Search

Abstract Background 3D printing has a wide range of applications and has brought significant change to many medical fields. However, ensuring quality assurance (QA) is essential for patient safety and requires a QA program that encompasses the entire production process. This process begins with imaging and continues on with segmentation, which is the conversion of Digital Imaging and Communications in Medicine (DICOM) data into virtual 3D-models. Since segmentation is highly influenced by manual intervention the influence of the users background on segmentation accuracy should be thoroughly investigated. Methods Seventeen computed tomography (CT) scans of the pelvis with physiological bony structures were identified, anonymized, exported as DICOM data sets, and pelvic bones were segmented by four observers with different backgrounds. Landmarks were measured on DICOM images and in the segmentations. Intraclass correlation coefficients (ICCs) were calculated to assess inter-observer agreement, and the trueness of the segmentation results was analyzed by comparing the DICOM landmark measurements with the measurements of the segmentation results. The correlation between segmentation trueness and segmentation time was analyzed. Results The lower limits of the 95% confidence intervals of the ICCs for the seven landmarks analyzed ranged from 0.511 to 0.986. The distance between the iliac crests showed the highest agreement between observers, while the distance between the ischial tuberosities showed the lowest. The distance between the upper edge of the symphysis and the promontory showed the lowest deviation between DICOM measurements and segmentation measurements (mean deviations

Abstract Background The responsible use of 3D-printing in medicine includes a context-based quality assurance. Considerable literature has been published in this field, yet the quality of assessment varies widely. The limited discriminatory power of some assessment methods challenges the comparison of results. The total error for patient specific anatomical models comprises relevant partial errors of the production process: segmentation error (SegE), digital editing error (DEE), printing error (PrE). The present review provides an overview to improve the general understanding of the process specific errors, quantitative analysis, and standardized terminology. Methods This review focuses on literature on quality assurance of patient-specific anatomical models in terms of geometric accuracy published before December 4th, 2022 (n = 139). In an attempt to organize the literature, the publications are assigned to comparable categories and the absolute values of the maximum mean deviation (AMMD) per publication are determined therein. Results The three major examined types of original structures are teeth or jaw (n = 52), skull bones without jaw (n = 17) and heart with coronary arteries (n = 16). VPP (vat photopolymerization) is the most frequently employed basic 3D-printing technology (n = 112 experiments). The median values of AMMD (AMMD: The metric AMMD is defined as the largest linear deviation, based on an average value from at least two individual measurements.) are 0.8 mm for the SegE, 0.26 mm for the PrE and 0.825 mm for the total error. No average values are found for the DEE. Conclusion The total error is not significantly higher than the partial errors which may compensate each other. Consequently SegE, DEE and PrE should be analyzed individually to describe the result quality as their sum according to rules of error propagation. Current methods for quality assurance of the segmentation are often either realistic and accurate or resource efficient. Future research should focus on implementing models for cost effective evaluations with high accuracy and realism. Our system of categorization may be enhancing the understanding of the overall process and a valuable contribution to the structural design and reporting of future experiments. It can be used to educate specialists for risk assessment and process validation within the additive manufacturing industry. Graphical Abstract Context of the figures in this review. Center: Fig. 5+ 7; top (blue): Fig. 8; right (green): Fig. 9; bottom (yellow): Fig. 10; left (red): Fig. 11. A version in high resolution can be found online in the supplementary material.

Abstract Backgrounds Elastic motion correction in PET has been shown to increase image quality and quantitative measurements of PET datasets affected by respiratory motion. However, little is known on the impact of respiratory motion correction on clinical image evaluation in oncologic PET. This study evaluated the impact of motion correction on expert readers’ lymph node assessment of lung cancer patients. Methods Forty-three patients undergoing F-18-FDG PET/CT for the staging of suspected lung cancer were included. Three different PET reconstructions were investigated: non-motion-corrected (“static”), belt gating-based motion-corrected (“BG-MC”) and data-driven gating-based motion-corrected (“DDG-MC”). Assessment was conducted independently by two nuclear medicine specialists blinded to the reconstruction method on a six-point scale $$s$$ s ranging from “certainly negative” (1) to “certainly positive” (6). Differences in $$s$$ s between reconstruction methods, accounting for variation caused by readers, were assessed by nonparametric regression analysis of longitudinal data. From $$s$$ s , a dichotomous score for N1, N2, and N3 (“negative,” “positive”) and a subjective certainty score were derived. SUV and metabolic tumor volumes (MTV) were compared between reconstruction methods. Results BG-MC resulted in higher scores for N1 compared to static (p = 0.001), whereas DDG-MC resulted in higher scores for N2 compared to static (p = 0.016). Motion correction resulted in the migration of N1 from tumor free to metastatic on the dichotomized score, consensually for both readers, in 3/43 cases and in 2 cases for N2. SUV was significantly higher for motion-corrected PET, while MTV was significantly lower (all p