Abstract
PurposeCone‐beam computerized tomography (CBCT) is routinely performed for verification of patient position in radiotherapy. It produced a large amount of data which require a method to compress them for efficient storage. In this study three video compression algorithms were introduced and their performance was evaluated based on real patient data.Materials and methodsAt first CBCT images in multiple sets of a patient were transferred from reconstruction workstation or exported from treatment planning system. Then CBCT images were sorted according to imaging time (time‐prioritized sequence) or imaging location (location‐prioritized sequence). Next, this sequence was processed by a video compression algorithm and resulted in a movie. Three representative video compression algorithms (Motion JPEG 2000, Motion JPEG AVI, and MPEG‐4) were employed and their compression performance was evaluated based on the CBCT data of 30 patients.ResultsAmong three video compression algorithms, Motion JPEG 2000 has the least compression ratio since it is a lossless compression algorithm. Motion JPEG AVI and MPEG‐4 have higher compression ratios than Motion JPEG 2000 but come with certain image losses. For MPEG‐4, location‐prioritized sequences show higher compression ratio than time‐prioritized sequences. Based on the results achieved on the clinical target verification application, the registration accuracy of CBCT after decompression was comparable to that of the original CBCT.ConclusionsVideo compression algorithms could provide a higher compression ratio comparing to static image compression algorithm. Although the loss of CBCT image due to compression its impact on registration accuracy of patient positioning is almost negligible. Video compression method is an effective way to substantially reduce the size of CBCT images for storage.
Highlights
IntroductionMedical imaging plays a key role in modern medicine since they offer comprehensive information for diagnosis, treatment, and follow‐up
Besides the demand of storage, there are many situations that the amount of data must be reduced such as low‐speed network connection, low‐resolution presentations, and printings.[1,2]
The lossless compression for medical images adopted by many organizations and standards in medicine, such as the Digital Imaging and Communications in Medicine (DICOM) group.[5–8]
Summary
Medical imaging plays a key role in modern medicine since they offer comprehensive information for diagnosis, treatment, and follow‐up. The amount of data generated by the imaging procedure is exploding and causes higher cost to store them. Image compression will reduce the file size on the storage device while maintaining relevant clinical information. Image compression algorithm takes advantages of redundancy that occur spatially, temporally, and spectrally. It can be categorized in lossy and lossless techniques.[3]. Lossless techniques are reversible and compression rates are low. Lossy techniques are irreversible and compression rates are much higher. Because of the regulatory policies set by agencies, there is few clinical research on the use of lossy compression for medical images.[4]. The lossless compression for medical images adopted by many organizations and standards in medicine, such as the Digital Imaging and Communications in Medicine (DICOM) group.[5–8]
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