Three-dimensional printing of patient-specific lung phantoms for CT imaging: Emulating lung tissue with accurate attenuation profiles and textures

Abstract

Purpose: Phantoms are a basic tool for assessing and verifying performance in CT research and clinical practice. Patient-based realistic lung phantoms accurately representing textures and densities are essential in developing and evaluating novel CT hardware and software. This study introduces PixelPrint, a 3D printing solution to create patient-based lung phantoms with accurate attenuation profiles and textures.

Methods: PixelPrint, a software tool, was developed to convert patient digital imaging and communications in medicine (DICOM) images directly into FDM printer instructions (G-code). Density was modeled as the ratio of filament to voxel volume to emulate attenuation profiles for each voxel, with the filament ratio controlled through continuous modification of the printing speed. A calibration phantom was designed to determine the mapping between filament line width and Hounsfield units (HU) within the range of human lungs. For evaluation of PixelPrint, a phantom based on a single human lung slice was manufactured and scanned with the same CT scanner and protocol used for the patient scan. Density and geometrical accuracy between phantom and patient CT data were evaluated for various anatomical features in the lung.

Results: For the calibration phantom, measured mean HU show a very high level of linear correlation with respect to the utilized filament line widths, (r > 0.999). Qualitatively, the CT image of the patient-based phantom closely resembles the original CT image both in texture and contrast levels (from -800 to 0 HU), with clearly visible vascular and parenchymal structures. Regions of interest comparing attenuation illustrated differences below 15 HU. Manual size measurements performed by an experienced thoracic radiologist reveal a high degree of geometrical correlation of details between identical patient and phantom features, with differences smaller than the intrinsic spatial resolution of the scans.

Conclusion: The present study demonstrates the feasibility of 3D-printed patient-based lung phantoms with accurate organ geometry, image texture, and attenuation profiles. PixelPrint will enable applications in the research and development of CT technology, including further development in radiomics.

Keywords: 3D printing; computed tomography; image quality; lung; quality assurance; radiomics.

FIGURE 1

Calibration phantom. (a) Photo of the multi-sector calibration phantom. (b) CT slice of the phantom. (c) ROIs (blue squares, width 11 mm) used to evaluate the HU for Figure 2. (e) Histogram of green circular region in (d). Data were acquired as reported in Table 1 (CTDI_vol 12.5 mGy). All ROI measurements are applied in five adjacent slices. Window level is −400 HU. Window width is 1500 HU

FIGURE 2

Linear regression between material density and Hounsfield Units. Blue markers and error bars show the mean values and standard deviation over 5 slices for regions of material density 10% to 100% in the multi-sector calibration phantom. The yellow line represents the regression line. r is Pearson’s correlation coefficient

FIGURE 3

Patient-based Lung Phantom visually highly resembles the original CT image both in texture and contrast levels. (a) Photography of the printed patient-based phantom. (b) CT image of patient-based phantom. (c) CT image of patient lung. Yellow, red and blue boxes indicate zoomed-in regions of the patient DICOM image. Window level is −500 HU. Window width is 1000 HU

FIGURE 4

Locations and size of the selected regions of interest for density measurements in patient and phantom data. (a) CT image of patient-based phantom. (b) CT image of original patient lung. Window level is −500 HU. Window width is 1000 HU

FIGURE 5

Locations and size of the selected anatomical features for size measurements in patient and phantom data. (a, c) CT image of patient-based phantom. (b, d) CT image of original patient lung. Window level is −500 HU. Window width is 1000 HU

FIGURE 6

Line profile of the patient-based phantom (blue) versus the same location from the original patient image (yellow). Window level is −500 HU. Window width is 1000 HU

FIGURE 7

Histogram of (a, b) the whole lung and (c-h) the regions of interest selected in Figure 2. (a, c, e, g) are from PixelPrint phantom CT images. (b, d, f, h) are from original patient CT images. Blue, red, and yellow indicate the selected regions in Figure 2. Note: due to the 10% and 100% cut-off, signal accumulates at 0 HU and −800 HU in PixelPrint (dashed lines)