Dataset Information

The accuracy and precision of acetabular implant measurements from CT imaging.

ABSTRACT: The placement of acetabular implant components determines the short- and long-term outcomes of total hip replacement (THR) and a number of tools have been developed to assist the surgeon in achieving cup orientation to match the surgical plan. However, the accuracy and precision of 3D-CT for the measurement of acetabular component position and orientation is yet to be established. To investigate this, we compared measurements of cobalt chrome acetabular components implanted into 2 different bony pelvic models between a coordinate measuring Faro arm and 3 different low dose CT images, including 3D-CT, 2D anterior pelvic plane (APP) referenced CT and 2D scanner referenced (SR) CT. Intra-observer differences were assessed using the Intraclass correlation coefficient (ICC). The effect of imaging the pelvis positioned in 3 different orientations within the CT scanner was also assessed. The measured parameters were the angles of inclination and version. 3D-CT measurements were found to closely match the "true values" of the component position measurements, compared with the 2D-CT methods. ICC analysis also showed good agreement between the coordinate measuring arm (CMA) and 3D-CT but poor agreement between the 2D SR method, in the results from two observers. When using the coordinate system of the CT scanner, the measurements consistently produced the greatest error; this method yielded values up to 34° different from the reference digitising arm. However, the difference between the true inclination and version angles and those measured from 3D APP CT was below half a degree in all cases. We concluded that low radiation dose 3D-CT is a validated reference standard for the measurement of acetabular cup orientation.

SUBMITTER: Henckel J

PROVIDER: S-EPMC10175694 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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The accuracy and precision of acetabular implant measurements from CT imaging.

Henckel Johann J Ramesh Angelika A Hothi Harry H Richards Robin R Di Laura Anna A Hart Alister A

Frontiers in bioengineering and biotechnology 20230428

The placement of acetabular implant components determines the short- and long-term outcomes of total hip replacement (THR) and a number of tools have been developed to assist the surgeon in achieving cup orientation to match the surgical plan. However, the accuracy and precision of 3D-CT for the measurement of acetabular component position and orientation is yet to be established. To investigate this, we compared measurements of cobalt chrome acetabular components implanted into 2 different bony ...[more]

PMID: 37187883

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Project description:BackgroundRenal quantitative measurements are important descriptors for assessing kidney function. We developed a deep learning-based method for automated kidney measurements from computed tomography (CT) images.MethodsThe study datasets comprised potential kidney donors (n = 88), both contrast-enhanced (Dataset 1 CE) and noncontrast (Dataset 1 NC) CT scans, and test sets of contrast-enhanced cases (Test set 2, n = 18), cases from a photon-counting (PC)CT scanner reconstructed at 60 and 190 keV (Test set 3 PCCT, n = 15), and low-dose cases (Test set 4, n = 8), which were retrospectively analyzed to train, validate, and test two networks for kidney segmentation and subsequent measurements. Segmentation performance was evaluated using the Dice similarity coefficient (DSC). The quantitative measurements' effectiveness was compared to manual annotations using the intraclass correlation coefficient (ICC).ResultsThe contrast-enhanced and noncontrast models demonstrated excellent reliability in renal segmentation with DSC of 0.95 (Test set 1 CE), 0.94 (Test set 2), 0.92 (Test set 3 PCCT) and 0.94 (Test set 1 NC), 0.92 (Test set 3 PCCT), and 0.93 (Test set 4). Volume estimation was accurate with mean volume errors of 4%, 3%, 6% mL (contrast test sets) and 4%, 5%, 7% mL (noncontrast test sets). Renal axes measurements (length, width, and thickness) had ICC values greater than 0.90 (p < 0.001) for all test sets, supported by narrow 95% confidence intervals.ConclusionTwo deep learning networks were shown to derive quantitative measurements from contrast-enhanced and noncontrast renal CT imaging at the human performance level.Relevance statementDeep learning-based networks can automatically obtain renal clinical descriptors from both noncontrast and contrast-enhanced CT images. When healthy subjects comprise the training cohort, careful consideration is required during model adaptation, especially in scenarios involving unhealthy kidneys. This creates an opportunity for improved clinical decision-making without labor-intensive manual effort.Key pointsTrained 3D UNet models quantify renal measurements from contrast and noncontrast CT. The models performed interchangeably to the manual annotator and to each other. The models can provide expert-level, quantitative, accurate, and rapid renal measurements.

Project description:To prospectively quantify the accuracy of hip cartilage thickness estimated from three-dimensional (3D) surfaces, generated by segmenting multidetector computed tomographic (CT) arthrograms by using direct physical measurements of cartilage thickness as the reference standard.Four fresh-frozen cadaver hip joints from two male donors, ages 43 and 46 years, were obtained; institutional review board approval for cadaver research was also obtained. Sixteen holes were drilled perpendicular to the cartilage of four cadaveric acetabula (two specimens). Hip capsules were surgically closed, injected with contrast material, and scanned by using multidetector CT. After scanning, 5.3-mmcores were harvested concentrically at each drill hole and cartilage thickness was measured with a microscope. Cartilage was reconstructed in 3D by using commercial software. Segmentations were repeated by two authors. Reconstructed cartilage thickness was determined by using a published algorithm. Bland-Altman plots and linear regression were used to assess accuracy. Repeatability was quantified by using the coefficient of variation, intraclass correlation coefficient (ICC), repeatability coefficient, and percentage variability.Cartilage was reconstructed to a bias of -0.13 mm and a repeatability coefficient of + or - 0.46 mm. Regression of the scatterplots indicated a tendency for multidetector CT to overestimate thickness. Intra- and interobserver repeatability were very good. For intraobserver correlation, the coefficient of variation was 14.80%, the ICC was 0.88, the repeatability coefficient was 0.55 mm, and the percentage variability was 11.77%. For interobserver correlation, the coefficient of variation was 13.47%, the ICC was 0.90, the repeatability coefficient was 0.52 mm, and the percentage variability was 11.63%.Assuming that an accuracy of approximately + or - 0.5 mm is sufficient, reconstructions of cartilage geometry from multidetector CT arthrographic data could be used as a preoperative surgical planning tool.

Dataset Information

The accuracy and precision of acetabular implant measurements from CT imaging.

Publications

The accuracy and precision of acetabular implant measurements from CT imaging.

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