Characterization of office laser printers for 3-D printing of soft tissue CT phantoms

Development of a neonate X-ray phantom for 2D imaging applications using single-tone inkjet printing

PURPOSE

Inkjet printers can be used to fabricate anthropomorphic phantoms by the use of iodine-doped ink. However, challenges persist in implementing this technique. The calibration from grayscale to ink density is complex and time-consuming. The purpose of this work is to develop a printing methodology that requires a simpler calibration and is less dependent on printer characteristics to produce the desired range of x-ray attenuation values.

METHODS

Conventional grayscale printing was substituted by single-tone printing; that is, the superposition of pure black layers of iodinated ink. Printing was performed with a consumer-grade inkjet printer using ink made of potassium-iodide (KI) dissolved in water at 1 g/ml. A calibration for the attenuation of ink was measured using a commercial x-ray system at 70 kVp. A neonate radiograph obtained at 70 kVp served as an anatomical model. The attenuation map of the neonate radiograph was processed into a series of single-tone images. Single-tone images were printed, stacked, and imaged at 70 kVp. The phantom was evaluated by comparing attenuation values between the printed phantom and the original radiograph; attenuation maps were compared using the structural similarity index measure (SSIM), while attenuation histograms were compared using the Kullback-Leibler (KL) divergence. A region of interest (ROI)-based analysis was also performed, where the attenuation distribution within given ROIs was compared between phantom and patient. The phantom sharpness was evaluated in terms of modulation transfer function (MTF) estimates and signal spread profiles of high spatial resolution features in the image.

RESULTS

The printed phantom required 36 pages. The printing queue was automated and it took about 2 h to print the phantom. The radiograph of the printed phantom demonstrated a close resemblance to the original neonate radiograph. The SSIM of the phantom with respect to that of the patient was 0.53. Both patient and phantom attenuation histograms followed similar distributions, and the KL divergence between such histograms was 0.20. The ROI-based analysis showed that the largest deviations from patient attenuation values were observed at the higher and lower ends of the attenuation range. The limiting resolution of the proposed methodology was about 1 mm.

CONCLUSION

A methodology to generate a neonate phantom for 2D imaging applications, using single-tone printing, was developed. This method only requires a single-value calibration and required less than 2 h to print a complete phantom.

Improving accuracy and robustness of deep convolutional neural network based thoracic OAR segmentation

Deep convolutional neural network (DCNN) has shown great success in various medical image segmentation tasks, including organ-at-risk (OAR) segmentation from computed tomography (CT) images. However, most studies use the dataset from the same source(s) for training and testing so that the ability of a trained DCNN to generalize to a different dataset is not well studied, as well as the strategy to address the issue of performance drop on a different dataset. In this study we investigated the performance of a well-trained DCNN model from a public dataset for thoracic OAR segmentation on a local dataset and explored the systematic differences between the datasets. We observed that a subtle shift of organs inside patient body due to the abdominal compression technique during image acquisition caused significantly worse performance on the local dataset. Furthermore, we developed an optimal strategy via incorporating different numbers of new cases from the local institution and using transfer learning to improve the accuracy and robustness of the trained DCNN model. We found that by adding as few as 10 cases from the local institution, the performance can reach the same level as in the original dataset. With transfer learning, the training time can be significantly shortened with slightly worse performance for heart segmentation.

Optimizing the Spectral Characterisation of a CMYK Printer with Embedded CMY Printer Modelling

In the digital printing process, reliable colour reproduction is commonly achieved by printer characterisation, which defines the correspondence between the input device control values and the output colour information. The cellular Yule–Nielsen spectral Neugebauer model, together with its variants, is widely adopted in this topic because of its superb colorimetric and spectral accuracy. However, it seems that current studies have neglected an inconspicuous defect in such models when characterising printers equipped with black ink. That is, the cellular structure of these models overemphasises the sampling for dark-tone colours, and thus leads to relatively large errors in light tones. In this paper, taking a CMYK printer as an example, a simple and effective solution is proposed with no need of extra sampling. With the aid of a newly built cellular spectral Neugebauer model for the embedded CMY printer, this approach optimises the printer characterisation for light tones, slightly improves the precision for middle tones while it maintains the accuracy for dark tones. The performance of the proposed method was evaluated with regard to three different kinds of substrates and the experimental results validated its improvement in spectral printer characterisation.