Search Results (7)

Purpose: The present study aimed to assess the radiation dose associated with low-dose (LD) CT pelvimetry compared with conventional radiography and to evaluate the adequacy of the resulting image quality. Methods: The absorbed dose was measured using thermoluminescent dosimeters positioned in an anthropomorphic female phantom, including uterine locations, to estimate the fetal dose. Conventional radiographic pelvimetry and LD-CT pelvimetry were performed using clinically implemented protocols. Effective dose was calculated using Monte Carlo–based modeling applying acquisition parameters and retrospective patient dose registry data. Image quality of LD-CT pelvimetry was independently evaluated in 14 consecutive clinical cases using a four-point ordinal scale. Results: LD-CT pelvimetry reduced the mean absorbed pelvic dose by approximately 50% compared with conventional pelvimetry (0.18 vs. 0.39 mGy) and decreased estimated fetal dose by 40% (0.21 vs. 0.37 mGy). These estimates were based on standardized single acquisitions and did not incorporate additional radiation from retakes commonly observed in conventional practice. CT demonstrated substantially more homogeneous dose distribution, whereas conventional pelvimetry exhibited marked heterogeneity with peak values up to 2.3 mGy. The maternal effective dose was lower for LD-CT (0.16 mSv) than for conventional pelvimetry (0.36 mSv); inclusion of retakes increased the conventional effective dose to 0.71 mSv. All CT examinations were diagnostically adequate, and no recalls were required. Conclusions: Optimized low-dose CT pelvimetry significantly reduces radiation dose compared with conventional radiographic pelvimetry while maintaining reliable diagnostic image quality. These results support the clinical adoption of CT-based pelvimetry as a dose-efficient and reproducible alternative to conventional techniques. Full article

Background/Objectives: Accurate patient-specific dosimetry is essential for optimizing radiopharmaceutical therapy (RPT), but current tools lack validation in clinically realistic conditions. This work aimed to develop a workflow for designing and fabricating patient-derived, organ-realistic RPT phantoms and evaluate their feasibility for commissioning patient-specific RPT radioactivity quantification. Methods: We used computed tomographic (CT) and magnetic resonance (MR) imaging of representative patients, computer-aided design, and in-house 3D printing technology to design and fabricate anthropomorphic kidney and parotid phantoms with realistic organ spacing, anatomically correct orientation, and surrounding tissue heterogeneities. We evaluated the fabrication process via geometric verification (i.e., volume comparisons) and leak testing (i.e., dye penetration tests). Clinical feasibility testing involved injecting known radioactivities of ¹⁷⁷Lu-PSMA-617 into the parotid and kidney cortex phantom chambers and acquiring SPECT/CT images. MIM SurePlan MRT SPECTRA Quant software (v7.1.2) reconstructed the acquired SPECT projections into a quantitative SPECT image and we evaluated the accuracy by region-based comparison to the known injected radioactivities and determined recovery coefficients for each organ phantom. Results: Phantom fabrication costs totaled < USD 250 and required <84 h. Geometric verification showed a slight systematic expansion (<10%) from the representative patient anatomy and leak testing confirmed watertightness of fillable chambers. Quantitative SPECT imaging systematically underestimated the injected radioactivity (mean error: −17.0 MBq; −13.2%) with recovery coefficients ranging from 0.82 to 0.93 that were negatively correlated with the surface-area-to-volume ratio. Conclusions: Patient-derived, 3D-printed fillable phantoms are a feasible, cost-effective tool to support commissioning and quality assurance for patient-specific RPT dosimetry. The results of this work will support other centers and clinics implementing patient-specific RPT dosimetry by providing the tools needed to comprehensively evaluate accuracy in clinically relevant geometries. Looking forward, widespread accurate patient-specific RPT dosimetry will improve our understanding of RPT dose response and enable personalized RPT dosing to optimize patient outcomes. Full article

