The University of Osaka · Medicine
Professor Akinori Hata's research lab specializes in low-dose and functional chest CT imaging, focusing on the early detection, characterization, and clinical management of interstitial lung abnormalities (ILAs). The lab investigates advanced image reconstruction techniques—such as deep learning-based denoising and iterative reconstruction—to improve image quality and diagnostic accuracy in ultra-low-dose CT. A key research direction involves the application of dynamic X-ray (DXR) for functional assessment of lung physiology, including ventilation and diaphragmatic motion. The lab also explores quantitative imaging biomarkers, such as the traction bronchiectasis/bronchiolectasis index, to predict disease progression and mortality in interstitial lung diseases.
Figures are computed from collected data and may differ slightly.
The clinical importance of interstitial lung abnormality (ILA) is increasingly recognized. In July 2020, the Fleischner Society published a position paper about ILA. The purposes of this article are to summarize the definition, existing evidence, clinical management, and unresolved issues for ILA from a radiologic standpoint and to provide a practical guide for radiologists. ILA is a common incidental finding at CT and is often progressive and associated with worsened clinical outcomes. The haza
Dynamic X-ray (DXR) is a functional imaging technique that uses sequential images obtained by a flat-panel detector (FPD). This article aims to describe the mechanism of DXR and the analysis methods used as well as review the clinical evidence for its use. DXR analyzes dynamic changes on the basis of X-ray translucency and can be used for analysis of diaphragmatic kinetics, ventilation, and lung perfusion. It offers many advantages such as a high temporal resolution and flexibility in body posit
<b>OBJECTIVE.</b> The objective of our study was to assess the effect of the combination of deep learning-based denoising (DLD) and iterative reconstruction (IR) on image quality and Lung Imaging Reporting and Data System (Lung-RADS) evaluation on chest ultra-low-dose CT (ULDCT). <b>MATERIALS AND METHODS.</b> Forty-one patients with 252 nodules were evaluated retrospectively. All patients underwent ULDCT (mean ± SD, 0.19 ± 0.01 mSv) and standard-dose CT (SDCT) (6.46 ± 2.28 mSv). ULDCT images wer
<i>Interstitial lung abnormality</i> (ILA) is defined as an interstitial change detected incidentally on CT images. It is seen in 4%-9% of smokers and 2%-7% of nonsmokers. ILA has a tendency to progress with time and is associated with respiratory symptoms, decreased exercise capability, reduced pulmonary function, and increased mortality. ILAs can be classified into three subcategories: nonsubpleural, subpleural nonfibrotic, and subpleural fibrotic. In cases of ILA, clinically significant inter
Background The clinical impact of interstitial lung abnormalities (ILAs) on poor prognosis has been reported in many studies, but risk stratification in ILA will contribute to clinical practice. Purpose To investigate the association of traction bronchiectasis/bronchiolectasis index (TBI) with mortality and clinical outcomes in individuals with ILA by using the COPDGene cohort. Materials and Methods This study was a secondary analysis of prospectively collected data. Chest CT scans of participan
The aim of this study was to assess the effects of reconstruction on the image quality and quantitative analysis for interstitial lung disease (ILD) using filtered back projection (FBP) and model-based iterative reconstruction (MBIR) with the lung setting and the conventional setting on ultra-low-dose computed tomography (CT).Fifty-two patients with known ILD were prospectively enrolled and underwent CT at an ultra-low dose (0.18 ± 0.02 mSv) and a standard dose (7.01 ± 2.66 mSv). Ultra-low-dose
This retrospective study developed an automated algorithm for 3D segmentation of adipose tissue and paravertebral muscle on chest CT using artificial intelligence (AI) and assessed its feasibility. The study included patients from the Boston Lung Cancer Study (2000-2011). For adipose tissue quantification, 77 patients were included, while 245 were used for muscle quantification. The data were split into training and test sets, with manual segmentation as the ground truth. Subcutaneous and viscer
"Traction Bronchiectasis/Bronchiolectasis in Interstitial Lung Abnormality: Follow-up in the COPDGene Study." American Journal of Respiratory and Critical Care Medicine, 207(10), pp. 1395–1398
Two texture features (Coefficient Variation and Entropy) were significant indicators to predict solid and micropapillary components in lung invasive adenocarcinoma.
PCD-CT visualized small nodules and airways better than EID-CT and improved with high spatial resolution and potentially can detect submillimeter nodules and airways.
Model-based iterative reconstruction improved the agreement between ULDCT and SDCT on emphysema quantification.
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