Tailoring the Mass Density of 3D Printing Materials for Accurate X-ray Imaging Simulation by Controlled Underfilling for Radiographic Phantoms.

Additive manufacturing and 3D printing allow for the design and rapid production of radiographic phantoms for X-ray imaging, including CT. These are used for numerous purposes, such as patient simulation, optimization of imaging procedures and dose levels, system evaluation and quality assurance. However, standard 3D printing polymers do not mimic X-ray attenuation properties of tissues like soft, adipose, lung or bone tissue, and standard materials like liquid water. The mass density of printing polymers-especially important in CT-is often inappropriate, i.e., mostly too high. Different methods can be applied to reduce mass density. This work examines reducing density by controlled underfilling either realized by using 3D printing materials expanded through foaming during heating in the printing process, or reducing polymer flow to introduce microscopic air-filled voids. The achievable density reduction depends on the base polymer used. When using foaming materials, density is controlled by the extrusion temperature, and ranges from 33 to 47% of the base polymer used, corresponding to a range of -650 to -394 HU in CT with 120 kV. Standard filaments (Nylon, modified PLA and modified ABS) allowed density reductions by 20 to 25%, covering HU values in CT from -260 to 77 (Nylon), -230 to -20 (ABS) and -81 to 143 (PLA). A standard chalk-filled PLA filament allowed reproduction of bone tissue in a wide range of bone mineral content resulting in CT numbers from 57 to 460 HU. Controlled underfilling allowed the production of radiographic phantom materials with continuously adjustable attenuation in a limited but appropriate range, allowing for the reproduction of X-ray attenuation properties of water, adipose, soft, lung, and bone tissue in an accurate, predictable and reproducible manner.

Diagnostic validation of a deep learning nodule detection algorithm in low-dose chest CT: determination of optimized dose thresholds in a virtual screening scenario.

OBJECTIVES: This study was conducted to evaluate the effect of dose reduction on the performance of a deep learning (DL)-based computer-aided diagnosis (CAD) system regarding pulmonary nodule detection in a virtual screening scenario. METHODS: Sixty-eight anthropomorphic chest phantoms were equipped with 329 nodules (150 ground glass, 179 solid) with four sizes (5 mm, 8 mm, 10 mm, 12 mm) and scanned with nine tube voltage/current combinations. The examinations were analyzed by a commercially available DL-based CAD system. The results were compared by a comparison of proportions. Logistic regression was performed to evaluate the impact of tube voltage, tube current, nodule size, nodule density, and nodule location. RESULTS: The combination with the lowest effective dose (E) and unimpaired detection rate was 80 kV/50 mAs (sensitivity: 97.9%, mean false-positive rate (FPR): 1.9, mean CTDIvol: 1.2 ± 0.4 mGy, mean E: 0.66 mSv). Logistic regression revealed that tube voltage and current had the greatest impact on the detection rate, while nodule size and density had no significant influence. CONCLUSIONS: The optimal tube voltage/current combination proposed in this study (80 kV/50 mAs) is comparable to the proposed combinations in similar studies, which mostly dealt with conventional CAD software. Modification of tube voltage and tube current has a significant impact on the performance of DL-based CAD software in pulmonary nodule detection regardless of their size and composition. KEY POINTS: • Modification of tube voltage and tube current has a significant impact on the performance of deep learning-based CAD software. • Nodule size and composition have no significant impact on the software's performance. • The optimal tube voltage/current combination for the examined software is 80 kV/50 mAs.

Classification of X-Ray Attenuation Properties of Additive Manufacturing and 3D Printing Materials Using Computed Tomography From 70 to 140 kVp.

Additive manufacturing and 3D printing is particularly useful in the production of phantoms for medical imaging applications including determination and optimization of (diagnostic) image quality and dosimetry. Additive manufacturing allows the leap from simple slab and stylized to (pseudo)-anthropomorphic phantoms. This necessitates the use of materials with x-ray attenuation as close as possible to that of the tissues or organs mimicked. X-ray attenuation properties including their energy dependence were determined for 35 printing materials comprising photocured resins and thermoplastic polymers. Prior to measuring x-ray attenuation in CT from 70 to 140 kVp, printing parameters were thoroughly optimized to ensure maximum density avoiding too low attenuation due to microscopic or macroscopic voids. These optimized parameters are made available. CT scanning was performed in a water filled phantom to guarantee defined scan conditions and accurate HU value determination. The spectrum of HU values covered by polymers printed using fused deposition modeling reached from -258 to +1,063 at 120 kVp (-197 to +1,804 at 70 kVp, to -266 to +985 at 140 kVp, respectively). Photocured resins covered 43 to 175 HU at 120 kVp (16-156 at 70, and 57-178 at 140 kVp). At 120 kVp, ASA mimics water almost perfectly (+2 HU). HIPS (-40 HU) is found close to adipose tissue. In all photocurable resins, and 17 printing filaments HU values decreased with increasing beam hardness contrary to soft tissues except adipose tissue making it difficult to mimic water or average soft tissue in phantoms correctly over a range of energies with one single printing material. Filled filaments provided both, the HU range, and an appropriate energy dependence mimicking bone tissues. A filled material with almost constant HU values was identified potentially allowing mimicking soft tissues by reducing density using controlled under-filling. The measurements performed in this study can be used to design phantoms with a wide range of x-ray contrasts, and energy dependence of these contrasts by combining appropriate materials. Data provided on the energy dependence can also be used to correct contrast or contrast to noise ratios from phantom measurements to real tissue contrasts or CNRs.

