Ex vivo characterization of pathologic fluids with quantitative phase-contrast computed tomography

Automated CT segmentation for lower extremity tissues in lymphedema evaluation using deep learning

OBJECTIVES

Clinical assessment of lymphedema, particularly for lymphedema severity and fluid-fibrotic lesions, remains challenging with traditional methods. We aimed to develop and validate a deep learning segmentation tool for automated tissue component analysis in lower extremity CT scans.

MATERIALS AND METHODS

For development datasets, lower extremity CT venography scans were collected in 118 patients with gynecologic cancers for algorithm training. Reference standards were created by segmentation of fat, muscle, and fluid-fibrotic tissue components using 3D slicer. A deep learning model based on the Unet++ architecture with an EfficientNet-B7 encoder was developed and trained. Segmentation accuracy of the deep learning model was validated in an internal validation set (n = 10) and an external validation set (n = 10) using Dice similarity coefficient (DSC) and volumetric similarity (VS). A graphical user interface (GUI) tool was developed for the visualization of the segmentation results.

RESULTS

Our deep learning algorithm achieved high segmentation accuracy. Mean DSCs for each component and all components ranged from 0.945 to 0.999 in the internal validation set and 0.946 to 0.999 in the external validation set. Similar performance was observed in the VS, with mean VSs for all components ranging from 0.97 to 0.999. In volumetric analysis, mean volumes of the entire leg and each component did not differ significantly between reference standard and deep learning measurements (p > 0.05). Our GUI displays lymphedema mapping, highlighting segmented fat, muscle, and fluid-fibrotic components in the entire leg.

CONCLUSION

Our deep learning algorithm provides an automated segmentation tool enabling accurate segmentation, volume measurement of tissue component, and lymphedema mapping.

KEY POINTS

Question Clinical assessment of lymphedema remains challenging, particularly for tissue segmentation and quantitative severity evaluation. Findings A deep learning algorithm achieved DSCs > 0.95 and VS > 0.97 for fat, muscle, and fluid-fibrotic components in internal and external validation datasets. Clinical relevance The developed deep learning tool accurately segments and quantifies lower extremity tissue components on CT scans, enabling automated lymphedema evaluation and mapping with high segmentation accuracy.

Establishing a Foundation for the In Vivo Visualization of Intravascular Blood with Photon-Counting Technology in Spectral Imaging in Cranial CT

BACKGROUND

Advances in computed tomography (CT) technology, particularly photon-counting CT (PCCT), are reshaping the possibilities for medical imaging. PCCT in spectral imaging enables the high-resolution visualization of tissues with material-specific accuracy. This study aims to establish a foundational approach for the in vivo visualization of intracranial blood using PCCT, focusing on non-enhanced imaging techniques and spectral imaging capabilities.

METHODS

We employed photon-counting detector within a spectral CT framework to differentiate between venous and arterial intracranial blood. Our analysis included not only monoenergetic +67 keV reconstructions, but also images from virtual non-contrast and iodine phases, enabling detailed assessments of blood's characteristics without the use of contrast agents.

RESULTS

Our findings demonstrate the ability of PCCT to provide clear and distinct visualizations of intracranial vascular structures. We quantified the signal-to-noise ratio across different imaging phases and found consistent enhancements in image clarity, particularly in the detection and differentiation of arterial and venous blood.

CONCLUSION

PCCT offers a robust platform for the non-invasive and detailed visualization of intravascular intracranial blood. With its superior resolution and specific imaging capabilities, PCCT lays the groundwork for advancing clinical applications and research, notably in the diagnosis and management of intracranial disorders. This technology promises to improve diagnostic accuracy by enabling more precise imaging assessments.

Quantitative X-ray phase contrast computed tomography with grating interferometry : Biomedical applications of quantitative X-ray grating-based phase contrast computed tomography

The ability of biomedical imaging data to be of quantitative nature is getting increasingly important with the ongoing developments in data science. In contrast to conventional attenuation-based X-ray imaging, grating-based phase contrast computed tomography (GBPC-CT) is a phase contrast micro-CT imaging technique that can provide high soft tissue contrast at high spatial resolution. While there is a variety of different phase contrast imaging techniques, GBPC-CT can be applied with laboratory X-ray sources and enables quantitative determination of electron density and effective atomic number. In this review article, we present quantitative GBPC-CT with the focus on biomedical applications.

Quantitative characterization of ex vivo breast tissue via x-ray phase-contrast tomography

BACKGROUND

Grating-based X-ray phase-contrast imaging (GPCI) has received growing interests in recent years due to its high capability of visualizing soft tissue. Breast imaging is one of the most promising candidates for the first clinical application of this imaging modality.

OBJECTIVE

In this work, quantitative breast tissue characterization based on GPCI computed tomography (CT) is investigated with a laboratory X-ray tube through a comparison between attenuation-based CT images and phase-contrast CT images.

METHODS

The Hounsfield units (HU) scale was introduced to phase-contrast images due to its wide application in clinical medicine. In this work, instead of water, plastic cylinders composed of polyethylene terephthalate (PET) was treated as the calibration material. An alternative test-retest reliability (TRR) was presented to evaluate the repeatability of GPCI. Comparison between attenuation-based CT imaging and GPCI CT imaging was operated with the use of statistical analysis methods like histograms and receiver operating characteristic (ROC) curves.

RESULTS

The determined mean TRR related to cylinders is slightly larger in phase-contrast imaging (0.93) than that in attenuation-based imaging (0.89). With respect to distinguishing breast tissues, the AUC (area under curve) values of ROC curves of phase-contrast images are higher than that of attenuation-based images.

CONCLUSIONS

An ex vivo study of GPCI shows that it is a stable imaging modality for visualizing the breast tissue with good repeatability, and that it could be of potential for the diagnosis of breast cancer as well.