This study compares dosimetric approaches for lung dosimetry in ¹⁶⁶ radioembolization (Ho-TARE) with direct Monte Carlo (MC) simulations on a voxelized anthropomorphic phantom derived from a real patient’s CT scan, preserving the patient’s lung density distribution. Lung dosimetry was assessed for five lung shunt (LS) scenarios with conventional methods: the mono-compartmental organ-level approach (MIRD), voxel S-value convolution for soft tissue (kST, ICRU soft tissue with 1.04 g/cm³) and lung tissue (kLT, ICRU lung tissue with 0.296 g/cm³), local density rescaling (kST_L and kLT_L, respectively, for soft tissue and lung tissue), or global rescaling for a lung mean density of 0.221 g/cm³ (kLT₂₂₁). Significant underestimations in the mean absorbed dose (AD) were observed, with relative differences with respect to the reference (MC) of −64% for MIRD, −93% for kST, −56% for kST_L, −76% for kLT, −68% for kLT₂₂₁, and −60% for kLT_L. Given the high heterogeneity of lung tissue, standard dosimetric approaches cannot accurately estimate the AD. Additionally, MC results for ¹⁶⁶Ho showed notable spatial absorbed dose inhomogeneity, highlighting the need for tailored lung dosimetry in Ho-TARE accounting for the patient-specific lung density distribution. MC-based dosimetry thus proves to be essential for safe and effective radioembolization treatment planning in the presence of LS. Full article

This study compares various methodologies for lung dosimetry in radioembolization using Monte Carlo (MC) simulations. A voxelized anthropomorphic phantom, created from a real patient’s CT scan, preserved the actual density distribution of the lungs. Lung dosimetry was evaluated for five lung-shunt (LS) cases using traditional methods: the mono-compartmental organ-level approach (MIRD), local energy deposition (LED), and convolution with voxel S-values, either with local density corrections (SVOX_L) or without (SVOX_ST). Additionally, a novel voxel S-value (VSV) kernel for lung tissue with an ICRU density of $0.296\mathrm{g}/\mathrm{c}{\mathrm{m}}^3$ was developed. Calculations were performed using either the ICRU lung density (Lung_296), the average lung density of the phantom (Lung_221), or the local density (Lung_L). The comparison revealed significant underestimations in the mean absorbed dose (AD) for the classical approaches: approximately −$40\%$ for MIRD, −$27\%$ for LED, −$28\%$ for SVOX_L, and −$88\%$ for SVOX_ST. Similarly, calculations with the lung VSV kernel showed underestimations of about −$62\%$ for Lung_296, −$50\%$ for Lung_221, and −$35\%$ for Lung_L. Given the high heterogeneity of lung tissue, traditional dosimetric methods fail to provide accurate estimates of the mean AD for the lungs. Therefore, MC dosimetry based on patient images is recommended as the preferred method for precise assessment of lung AD during radioembolization. Full article

Background: Accurate dosimetry is crucial in radiotherapy to ensure optimal radiation dose delivery to the tumor while sparing healthy tissues. Traditional dosimetry techniques using homogeneous phantoms may not accurately represent the complex anatomical variations in cervical cancer patients, highlighting the need to compare dosimetry results obtained from different phantom models. Purpose: The aim of this study is to design and evaluate an anthropomorphic heterogeneous female pelvic (AHFP) phantom for radiotherapy quality assurance in cervical cancer treatment. Materials and method: Thirty RapidArc plans designed for cervical cancer patients were exported to both the RW3 homogeneous phantom and the anthropomorphic heterogeneous pelvic phantom. Dose calculations were performed using the anisotropic analytic algorithm (AAA), and the plans were delivered using a linear accelerator (LA). Dose measurements were obtained using a 0.6 cc ion chamber. The percentage (%) variation between planned and measured doses was calculated and analyzed. Additionally, relative dosimetry was performed for various target locations using RapidArc and IMRT treatment techniques. The AHFP phantom demonstrated excellent agreement between measured and expected dose distributions, making it a reliable quality assurance tool in radiotherapy. Results: The results reveal that the percentage variation between planned and measured doses for all RapidArc quality assurance (QA) plans using the AHFP phantom is 10.67% (maximum value), 2.31% (minimum value), and 6.89% (average value), with a standard deviation (SD) of 2.565 (t = 3.21604, p = 0.001063). Also, for the percentage of variation between homogeneous and AHFP phantoms, the t-value is −11.17016 and the p-value is <0.00001. The result is thus significant at p < 0.05. We can see that the outcomes differ significantly due to the influence of heterogeneous media. Also, the average gamma values in RapidArc plans are 0.29, 0.32, and 0.35 (g ≤ 1) and IMRT plans are 0.45, 0.44, and 0.42 (g ≤ 1) for targets 1, 2, and 3, respectively. Conclusion: The AHFP phantom results show more dose variability than homogenous phantom outcomes. Also, the AHFP phantom was found to be suitable for QA evaluation. Full article

