A radiologist may frequently need to search for, for example, a nodule (e.g., a lung nodule) using computed tomography (CT) scan images. The size of a nodule may be substantially smaller than the sizes of other structures of the object (e.g., a lung) scanned. For example, FIG. 1A illustrates an exemplary CT scan image 100 representing a dataset of a lung slice. A CT scan image represents an axial or cross-sectional image of a 3D object, such as a lung. Axial or cross-sectional images represent datasets associated with virtual slices of the 3D object. As shown in FIG. 1A, in CT scan image 100, a representation 102 of a lung nodule may appear as a small white dot or circle. However, in CT image 100, the representations of other structures of a lung may also appear as small white dots or circles. But the other structures may not be lung nodules. Rather, they may be branching airways, arteries of veins, etc. As a result, a radiologist may have difficulty visually detect the representation (e.g., representation 102) of a lung nodule using a single CT scan image (e.g., CT scan image 100).
This difficulty may be mitigated or reduced by, for example, generating a CT scan image representing a dataset associated with a thicker slice of the lung or manually navigating through multiple CT scan images. For example, FIG. 1B illustrates an exemplary CT scan image 220 representing a dataset of a lung slice having a thickness that is greater than that of the lung slice shown in FIG. 1A. With a thicker slice, non-nodule structures, such as branching airways, arteries, and veins, may appear to be longer than a dot or circle because they typically extend through the thicker slice at an angle. A nodule, on the other hand, would remain as a dot or circle in a thicker slice. As a result, a radiologist can potentially distinguish between a lung nodule and other lung structures. The thicker slice approach, however, may not be readily available or may not be efficient in visual detection of a nodule. For example, the thickness would need to be determined or optimized for better distinguishing the nodules from non-nodule structures. This may involve a cumbersome and time consuming process and may not be desired.
Moreover, a radiologist may attempt to visually detect a nodule using multiple images representing datasets of regular or thin slices of a lung. For doing that, the radiologist may need to manually navigating through multiple CT scan images. Such navigation may require, for example, scrolling the track wheel of a computer mouse up and down for a large number of times. In addition, during the scrolling, the radiologist may easily lose track of the image on which he or she is attempting to visually detect the nodule. Therefore, the approach of manual navigation through multiple CT scan images can also be cumbersome and inefficient.