• Ranish Deedar Ali Khawaja, Sarabjeet Singh, Diego Lira, Rolf Bippus, Synho Do, Atul Padole, Sarvenaz Pourjabbar, Thomas Koehler, Jo-Anne Shepard, and Mannudeep K Kalra.
• From the *MGH Imaging, Division of Thoracic Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA; and †Philips Technologie GmbH, Innovative Technologies, Hamburg, Germany.
• J Comput Assist Tomogr. 2014 Sep 1; 38 (5): 760-7.

PurposeThe purpose of this study was to assess pulmonary lesion detection, diagnostic confidence, and noise reduction in sparse-sampled (SpS) computed tomographic (CT) data of submillisievert (SubmSv) chest CT reconstructed with iterative reconstruction technique (IRT).Materials And MethodsThis Human Insurance Portability and Accountability-compliant, institutional review board-approved prospective study was performed using SpS-SubmSv IRT chest CT in 10 non-obese patients (body-mass index, 21-35 kg/m; age range, 26-90 years). Written informed consent was obtained. The patients were scanned at standard-dose CT (mean [SD] volumetric CT dose index, 6 [0.9] mGy; mean [SD] dose-length product, 208 ± 44 mGy·cm; and mean [SD] effective dose, 3 [0.6] mSv) and at SubmSv dose (1.8 [0.2] mGy, 67 [2] mGy·cm, 0.9 [0.03] mSv, respectively) on a Philips 128-slice CT scanner with double z-sampling. Sparse angular sampling data were reconstructed using 25% of the angular projections from the SubmSv sinogram to reduce the number of views and radiation dose by approximately 4-fold. Hence, the patients were scanned and then, simulation-based sparse sampling was performed with a resultant dose hypothetical SpS scan estimated mathematically (0.2 mSv). From each patient data, 3 digital imaging and communications in medicine series were generated: SpS-SubmSv with IRT, fully sampled SubmSv filtered back projection (FBP), and fully sampled standard-dose FBP (SD-FBP). Two radiologists independently assessed these image series for detection of lung lesions, visibility of small structures, and diagnostic acceptability. Objective noise was measured in the thoracic aorta, and noise spectral density was obtained for SpS-SubmSv IRT, SubmSv-FBP, and SD-FBP.ResultsThe SpS-SubmSv IRT resulted in 75% (0.2/0.9 mSv) and 92% (0.2/2.9 mSv) dose reduction, when compared with the fully sampled SubmSv-FBP and SD-FBP, respectively. Images of SpS-SubmSv displayed all 46 lesions (most <1 cm, 30 lung nodules, 7 ground glass opacities, 9 emphysema) seen on the SubmSv-FBP and SD-FBP data sets. Lesion margins with sparse-sampled data were deemed acceptable compared with both SubmSv-FBP and SD-FBP. Overall diagnostic confidence was maintained with SpS-SubmSv IRT despite the presence of minor pixilation artifacts in 3 of 10 cases. The SpS-SubmSv IRT showed 63% and 38% noise reduction when compared with SubmSv-FBP (P < 0.0001) and SD-FBP (P < 0.01), respectively, with no significant change in Hounsfield unit values (P > 0.05). Noise-spectral density showed that SpS-SubmSv IRT gives a linear decrease over frequency in the semilog plot and an exponential decrease of noise power over frequency compared with SubmSv-FBP and SD-FBP.ConclusionsMore than 90% dose reduction could be achieved with one-fourth sparse-sampled and SubmSv chest CT examination when reconstructed with IRT. Chest CT dose at one fourth of a millisievert with SpS is possible with optimal lesion detection and diagnostic confidence for the evaluation of pulmonary findings.

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