98%
921
2 minutes
20
Non-alcoholic fatty liver disease (NAFLD) is becoming a global public health issue and the identification of the steatosis severity is very important for the patients' health. Ultrasound (US) images of 214 patients were acquired in two different scan views (subcostal and intercostal). A classification of the level of steatosis was made by a qualitative evaluation of the liver ultrasound images. Furthermore, an US image processing algorithm provided quantitative parameters (hepatic-renal ratio (HR) and Steato-score) designed to quantifying the fatty liver content. The aim of the study is to evaluate the differences in the assessment of hepatic steatosis acquiring and processing different US scan views. No significant differences were obtained calculating the HR and the Steato-score parameters, not even with the classification of patients on the basis of body mass index (BMI) and of different classes of steatosis severity. Significant differences between the two parameters were found only for patients with absence or mild level of steatosis. These results show that the two different scan projections do not greatly affect HR and the Steato-score assessment. Accordingly, the US-based steatosis assessment is independent from the view of the acquisitions, thus making the subcostal and intercostal scans interchangeable, especially for patients with moderate and severe steatosis.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8872438 | PMC |
http://dx.doi.org/10.3390/healthcare10020374 | DOI Listing |
J Med Imaging (Bellingham)
September 2025
Otto von Guericke University, Institute for Medical Engineering and Research Campus STIMULATE, Magdeburg, Germany.
Purpose: The combination of multi-layer flat panel detector (FPDT) X-ray imaging and physics-based material decomposition algorithms allows for the removal of anatomical structures. However, the reliability of these algorithms may be compromised by unaccounted materials or scattered radiation.
Approach: We investigated the two-material decomposition performance of a multi-layer FPDT in the context of 2D chest radiography without and with a 13:1 anti-scatter grid employed.
Med Phys
August 2025
Division of X-Ray Imaging and CT, German Cancer Research Center (DKFZ), Heidelberg, Germany.
Background: The optimal tube voltage in clinical CT depends on the patient's attenuation and the imaging task. Although the patient's attenuation changes with view angle and longitudinal position of the X-ray tube, the tube voltage remains constant throughout the scan in current clinical practice. In general, the optimum tube voltage increases with patient diameter.
View Article and Find Full Text PDFMed Phys
September 2025
Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA.
Background: Free-breathing gated cone-beam computed tomography (gCBCT), which captures a specific anatomy coinciding with a preset gating window in the breathing cycle, is routinely prescribed to gating lung SBRT patients for pretreatment setup verification. However, a half-fan gCBCT scan can take 2-8 min (for a typical gating duty cycle of 30%-60% and patient breathing period of 3-6 s) on a C-arm linear accelerator because the gantry movement is interrupted and resumed by the respiratory gating signal multiple times over the scan. The long scan time increases patient on-table time, leading to discomfort and a higher likelihood of patient movement.
View Article and Find Full Text PDFJ Appl Clin Med Phys
September 2025
Department of Radiology, Emory University School of Medicine, Atlanta, Georgia, USA.
Background: Ultra-wide coverage CT (> 128 detector rows) makes it possible to image a heart or brain in a single rotation but are associated with large cone angles, which can severely degrade the image quality.
Purpose: This study evaluate the image quality and artifact levels of a dual-focal-spot single-detector (DFSSD) CT geometry designed to achieve 140 mm z-axis coverage, through a simulation study.
Methods: The DFSSD CT system employs two x-ray focal spots spaced 90 mm apart along the z-axis and a 100 mm CT detector.
Sci Rep
August 2025
Musculoskeletal Digital Innovation and Informatics (MDI²) Program, Department of Orthopaedic and Sports Medicine, Boston Children's Hospital, Harvard Medical School, Boston MA, USA.
It is well known that machine learning models require a high amount of annotated data to obtain optimal performance. Labelling Computed Tomography (CT) data can be a particularly challenging task due to its volumetric nature and often missing and/or incomplete associated meta-data. Even inspecting one CT scan requires additional computer software, or in the case of programming languages-additional programming libraries.
View Article and Find Full Text PDF