PURPOSE: To prospectively evaluate dose reduction and image quality characteristics of abdominal computed tomographic (CT) scans reconstructed with model-based iterative reconstruction (MBIR) compared with adaptive statistical iterative reconstruction (ASIR) in oncology patients with colorectal liver metastases. MATERIALS AND METHODS: The study complied with HIPAA guidelines and was approved by the ethics committee of the institutional review board. All patients gave written informed consent. Fifty-one patients with colorectal liver metastases underwent body CT (thorax and abdomen) with a 64-section multidetector unit. With a radiation dose reduction by 2.36 mGy compared to standard of care CT with ASIR 50% (radiation dose, 7.54 mGy), MBIR can provide diagnostically acceptable CT scans without compromising image quality. Two radiologists independently assessed randomized images in a blinded manner. Imaging sets were compared for lesion detection, lesion conspicuity, overall image quality, and signal-to-noise ratio with a paired sample t test. Inter- and intraobserver agreement was assessed with the Cohen κ. RESULTS: The mean volume CT dose index was 5.18 mGy ± 0.76, mean dose-length product 374 mGy · cm ± 63.47, mean effective diameter 29.38 cm ± 3.46, and mean size-specific dose estimate 6.52 mGy ± 0.73. In small liver lesions (<10 mm), detection and conspicuity were significantly higher with MBIR than with ASIR for both right (t = 3.245, P = .004 and t = 2.696, P = .013, respectively) and left (t = 2.390, P = .038 and t = 2.283, P = .046) liver lobes. Subjective image noise (t = 4.506, P < .001), artifacts (t = 3.479, P = .001), and diagnostic confidence (t = 2.643, P = .011) were significantly better with MBIR than with ASIR. CONCLUSION: MBIR performed better than ASIR 50% at providing diagnostically acceptable CT scans without compromising image quality and in the detection of colorectal liver metastases. RSNA, 2013
PURPOSE: To prospectively evaluate dose reduction and image quality characteristics of abdominal computed tomographic (CT) scans reconstructed with model-based iterative reconstruction (MBIR) compared with adaptive statistical iterative reconstruction (ASIR) in oncology patients with colorectal liver metastases. MATERIALS AND METHODS: The study complied with HIPAA guidelines and was approved by the ethics committee of the institutional review board. All patients gave written informed consent. Fifty-one patients with colorectal liver metastases underwent body CT (thorax and abdomen) with a 64-section multidetector unit. With a radiation dose reduction by 2.36 mGy compared to standard of care CT with ASIR 50% (radiation dose, 7.54 mGy), MBIR can provide diagnostically acceptable CT scans without compromising image quality. Two radiologists independently assessed randomized images in a blinded manner. Imaging sets were compared for lesion detection, lesion conspicuity, overall image quality, and signal-to-noise ratio with a paired sample t test. Inter- and intraobserver agreement was assessed with the Cohen κ. RESULTS: The mean volume CT dose index was 5.18 mGy ± 0.76, mean dose-length product 374 mGy · cm ± 63.47, mean effective diameter 29.38 cm ± 3.46, and mean size-specific dose estimate 6.52 mGy ± 0.73. In small liver lesions (<10 mm), detection and conspicuity were significantly higher with MBIR than with ASIR for both right (t = 3.245, P = .004 and t = 2.696, P = .013, respectively) and left (t = 2.390, P = .038 and t = 2.283, P = .046) liver lobes. Subjective image noise (t = 4.506, P < .001), artifacts (t = 3.479, P = .001), and diagnostic confidence (t = 2.643, P = .011) were significantly better with MBIR than with ASIR. CONCLUSION: MBIR performed better than ASIR 50% at providing diagnostically acceptable CT scans without compromising image quality and in the detection of colorectal liver metastases. RSNA, 2013
Authors: B Dustin Pooler; Meghan G Lubner; David H Kim; Eva M Ryckman; Sri Sivalingam; Jie Tang; Stephen Y Nakada; Guang-Hong Chen; Perry J Pickhardt Journal: J Urol Date: 2014-05-21 Impact factor: 7.450
Authors: Corey T Jensen; Morgan E Telesmanich; Nicolaus A Wagner-Bartak; Xinming Liu; John Rong; Janio Szklaruk; Aliya Qayyum; Wei Wei; Adam G Chandler; Eric P Tamm Journal: J Comput Assist Tomogr Date: 2017-01 Impact factor: 1.826