Dataset Information

Identification of robust and reproducible CT-texture metrics using a customized 3D-printed texture phantom.

ABSTRACT:

Objective

The objective of this study was to evaluate the robustness and reproducibility of computed tomography-based texture analysis (CTTA) metrics extracted from CT images of a customized texture phantom built for assessing the association of texture metrics to three-dimensional (3D) printed progressively increasing textural heterogeneity.

Materials and methods

A custom-built 3D-printed texture phantom comprising of six texture patterns was used to evaluate the robustness and reproducibility of a radiomics panel under a variety of routine abdominal imaging protocols. The phantom was scanned on four CT scanners (Philips, Canon, GE, and Siemens) to assess reproducibility. The robustness assessment was conducted by imaging the texture phantom across different CT imaging parameters such as slice thickness, field of view (FOV), tube voltage, and tube current for each scanner. The texture panel comprised of 387 features belonging to 15 subgroups of texture extraction methods (e.g., Gray-level Co-occurrence Matrix: GLCM). Twelve unique image settings were tested on all the four scanners (e.g., FOV125). Interclass correlation two-way mixed with absolute agreement (ICC3) was used to assess the robustness and reproducibility of radiomic features. Linear regression was used to test the association between change in radiomic features and increased texture heterogeneity. Results were summarized in heat maps.

Results

A total of 5612 (23.2%) of 24 090 features showed excellent robustness and reproducibility (ICC ? 0.9). Intensity, GLCM 3D, and gray-level run length matrix (GLRLM) 3D features showed best performance. Among imaging variables, changes in slice thickness affected all metrics more intensely compared to other imaging variables in reducing the ICC3. From the analysis of linear trend effect of the CTTA metrics, the top three metrics with high linear correlations across all scanners and scanning settings were from the GLRLM 2D/3D and discrete cosine transform (DCT) texture family.

Conclusion

The choice of scanner and imaging protocols affect texture metrics. Furthermore, not all CTTA metrics have a linear association with linearly varying texture patterns.

SUBMITTER: Varghese BA

PROVIDER: S-EPMC7882093 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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Publications

Identification of robust and reproducible CT-texture metrics using a customized 3D-printed texture phantom.

Varghese Bino A BA Hwang Darryl D Cen Steven Y SY Lei Xiaomeng X Levy Joshua J Desai Bhushan B Goodenough David J DJ Duddalwar Vinay A VA

Journal of applied clinical medical physics 20210112 2

<h4>Objective</h4>The objective of this study was to evaluate the robustness and reproducibility of computed tomography-based texture analysis (CTTA) metrics extracted from CT images of a customized texture phantom built for assessing the association of texture metrics to three-dimensional (3D) printed progressively increasing textural heterogeneity.<h4>Materials and methods</h4>A custom-built 3D-printed texture phantom comprising of six texture patterns was used to evaluate the robustness and r ...[more]

PMID: 33434374

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Project description:ObjectiveTo determine the intra-, inter- and test-retest variability of CT-based texture analysis (CTTA) metrics.Materials and methodsIn this study, we conducted a series of CT imaging experiments using a texture phantom to evaluate the performance of a CTTA panel on routine abdominal imaging protocols. The phantom comprises of three different regions with various textures found in tumors. The phantom was scanned on two CT scanners viz. the Philips Brilliance 64 CT and Toshiba Aquilion Prime 160 CT scanners. The intra-scanner variability of the CTTA metrics was evaluated across imaging parameters such as slice thickness, field of view, post-reconstruction filtering, tube voltage, and tube current. For each scanner and scanning parameter combination, we evaluated the performance of eight different types of texture quantification techniques on a predetermined region of interest (ROI) within the phantom image using 235 different texture metrics. We conducted the repeatability (test-retest) and robustness (intra-scanner) test on both the scanners and the reproducibility test was conducted by comparing the inter-scanner differences in the repeatability and robustness to identify reliable CTTA metrics. Reliable metrics are those metrics that are repeatable, reproducible and robust.ResultsAs expected, the robustness, repeatability and reproducibility of CTTA metrics are variably sensitive to various scanner and scanning parameters. Entropy of Fast Fourier Transform-based texture metrics was overall most reliable across the two scanners and scanning conditions. Post-processing techniques that reduce image noise while preserving the underlying edges associated with true anatomy or pathology bring about significant differences in radiomic reliability compared to when they were not used.ConclusionFollowing large-scale validation, identification of reliable CTTA metrics can aid in conducting large-scale multicenter CTTA analysis using sample sets acquired using different imaging protocols, scanners etc.

