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Exemplary results of the <t>multi-texture</t> <t>lung</t> <t>analysis</t> tool and the opacity-based lung analysis tool. CT in coronal view of a 62 year old patient with idiopathic pulmonary fibrosis ( A ), analyzed by the opacity-based Pneumonia tool (Siemens Healthineers), the red lines represent the segmented fibrotic areas ( B ) with additional 3D reconstruction ( C ). ( D ) Analysis with the multi-texture lung analysis tool (CALIPER, Biomedical imaging resource laboratory, Mayo Clinic) with the yellow areas representing fibrotic altered tissue.
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<t>Lung</t> <t>texture</t> <t>analysis</t> report: The Dicom representative image of each category of pattern. These were chosen based on the predominance of lung parenchymal patterns including hyperlucent, ground glass [GG], reticular, and honeycombing. Imbio LTA provided their regional distribution within three different lung zones [upper, middle, lower] in each lung. The platform provided an image in which each lung pattern was colored differently, allowing the evaluation of disease extent, composition, and location at a glance. Specific images were chosen because of the predominance of one of the patterns in each of them; at the same time, they had other patterns, also showing that all pathologies could coexist in a single case of bronchiectasis, yet have a predominance of a single pattern that may have a bearing on their clinical symptoms.
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Image Search Results


Exemplary results of the multi-texture lung analysis tool and the opacity-based lung analysis tool. CT in coronal view of a 62 year old patient with idiopathic pulmonary fibrosis ( A ), analyzed by the opacity-based Pneumonia tool (Siemens Healthineers), the red lines represent the segmented fibrotic areas ( B ) with additional 3D reconstruction ( C ). ( D ) Analysis with the multi-texture lung analysis tool (CALIPER, Biomedical imaging resource laboratory, Mayo Clinic) with the yellow areas representing fibrotic altered tissue.

Journal: Scientific Reports

Article Title: Comparing multi-texture fibrosis analysis versus binary opacity-based abnormality detection for quantitative assessment of idiopathic pulmonary fibrosis

doi: 10.1038/s41598-025-85135-7

Figure Lengend Snippet: Exemplary results of the multi-texture lung analysis tool and the opacity-based lung analysis tool. CT in coronal view of a 62 year old patient with idiopathic pulmonary fibrosis ( A ), analyzed by the opacity-based Pneumonia tool (Siemens Healthineers), the red lines represent the segmented fibrotic areas ( B ) with additional 3D reconstruction ( C ). ( D ) Analysis with the multi-texture lung analysis tool (CALIPER, Biomedical imaging resource laboratory, Mayo Clinic) with the yellow areas representing fibrotic altered tissue.

Article Snippet: The computer-aided lung informatics for pathology evaluation and ratings (CALIPER) tool is part of the Imbio lung texture analysis software (Imbio) and was created by the Biomedical Imaging Resource Laboratory of the Mayo Clinic, Minnesota.

Techniques: Imaging

Linear regression analyses. Illustration of regression analysis between the opacity-based fibrotic lung (OFL) and the multi-texture fibrotic lung (MTFL) quantification, as well as regression analysis of OFL and MTFL with spirometry parameters with Pearson correlation coefficient (r) bigger than 0.25. 95% confidence intervals are indicated by light-colored areas. TLC percent predicted total lung capacity, VC percent predicted vital capacity and DL CO diffusing capacity of the lungs for carbon monoxide.

Journal: Scientific Reports

Article Title: Comparing multi-texture fibrosis analysis versus binary opacity-based abnormality detection for quantitative assessment of idiopathic pulmonary fibrosis

doi: 10.1038/s41598-025-85135-7

Figure Lengend Snippet: Linear regression analyses. Illustration of regression analysis between the opacity-based fibrotic lung (OFL) and the multi-texture fibrotic lung (MTFL) quantification, as well as regression analysis of OFL and MTFL with spirometry parameters with Pearson correlation coefficient (r) bigger than 0.25. 95% confidence intervals are indicated by light-colored areas. TLC percent predicted total lung capacity, VC percent predicted vital capacity and DL CO diffusing capacity of the lungs for carbon monoxide.

Article Snippet: The computer-aided lung informatics for pathology evaluation and ratings (CALIPER) tool is part of the Imbio lung texture analysis software (Imbio) and was created by the Biomedical Imaging Resource Laboratory of the Mayo Clinic, Minnesota.

Techniques:

Bland-Altman analysis. The opacity-based fibrotic lung (OFL) quantifications was compared to the multi-texture fibrotic lung (MTFL) measurements.

Journal: Scientific Reports

Article Title: Comparing multi-texture fibrosis analysis versus binary opacity-based abnormality detection for quantitative assessment of idiopathic pulmonary fibrosis

doi: 10.1038/s41598-025-85135-7

Figure Lengend Snippet: Bland-Altman analysis. The opacity-based fibrotic lung (OFL) quantifications was compared to the multi-texture fibrotic lung (MTFL) measurements.

Article Snippet: The computer-aided lung informatics for pathology evaluation and ratings (CALIPER) tool is part of the Imbio lung texture analysis software (Imbio) and was created by the Biomedical Imaging Resource Laboratory of the Mayo Clinic, Minnesota.

Techniques:

Lung texture analysis report: The Dicom representative image of each category of pattern. These were chosen based on the predominance of lung parenchymal patterns including hyperlucent, ground glass [GG], reticular, and honeycombing. Imbio LTA provided their regional distribution within three different lung zones [upper, middle, lower] in each lung. The platform provided an image in which each lung pattern was colored differently, allowing the evaluation of disease extent, composition, and location at a glance. Specific images were chosen because of the predominance of one of the patterns in each of them; at the same time, they had other patterns, also showing that all pathologies could coexist in a single case of bronchiectasis, yet have a predominance of a single pattern that may have a bearing on their clinical symptoms.

Journal: Diagnostics

Article Title: Artificial Intelligence Unveils the Unseen: Mapping Novel Lung Patterns in Bronchiectasis via Texture Analysis

doi: 10.3390/diagnostics14242883

Figure Lengend Snippet: Lung texture analysis report: The Dicom representative image of each category of pattern. These were chosen based on the predominance of lung parenchymal patterns including hyperlucent, ground glass [GG], reticular, and honeycombing. Imbio LTA provided their regional distribution within three different lung zones [upper, middle, lower] in each lung. The platform provided an image in which each lung pattern was colored differently, allowing the evaluation of disease extent, composition, and location at a glance. Specific images were chosen because of the predominance of one of the patterns in each of them; at the same time, they had other patterns, also showing that all pathologies could coexist in a single case of bronchiectasis, yet have a predominance of a single pattern that may have a bearing on their clinical symptoms.

Article Snippet: This study aimed to assess lung involvement in patients with bronchiectasis using the Bronchiectasis Radiologically Indexed CT Score (BRICS) and AI-based quantitative lung texture analysis software (IMBIO, Version 2.2.0).

Techniques: