from the MIP. The performance gets worse as projection path length increases. All measures demonstrate a better performance when the vessel diameter is larger. Other properties and possible applications of the derived measures are also discussed.