We propose a novel framework for determining radiomics feature robustness by considering the effects of both biological and noise signals. This framework is preliminarily tested in a study predicting the epidermal growth factor receptor (EGFR) mutation status in non-small cell lung cancer (NSCLC) patients. Pairs of CT images (baseline, 3-week post therapy) of 46 NSCLC patients with known EGFR mutation status were collected and a FDA-customized anthropomorphic thoracic phantom was scanned on two vendors’ scanners at four different tube currents. Delta radiomics features were extracted from the NSCLC patient CTs and reproducible, non-redundant, and informative features were identified. The feature value differences between EGFR mutant and EGFR wildtype patients were quantitatively measured as the biological signal. Similarly, radiomics features were extracted from the phantom CTs. A pairwise comparison between settings resulted in a feature value difference that was quantitatively measured as the noise signal. Biological signals were compared to noise signals at each setting to determine if the distributions were significantly different by two-sample t-test, and thus robust. Four optimal features were selected to predict EGFR mutation status, Tumor-Mass, Sigmoid-Offset-Mean, Gabor-Energy and DWT-Energy, which quantified tumor mass, tumor-parenchyma density transition at boundary, line-like pattern inside tumor and intratumoral heterogeneity, respectively. The first three variables showed robustness across the majority of studied CT acquisition parameters. The textual feature DWT-Energy was less robust. The proposed framework was able to determine robustness of radiomics features at specific settings by comparing biological signal to noise signal. Identification of robust radiomics features may improve the generalizability of radiomics models in future studies. Full article

The aim of this work was to develop multimodal anthropomorphic breast phantoms suitable for evaluating the imaging performance of a recently-introduced Microwave Imaging (MWI) technique in comparison to the established diagnostic imaging modalities of Magnetic Resonance Imaging (MRI), Ultrasound (US), mammography and Computed Tomography (CT). MWI is an emerging technique with significant potential to supplement established imaging techniques to improve diagnostic confidence for breast cancer detection. To date, numerical simulations have been used to assess the different MWI scanning and image reconstruction algorithms in current use, while only a few clinical trials have been conducted. To bridge the gap between the numerical simulation environment and a more realistic diagnostic scenario, anthropomorphic phantoms which mimic breast tissues in terms of their heterogeneity, anatomy, morphology, and mechanical and dielectric characteristics, may be used. Key in this regard is achieving realism in the imaging appearance of the different healthy and pathologic tissue types for each of the modalities, taking into consideration the differing imaging and contrast mechanisms for each modality. Suitable phantoms can thus be used by radiologists to correlate image findings between the emerging MWI technique and the more familiar images generated by the conventional modalities. Two phantoms were developed in this study, representing difficult-to-image and easy-to-image patients: the former contained a complex boundary between the mammary fat and fibroglandular tissues, extracted from real patient MRI datasets, while the latter contained a simpler and less morphologically accurate interface. Both phantoms were otherwise identical, with tissue-mimicking materials (TMMs) developed to mimic skin, subcutaneous fat, fibroglandular tissue, tumor and pectoral muscle. The phantoms’ construction used non-toxic materials, and they were inexpensive and relatively easy to manufacture. Both phantoms were scanned using conventional modalities (MRI, US, mammography and CT) and a recently introduced MWI radar detection procedure called in-coherent Multiple Signal Classification (I-MUSIC). Clinically realistic artifact-free images of the anthropomorphic breast phantoms were obtained using the conventional imaging techniques as well as the emerging technique of MWI. Full article