Multi-detector CT (MDCT) evaluation in interstitial lung disease (ILD): Comparison of MinIP and volumetric high resolution CT (HRCT) images

High-Resolution Computed Tomography of Cystic Lung Disease

The cystic lung diseases (CLD) are characterized by the presence of multiple, thin-walled, air-filled spaces in the pulmonary parenchyma. Cyst formation may occur with congenital, autoimmune, inflammatory, infectious, or neoplastic processes. Recognition of cyst mimics such as emphysema and bronchiectasis is important to prevent diagnostic confusion and unnecessary evaluation. Chest CT can be diagnostic or may guide the workup based on cyst number, distribution, morphology, and associated lung, and extrapulmonary findings. Diffuse CLD (DCLDs) are often considered those presenting with 10 or more cysts. The more commonly encountered DCLDs include lymphangioleiomyomatosis, pulmonary Langerhans' cell histiocytosis, lymphoid interstitial pneumonia, Birt-Hogg-Dubé syndrome, and amyloidosis/light chain deposition disease.

Value of minimum intensity projections for chest CT in COVID-19 patients

PURPOSE

To investigate whether minimum intensity projection (MinIP) reconstructions enable more accurate depiction of pulmonary ground-glass opacity (GGO) compared to standard transverse sections and multiplanar reformat (MPR) series in patients with suspected coronavirus disease 2019 (COVID-19).

METHOD

In this multinational study, chest CT scans of 185 patients were retrospectively analyzed. Diagnostic accuracy, diagnostic confidence, image quality regarding the assessment of GGO, as well as subjective time-efficiency of MinIP and standard MPR series were analyzed based on the assessment of six radiologists. In addition, the suitability for COVID-19 evaluation, image quality regarding GGO and subjective time-efficiency in clinical routine was assessed by five clinicians.

RESULTS

The reference standard revealed a total of 149 CT scans with pulmonary GGO. MinIP reconstructions yielded significantly higher sensitivity (99.9 % vs 95.6 %), specificity (95.8 % vs 86.1 %) and accuracy (99.1 % vs 93.8 %) for assessing of GGO compared with standard MPR series. MinIP reconstructions achieved significantly higher ratings by radiologists concerning diagnostic confidence (medians, 5.00 vs 4.00), image quality (medians, 4.00 vs 4.00), contrast between GGO and unaffected lung parenchyma (medians, 5.00 vs 4.00) as well as subjective time-efficiency (medians, 5.00 vs 4.00) compared with MPR-series (all P < .001). Clinicians preferred MinIP reconstructions for COVID-19 assessment (medians, 5.00 vs 3.00), image quality regarding GGO (medians, 5.00 vs 3.00) and subjective time-efficiency in clinical routine (medians, 5.00 vs 3.00).

CONCLUSIONS

MinIP reconstructions improve the assessment of COVID-19 in chest CT compared to standard images and may be suitable for routine application.

Application of high-resolution CT images information in complicated infection of lung tumors

To explore the quality of high-resolution CT images information in the evaluation of pulmonary nodule interface and internal structure of nodules in lung tissue, as well as the value of early diagnosis of lung cancer associated with infection, high-resolution CT images were used as the research object. Through the analysis of the computerized detection and diagnosis (Computer-Aided Diagnosis (CAD)) of lung cancer, the high-resolution CT was further explored in the process of clinical imaging doctors in the diagnosis of lung cancer, and more conditions were created for the application of medical image processing in the early diagnosis of lung cancer. The research results show that CAD can automatically and accurately complete the automatic segmentation of the lung region in the CT image by applying the automatic segmentation algorithm for a series of processing and analysis of the CT image, that is, generating high-resolution CT images. It can enhance the pulmonary nodules in CT images and improve the accuracy of lung nodule detection, which is of great value in the diagnosis of early lung cancer. CAD diagnosis of lung lesions based on high-resolution CT images is studied, which can provide reference for imaging physicians to diagnose early lung cancer. However, in the automatic identification of benign and malignant lesions in the lungs, it is necessary to further improve the analysis function of similar nodules, which will be an important step for humans in the diagnosis and treatment of diseases.