Abstract
Computed tomography（CT）is widely used as the first cross-sectional imaging modality and plays an important role in the management of patients with head and neck cancer. Recent advanced CT imaging techniques can generate additional CT reconstructions and there is increasing evidence to improve the diagnostic evaluation. First, dual-energy CT allows material decomposition so that iodine can be differentiated from soft tissue, and can potentially provide additional further “contrast resolution” to the standard contrast-enhanced CT images. The addition of iodine overlay maps using dual-energy CT scanning to the conventional CT images has increased the specificity for detection of cartilage invasion without compromising sensitivity by laryngeal and hypopharyngeal cancer. Second, bone subtraction iodine（BSI）imaging using 320-row area detector CT scanning is useful for detecting and accurately assessing the extent of bone invasion such as skull base/mandible by head and neck cancer. This technique reduces spatial mismatch using volume scanning with wide-area detector CT and a high-resolution deformable registration algorithm, enabling identification of contrast enhancement in the bone marrow by subtracting the unenhanced CT from the contrast-enhanced CT. Third, additional metal artifact reduction postprocessing, such as single-energy metal artifact reduction（SEMAR）, has been applied to improve overall image quality and increase diagnostic confidence in the assessment of soft tissues near and far from metallic implants. The SEMAR algorithm significantly improves both the objective and subjective image quality, thereby increasing the rate of detection of oral cavity tumors.
As a next step, texture analysis/radiomics using CT imaging is now entering the area of personalized medicine with the development of new techniques to predict prognosis, response to treatment, and outcomes from images and other associated data. These developments will be applied to clinical applications and decision support systems using deep learning algorithms in the near future.
