Purpose:To develop low-dose thin-section computed tomographic (CT) protocols for assessment of cystic fi brosis (CF) in pediatric patients and determine the clinical usefulness thereof compared with chest radiography. Materials and Methods:After institutional review board approval and informed consent from patients or guardians were obtained, 14 patients with CF and 11 patients without CF (16 male, nine female; mean age, 12.6 years 6 5.4 [standard deviation]; range, 3.5-25 years) who underwent imaging for clinical reasons underwent low-dose thin-section CT. Sections 1 mm thick (protocol A) were used in 10 patients, and sections 0.5 mm thick (protocol B) were used in 15 patients at six levels at 120 kVp and 30-50 mA. Image quality and diagnostic acceptability were scored qualitatively and quantitatively by two radiologists who also quantifi ed disease severity at thin-section CT and chest radiography. Effective doses were calculated by using a CT dosimetry calculator. Results:Low-dose thin-section CT was performed with mean effective doses of 0.19 mSv 6 0.03 for protocol A and 0.14 mSv 6 0.04 for protocol B ( P , .005). Diagnostic acceptability and depiction of bronchovascular structures at lung window settings were graded as almost excellent for both protocols, but protocol B was inferior to protocol A for mediastinal assessment ( P , .02). Patients with CF had moderate lung disease with a mean Bhalla score of 9.2 6 5.3 (range, 0-19), compared with that of patients without CF (1.1 6 1.4; P , .001). There was excellent correlation between thin-section CT and chest radiography ( r = 0.88-0.92; P , .001). Conclusion:Low-dose thin-section CT can be performed at lower effective doses than can standard CT, approaching those of chest radiography. Low-dose thin-section CT could be appropriate for evaluating bronchiectasis in pediatric patients, yielding appropriate information about lung parenchyma and bronchovascular structures.

BackgroundThe size-specific dose estimate (SSDE) is a dose-related metrics that incorporates patient size into its calculation. It is usually derived from the volume computed tomography dose index (CTDIvol) by applying a conversion factor determined from manually measured anteroposterior and lateral skin-to-skin patient diameters at the midslice level on computed tomography (CT) localiser images, an awkward, time-consuming, and not highly reproducible technique. The objective of this study was to evaluate the potential for the use of body mass index (BMI) as a size-related metrics alternative to the midslice effective diameter (DE) to obtain a size-specific dose (SSDE) in abdominal CT.MethodsIn this retrospective study of patients who underwent abdominal CT for the investigation of inflammatory bowel disease, the DE was measured on the midslice level on CT-localiser images of each patient. This was correlated with patient BMI and the linear regression equation relating the quantities was calculated. The ratio between the internal and the external abdominal diameters (DRATIO) was also measured to assess correlation with radiation dose. Pearson correlation analysis and linear regression models were used.ResultsThere was good correlation between DE and patient BMI (r = 0.88). An equation allowing calculation of DE from BMI was calculated by linear regression analysis as follows: DE = 0.76 (BMI) + 9.4. A weak correlation between radiation dose and DRATIO was demonstrated (r = 0.45).ConclusionsPatient BMI can be used to accurately estimate DE, obviating the need to measure anteroposterior and lateral diameters in order to calculate a SSDE for abdominal CT.

Background Computed tomography (CT) helps physicians locate and diagnose pathological conditions. In some conditions, having an airway segmentation method which facilitates reconstruction of the airway from chest CT images can help hugely in the assessment of lung diseases. Many efforts have been made to develop airway segmentation algorithms, but methods are usually not optimized to be reliable across different CT scan parameters.MethodsIn this paper, we present a simple and reliable semi-automatic algorithm which can segment tracheal and bronchial anatomy using the open-source 3D Slicer platform. The method is based on a region growing approach where trachea, right and left bronchi are cropped and segmented independently using three different thresholds. The algorithm and its parameters have been optimized to be efficient across different CT scan acquisition parameters. The performance of the proposed method has been evaluated on EXACT’09 cases and local clinical cases as well as on a breathing pig lung phantom using multiple scans and changing parameters. In particular, to investigate multiple scan parameters reconstruction kernel, radiation dose and slice thickness have been considered. Volume, branch count, branch length and leakage presence have been evaluated. A new method for leakage evaluation has been developed and correlation between segmentation metrics and CT acquisition parameters has been considered.ResultsAll the considered cases have been segmented successfully with good results in terms of leakage presence. Results on clinical data are comparable to other teams’ methods, as obtained by evaluation against the EXACT09 challenge, whereas results obtained from the phantom prove the reliability of the method across multiple CT platforms and acquisition parameters. As expected, slice thickness is the parameter affecting the results the most, whereas reconstruction kernel and radiation dose seem not to particularly affect airway segmentation.ConclusionThe system represents the first open-source airway segmentation platform. The quantitative evaluation approach presented represents the first repeatable system evaluation tool for like-for-like comparison between different airway segmentation platforms. Results suggest that the algorithm can be considered stable across multiple CT platforms and acquisition parameters and can be considered as a starting point for the development of a complete airway segmentation algorithm.

Low-dose CT enterography with MBIR yields images that are comparable to or superior to conventional images.

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