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F Preteux

Publications and source records attributed to F Preteux.

4 recordsLinked to original sources
PubMed2006

Quantification of bronchial dimensions at MDCT using dedicated software.

This study aimed to assess the feasibility of quantification of bronchial dimensions at MDCT using dedicated software (BronCare). We evaluated the reliability of the software to segment the airways and defined criteria ensuring accurate measurements. BronCare was applied on two successive examinations in 10 mild asthmatic patients. Acquisitions were performed at pneumotachographically controlled lung volume (65% TLC), with reconstructions focused on the right lung base. Five validation criteria were imposed: (1) bronchus type: segmental and subsegmental; (2) lumen area (LA)>4 mm2; (3) bronchus length (Lg) > 7 mm; (4) confidence index - giving the percentage of the bronchus not abutted by a vessel - (CI) >55% for validation of wall area (WA) and (5) a minimum of 10 contiguous cross-sectional images fulfilling the criteria. A complete segmentation procedure on both acquisitions made possible an evaluation of LA and WA in 174/223 (78%) and 171/174 (98%) of bronchi, respectively. The validation criteria were met for 56/69 (81%) and for 16/69 (23%) of segmental bronchi and for 73/102 (72%) and 58/102 (57%) of subsegmental bronchi, for LA and WA, respectively. In conclusion, BronCare is reliable to segment the airways in clinical practice. The proposed criteria seem appropriate to select bronchi candidates for measurement.

Asthma↗
PubMed2000

Regional distribution of gas and tissue in acute respiratory distress syndrome. I. Consequences for lung morphology. CT Scan ARDS Study Group.

OBJECTIVE: To compare the computed tomographic (CT) analysis of the distribution of gas and tissue in the lungs of patients with ARDS with that in healthy volunteers. DESIGN: Prospective study over a 53-month period. SETTING: Fourteen-bed surgical intensive care unit of a university hospital. PATIENTS AND PARTICIPANTS: Seventy-one consecutive patients with early ARDS and 11 healthy volunteers. MEASUREMENTS AND RESULTS: A lung CT was performed at end-expiration in patients with ARDS (at zero PEEP) and healthy volunteers. In patients with ARDS, end-expiratory lung volume (gas + tissue) and functional residual capacity (FRC) were reduced by 17% and 58% respectively, and an excess lung tissue of 701+/-321 ml was observed. The loss of gas was more pronounced in the lower than in the upper lobes. The lower lobes of 27% of the patients were characterized by "compression atelectasis," defined as a massive loss of aeration with no concomitant excess in lung tissue, and "inflammatory atelectasis," defined as a massive loss of aeration associated with an excess lung tissue, was observed in 73% of the patients. Three groups of patients were differentiated according to the appearance of their CT: 23% had diffuse attenuations evenly distributed in the two lungs, 36% had lobar attenuations predominating in the lower lobes, and 41% had patchy attenuations unevenly distributed in the two lungs. The three groups were similar regarding excess lung tissue in the upper and lower lobes and reduction in FRC in the lower lobes. In contrast, the FRC of the upper lobes was markedly lower in patients with diffuse or patchy attenuations than in healthy volunteers or patients with lobar attenuations. CONCLUSIONS: These results demonstrate that striking differences in lung morphology, corresponding to different distributions of gas within the lungs, are observed in patients whose respiratory condition fulfills the definition criteria of ARDS.

Analysis of Variance↗
PubMed1986

Mathematical morphology for automatic detection of brain lesions at magnetic resonance imaging.

An original method of image analysis has been developed, using 'mathematical morphology', in order to detect brain tumors at magnetic resonance imaging of the head following injection of gadolinium-DPTA. The main steps of the analysis are as follows: 1) Spatial reduction by automatic determination of a region of interest, 2) elimination of noise by morphologic filtering (increasing alternate closing-opening sequence), 3) localization of tumor by searching for r-h maxima, and 4) extraction of tumor contours using three-dimensional structuring elements. The preliminary results of this automatic and robust method encourage further studies of the potential of tissue analysis as an aid to tumor diagnosis.

Brain↗