Radiation dose reduction in soft tissue neck CT using adaptive statistical iterative reconstruction (ASIR)
Introduction
Significant improvements in image quality, acquisition speed and patient throughput have led to a dramatic increase in the use of CT as an essential diagnostic tool for multiple clinical applications. As a result, there is growing concern regarding the accompanying radiation exposure and potential radiation-induced malignancies [1], [2], [3], [4].
The “linear no-threshold” model accepted by many authors for stochastic effects posits a direct dose–response relationship between the development of solid cancers and exposure even to low doses of radiation [1]. Multiple dose-reducing strategies (X-ray beam filtration and collimation, manual tube current modulation tailored to patient size and indication, peak kilovoltage optimization, improved detector efficiency and noise reduction algorithms) are now included in newer CT scanners in an effort to reduce radiation doses and hence the potential for radiation-induced malignancies.
A technique to lower radiation dose, the reduction of tube current, is associated with unacceptable increases in image noise when the current standard reconstruction method of filtered back projection (FBP) is used to create the CT images. Iterative reconstruction (IR), routinely used for positron emission tomography (PET) and single photon emission computed tomography (SPECT), has been recently re-introduced to CT as an alternative mathematic algorithm that results in lower image noise than FBP [5], [6], [7]. The need for expensive computational hardware and increased reconstruction time prevented the implementation of IR for clinical use until recently. Adaptive Statistical Iterative Reconstruction (ASIR; GE Healthcare, Milwaukee, WI) is a recently developed IR algorithm approved by the US Food and Drug Administration for clinical use. ASIR reduces reconstruction time by using information obtained from the FBP algorithm as a starting point for image reconstruction and then repeatedly compares the estimated pixel value to the ideal value predicted by the noise model, until the estimated and ideal values converge [6].
Clinical studies supporting the use of iterative reconstruction methods such as ASIR in reducing radiation dose in head [8], [9], temporal bone [10], cardiac [11], [12], [13], [14], [15], chest [16], [17], [18] and abdomen [19], [20] have been reported. To our knowledge, the role of ASIR in reducing radiation dose in soft-tissue neck CT has not been reported. The present study compares objective and subjective image quality in soft-tissue neck CT images acquired at different tube current–time products (275 mA s and 340 mA s) and reconstructed with FBP and ASIR.
Section snippets
Patient
Our prospective clinical study was compliant with the Health Insurance Portability and Accountability Act (HIPAA) guidelines and was approved by the Human Research Committee of our Institutional Review Board (IRB). Informed written consent was waived because all studies were clinically indicated and performed as standard-of-care with a standard or lower-dose radiation protocol, and because early experience in our department showed that there was preservation of diagnostic image quality at all
Results
The characteristics of subjects within the distinct study groups are shown in Table 1. There was no significant difference in BMI between the patients scanned with the standard dose (340 mA s) and the lower-dose (275 mA s) protocols, p = 0.07.
Discussion
Neck CT is the imaging modality of choice to evaluate neck pathology. A neck CT exam extends from the posterior fossa to the aortic arch, irradiating radiosensitive organs such as the eyes and the thyroid, which have higher stochastic risk for injury and future malignancy, particularly with cumulative radiation exposure [3]. Several studies have already confirmed the capability of ASIR to achieve robust image quality in low dose CT examinations of the head, temporal bone, thorax and abdomen [8]
Conflicts of interest
None of the authors have any conflicts of interest.
Acknowledgement
The authors thank Dr. J.A. Parker for statistical guidance.
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