Project description:BackgroundComputed tomography (CT) imaging is the most important and common means of detecting and diagnosing pelvic bone tumors. While phantoms with sufficient flexibility and anatomical realism are useful in CT research, using phantoms has been difficult for pelvic bone tumors because of the tumors' relatively large size and highly variable shape. By combining medical 3D printing technology and fresh tumor specimens, this study aimed to design such a hybrid phantom, test its imaging properties, and demonstrate its usefulness in optimizing the CT protocols.MethodsTwo phantoms were designed for 2 patients with pelvic bone tumors who underwent surgical resection. One phantom was scanned with a routine pelvic CT protocol and compared against the patient image to test the imaging properties. We optimized the imaging protocol by assessing a series of varied settings on tube voltage (80, 100, 120, and 140 kVp), tube current (80, 120, and 160 to 200 mAs), and pitch factor (0.5, 0.8, 1.1, and 1.4) using the other phantom. These were assessed in comparison to the clinical reference of 140 kVp, 240 mAs, and 1.0 pitch, respectively. Image quality was quantified in terms of CT value, image noise, signal to noise ratio (SNR), and contrast to noise ratio (CNR) in various regions of interest.ResultsWith the routine protocol, the phantom image showed no significant difference in CT values of the bone and soft tissues and image noise compared to the patient image (all P values >0.05). With a lower tube voltage (80, 100, and 120 kVp) than the reference protocol, the CT value of bone tissue showed significant differences (all P values <0.001). No significant difference was found when applying a reduced tube current (all P values >0.05). With an increased helical pitch, pitches of 0.5, 0.8 and 1.1 were found to be comparable to those using the reference protocol (all P values >0.05).ConclusionsThe 3D-printed phantom can simulate the radiological properties of tumors in the pelvis and was successfully used in imaging studies of pelvic bone tumors. According to our preliminary findings, a low-dose pelvic CT protocol with acceptable image quality is achievable using reduced tube current or increased pitch.

Project description:Background:For surgical fixation of bone fractures of the human hand, so-called Kirschner-wires (K-wires) are drilled through bone fragments. Due to the minimally invasive drilling procedures without a view of risk structures like vessels and nerves, a thorough training of young surgeons is necessary. For the development of a virtual reality (VR) based training system, a three-dimensional (3D) printed phantom hand is required. To ensure an intuitive operation, this phantom hand has to be realistic in both, its position relative to the driller as well as in its haptic features. The softest 3D printing material available on the market, however, is too hard to imitate human soft tissue. Therefore, a support-material (SUP) filled metamaterial is used to soften the raw material. Realistic haptic features are important to palpate protrusions of the bone to determine the drilling starting point and angle. An optical real-time tracking is used to transfer position and rotation to the training system. Methods:A metamaterial already developed in previous work is further improved by use of a new unit cell. Thus, the amount of SUP within the volume can be increased and the tissue is softened further. In addition, the human anatomy is transferred to the entire hand model. A subcutaneous fat layer and penetration of air through pores into the volume simulate shiftability of skin layers. For optical tracking, a rotationally symmetrical marker attached to the phantom hand with corresponding reference marker is developed. In order to ensure trouble-free position transmission, various types of marker point applications are tested. Results:Several cuboid and forearm sample prints lead to a final 30 centimeter long hand model. The whole haptic phantom could be printed faultless within about 17 hours. The metamaterial consisting of the new unit cell results in an increased SUP share of 4.32%. Validated by an expert surgeon study, this allows in combination with a displacement of the uppermost skin layer a good palpability of the bones. Tracking of the hand marker in dodecahedron design works trouble-free in conjunction with a reference marker attached to the worktop of the training system. Conclusions:In this work, an optically tracked and haptically correct phantom hand was developed using dual-material 3D printing, which can be easily integrated into a surgical training system.

Dataset Information

Identification of robust and reproducible CT-texture metrics using a customized 3D-printed texture phantom.

Objective

Materials and methods

Results

Conclusion

Publications

Identification of robust and reproducible CT-texture metrics using a customized 3D-printed texture phantom.

Similar Datasets

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