The reconstruction algorithms including at least one iterative step can reduce the 4 CBCT specific artifacts. Nevertheless, the algorithms that use the full data set, at least for initialization, such as MKB and PICCS in the authors' implementation, are only a trade-off and may not fully achieve the optimal temporal resolution. A predictable image quality as seen in conventional reconstruction methods, i.e., without total variation minimization, is a desirable property for reconstruction algorithms. Fast, iterative approaches such as the MKB can therefore be seen as a suitable tradeoff.
In circular cone-beam CT the Feldkamp [Feldkamp-Davis-Kress (FDK)] algorithm is the most prominent image reconstruction algorithm. For example, in radiation oncology images reconstructed with the Feldkamp algorithm are used for accurate patient positioning. The scan and reconstruction volumes are limited by the size of the flat panel detector. Flat panel detectors, however, are expensive and difficult to manufacture in large size. For numerous treatment techniques, extending this scan volume would be very beneficial. In most applications, data from 360 degrees or more are available. However, usually only those slices are reconstructed where each pixel is seen under the full 360 degree range. Yet for a 360 degree scan there are regions that are seen by less than 360 degrees, namely, those that lie further off the plane of the circular source trajectory. Performing a reconstruction also for those slices where all voxels are seen at least by 180 degrees will extend the z range and therefore increase the dose usage. In this work a new method is presented that reconstructs also those slices where some or all pixels receive less than 360 degrees but at least 180 degrees of the data. The procedure significantly increases the longitudinal range of the reconstructed volume. As opposed to the existing techniques, the proposed method does not necessitate any multiple convolutions or multiple backprojections, lending itself therefore for a very efficient implementation. To validate the abilities of the extended reconstruction, the authors performed an evaluation of the image quality by using simulated and measured CT data. The method shows good image quality on simulated phantom data as well as on clinical patient scans. Image noise and spatial resolution behave as expected. This means that the noise equals FDK values in the normal region and increases in the extended region due to reduced data redundancies. The extended Feldkamp demonstrates its ability to extend the reconstructable z range and appears to be useful in clinical practice.
To delineate image data curation needs and describe a locally designed graphical user interface (GUI) to aid radiologists in image annotation for artificial intelligence (AI) applications in medical imaging.
Materials and Methods:GUI components support image analysis toolboxes, picture archiving and communication system integration, third-party applications, processing of scripting languages, and integration of deep learning libraries. For clinical AI applications, GUI components included two-dimensional segmentation and classification; three-dimensional segmentation and quantification; and threedimensional segmentation, quantification, and classification. To assess radiologist engagement and performance efficiency associated with GUI-related capabilities, image annotation rate (studies per day) and speed (minutes per case) were evaluated in two clinical scenarios of varying complexity: hip fracture detection and coronary atherosclerotic plaque demarcation and stenosis grading.Results: For hip fracture, 1050 radiographs were annotated over 7 days (150 studies per day; median speed: 10 seconds per study [interquartile range, 3-21 seconds per study]). A total of 294 coronary CT angiographic studies with 1843 arteries and branches were annotated for atherosclerotic plaque over 23 days (15.2 studies [80.1 vessels] per day; median speed: 6.08 minutes per study [interquartile range, 2.8-10.6 minutes per study] and 73 seconds per vessel [interquartile range, 20.9-155 seconds per vessel]).
Conclusion:GUI-component compatibility with common image analysis tools facilitates radiologist engagement in image data curation, including image annotation, supporting AI application development and evolution for medical imaging. When complemented by other GUI elements, a continuous integrated workflow supporting formation of an agile deep neural network life cycle results.
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