Progression parameters for emphysema: A clinical investigation

Stolk, Jan; Putter, Hein; Bakker, Els; Shaker, Saher Burhan; Parr, David; Piitulainen, Eeva; Russi, Erich W.; Grebski, Elzbieta; Dirksen, Asger; Stockley, Robert A.; Reiber, Johan H. C.; Stoel, Berend C.

doi:10.1016/j.rmed.2007.04.016

Cited by 104 publications

(95 citation statements)

References 24 publications

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“…(7)(8)(9)(10)(11)(12)(13)(14)(15) The method has been recommended for use in longitudinal studies of emphysema and is currently considered to be better than functional tests for disease assessment. (6,16) In addition, previous studies have reported that the correlation between CT densitometry and macroscopic morphometry is higher than is that between macroscopic morphometry and subjective visual grading of emphysema. (14) Emphysema has a long and silent asymptomatic evolution, manifesting clinically only at an advanced stage.…”

Section: Discussionmentioning

confidence: 99%

Índice de enfisema pulmonar em coorte de pacientes sem doença pulmonar conhecida: influência da idade

et al. 2012

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Objective: To investigate the effects of age on pulmonary emphysema, based on the values of the emphysema index (EI) in a cohort of patients who had never smoked and who had no recognizable lung disease. Methods: We reviewed the CT scans, reported as normal, of 315 patients. Exclusion criteria were a history of smoking, cardiorespiratory disease, and exposure to drugs that could cause lung disease. From this cohort, we selected 32 patients (16 men and 16 women), matched for gender and body mass index, who were divided equally into two groups by age (< 50 years and ≥ 50 years). We quantified emphysema using a computer program specific to that task. The EI was calculated with a threshold of −950 HU. We also evaluated total lung volume (TLV) and mean lung density (MLD). Results: The overall means for TLV, MLD, and EI were 5,027 mL, −827 HU, and 2.54%, respectively. Mean values in the older and younger groups, respectively, were as follows: for TLV, 5,229 mL vs. 4,824 mL (p > 0.05); for MLD, −846 HU vs. −813 HU (p < 0.04); and for EI, 3.30% vs. 1.28% (p < 0.001). Significant correlations were found between EI and age (r = 0.66; p = 0.001), EI and TLV (r = 0.58; p = 0.001), and EI and MLD (r = −0.67; p < 0.001). The predicted EI per age was defined by the regression equation (r 2 = 0.43): p50(EI) = 0.049 × age − 0.5353. Conclusions: It is important to consider the influence of age when quantifying emphysema in patients over 50 years of age. Based on the regression analysis, EI values of 2.6%, 3.5%, and 4.5% can be considered normal for patients 30, 50, and 70 years of age, respectively. Keywords: Pulmonary emphysema; Tomography, spiral computed; Aging; Pulmonary disease, chronic obstructive. ResumoObjetivo: Investigar os efeitos da idade no enfisema pulmonar, com base nos valores do índice de enfisema (IE) em uma coorte de pacientes que nunca fumou e que não possuía doença pulmonar conhecida. Métodos: Foram revisados exames de TC, considerados normais, de 315 pacientes. Tabagismo, doenças cardiorrespiratórias e exposição a drogas que poderiam causar doença pulmonar foram critérios de exclusão. Dessa coorte, selecionamos 32 pacientes (16 homens e 16 mulheres), igualmente divididos em dois grupos (idade < 50 anos e idade ≥ 50 anos), que foram pareados por gênero e índice de massa corpórea. Realizou-se a quantificação do enfisema utilizando um programa específico. O IE foi calculado com um limiar de −950 UH. O volume pulmonar total (VPT) e a densidade pulmonar média (DPM) também foram avaliados. Resultados: As médias gerais de VPT, DPM e IE foram 5.027 mL, −827 UH e 2,54%, respectivamente. A comparação entre os mais velhos e os mais novos mostrou as seguintes médias: VPT (5.229 mL vs. 4.824 mL; p > 0,05); DPM (−846 UH vs. −813 UH; p < 0,04) e IE (3,30% vs. 1,28%; p < 0,001). Houve correlações significativas entre IE e idade (r = 0,66; p = 0,001), IE e VPT (r = 0,58; p = 0,001) e IE e DPM (r = −0,67; p < 0,001). O IE previsto por idade foi definido através da equação de regressão (r 2 = 0,43): p50(IE) = 0,049 × i...

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Section: Discussionmentioning

confidence: 99%

Índice de enfisema pulmonar em coorte de pacientes sem doença pulmonar conhecida: influência da idade

et al. 2012

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“…Materials and ground truth: In this study, the SPREAD database [10] has been used, which has 21 pairs of 3D lung CT images. The dimension of the images is about 446 × 315 × 129 with an average voxel size of 0.781 × 0.781 × 2.5 mm.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…3(a). For the registrations, we used three resolutions of 500 iterations each, with a final B-spline grid spacing of [10,10,10] mm. The cost function is mutual information, which is optimized by adaptive stochastic gradient descent [12].…”

Section: Experiments and Resultsmentioning

confidence: 99%

Accuracy Estimation for Medical Image Registration Using Regression Forests

Sokooti

Saygılı

Glocker

et al. 2016

Lecture Notes in Computer Science

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Abstract. This paper reports a new automatic algorithm to estimate the misregistration in a quantitative manner. A random regression forest is constructed, predicting the local registration error. The forest is built using local and modality independent features related to the registration precision, the transformation model and intensity-based similarity after registration. The forest is trained and tested using manually annotated corresponding points between pairs of chest CT scans. The results show that the mean absolute error of regression is 0.72 ± 0.96 mm and the accuracy of classification in three classes (correct, poor and wrong registration) is 93.4%, comparing favorably to a competing method. In conclusion, a method was proposed that for the first time shows the feasibility of automatic registration assessment by means of regression, and promising results were obtained.

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“…The first two levels of the BSpline transform were applied to smoothed and sub-sampled versions of the images whereas the finest level was applied to the original images. The image registration framework was implemented with ITK [11]. Fig.1.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Weight Preserving Image Registration for Monitoring Disease Progression in Lung CT

Gorbunova

Ashraf

et al. 2008

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2008

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Abstract.We present a new image registration based method for monitoring regional disease progression in longitudinal image studies of lung disease. A free-form image registration technique is used to match a baseline 3D CT lung scan onto a following scan. Areas with lower intensity in the following scan compared with intensities in the deformed baseline image indicate local loss of lung tissue that is associated with progression of emphysema. To account for differences in lung intensity owing to differences in the inspiration level in the two scans rather than disease progression, we propose to adjust the density of lung tissue with respect to local expansion or compression such that the total weight of the lungs is preserved during deformation. Our method provides a good estimation of regional destruction of lung tissue for subjects with a significant difference in inspiration level between CT scans and may result in a more sensitive measure of disease progression than standard quantitative CT measures.

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Progression parameters for emphysema: A clinical investigation

Cited by 104 publications

References 24 publications

Índice de enfisema pulmonar em coorte de pacientes sem doença pulmonar conhecida: influência da idade

Índice de enfisema pulmonar em coorte de pacientes sem doença pulmonar conhecida: influência da idade

Accuracy Estimation for Medical Image Registration Using Regression Forests

Weight Preserving Image Registration for Monitoring Disease Progression in Lung CT

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