Performance of an AI based CAD system in solid lung nodule detection on chest phantom radiographs compared to radiology residents and fellow radiologists.

BACKGROUND: Despite the decreasing relevance of chest radiography in lung cancer screening, chest radiography is still frequently applied to assess for lung nodules. The aim of the current study was to determine the accuracy of a commercial AI based CAD system for the detection of artificial lung nodules on chest radiograph phantoms and compare the performance to radiologists in training. METHODS: Sixty-one anthropomorphic lung phantoms were equipped with 140 randomly deployed artificial lung nodules (5, 8, 10, 12 mm). A random generator chose nodule size and distribution before a two-plane chest X-ray (CXR) of each phantom was performed. Seven blinded radiologists in training (2 fellows, 5 residents) with 2 to 5 years of experience in chest imaging read the CXRs on a PACS-workstation independently. Results of the software were recorded separately. McNemar test was used to compare each radiologist's results to the AI-computer-aided-diagnostic (CAD) software in a per-nodule and a per-phantom approach and Fleiss-Kappa was applied for inter-rater and intra-observer agreements. RESULTS: Five out of seven readers showed a significantly higher accuracy than the AI algorithm. The pooled accuracies of the radiologists in a nodule-based and a phantom-based approach were 0.59 and 0.82 respectively, whereas the AI-CAD showed accuracies of 0.47 and 0.67, respectively. Radiologists' average sensitivity for 10 and 12 mm nodules was 0.80 and dropped to 0.66 for 8 mm (P=0.04) and 0.14 for 5 mm nodules (P<0.001). The radiologists and the algorithm both demonstrated a significant higher sensitivity for peripheral compared to central nodules (0.66 vs. 0.48; P=0.004 and 0.64 vs. 0.094; P=0.025, respectively). Inter-rater agreements were moderate among the radiologists and between radiologists and AI-CAD software (K'=0.58±0.13 and 0.51±0.1). Intra-observer agreement was calculated for two readers and was almost perfect for the phantom-based (K'=0.85±0.05; K'=0.80±0.02); and substantial to almost perfect for the nodule-based approach (K'=0.83±0.02; K'=0.78±0.02). CONCLUSIONS: The AI based CAD system as a primary reader acts inferior to radiologists regarding lung nodule detection in chest phantoms. Chest radiography has reasonable accuracy in lung nodule detection if read by a radiologist alone and may be further optimized by an AI based CAD system as a second reader.

Dosimetry analysis of panoramic-imaging devices in different-sized phantoms.

The aim of this study is to measure the radiographic dose in adult, adolescent, and child head-sized PMMA phantoms for three panoramic-imaging devices: the panoramic mode on two CBCT machines (Carestream 9300 and i-CAT NG) and the Planmeca ProMax 2D. A SEDENTEXCT dose index adult phantom and custom-built adolescent and pediatric PMMA dosimetry phantoms were used. Panoramic radiographs were performed using a Planmeca ProMax 2D and the panoramic mode on a Carestream 9300 CBCT and an i-CAT NG using the protocols used clinically. Point dose measurements were performed at the center, around the periphery and on the surface of each phantom using a thimble ionization chamber. Five repeat measurements were taken at each location. For each machine, single-factor ANOVA was conducted to determine dose differences between protocols in each phantom, as well as determine the differences in absorbed dose when the same protocol was used for different-sized phantoms. For any individual phantom, using protocols with lower kVp, mA, or acquisition times resulted in statistically significant dose savings, as expected. When the same protocol was used for different-sized phantoms, the smaller phantom had a higher radiation dose due to less attenuation of x-rays by the smaller phantom and differences in the positioning of the ion chamber relative to the focal trough. The panoramic-mode on the CBCT machines produce images suitable for clinical use with similar dose levels to the stand-alone panoramic device. Significant dose savings may result by selecting age- and size- appropriate protocols for pediatric patients, but a wider range of protocols for children and adolescents may be beneficial.

Measuring absorbed dose for i-CAT CBCT examinations in child, adolescent and adult phantoms.

OBJECTIVES: Design and construct child and adolescent head phantoms to measure the absorbed doses imparted during dental CBCT and compare with the absorbed dose measured in an adult phantom. METHODS: A child phantom was developed to represent the smallest patients receiving CBCT, usually for craniofacial developmental concerns, and an adolescent phantom was developed to represent healthy orthodontic patients. Absorbed doses were measured using a thimble ionization chamber for the custom-built child and adolescent phantoms and compared with measurements using a commercially available adult phantom. Imaging was performed with an i-CAT Next Generation (Imaging Sciences International, Hatfield, PA) CBCT using two different fields of view covering the craniofacial complex (130 mm high) or maxilla/mandible (60 mm high). RESULTS: Measured absorbed doses varied depending on the location of the ionization chamber within the phantoms. For CBCT images obtained using the same protocol for all phantoms, the highest absorbed dose was measured in all locations of the small child phantom. The lowest absorbed dose was measured in the adult phantom. CONCLUSIONS: Images were obtained with the same protocol for the adult, adolescent and child phantoms. A consistent trend was observed with the highest absorbed dose being measured in the smallest phantom (child), while the lowest absorbed dose was measured in the largest phantom (adult). This study demonstrates the importance of child-sizing the dose by using dedicated paediatric protocols optimized for the imaging task, which is critical as children are more sensitive to harmful effects of radiation and have a longer life-span post-irradiation for radiation-induced symptoms to develop than do adults.