Physica Medica-European Journal of Medical Physics最新文献

Purpose: To present an open access platform for CT image quality assessment combined with anthropomorphic phantoms. To promote the accessibility of anthropomorphic phantoms, including 3D-printed affordable ones, in regular QC analysis of CT images reconstructed with filtered back projection (FBP), iterative (IR) and Deep-Learning (DLR) reconstruction algorithms.

Methods: The Python-based platform CT-DImQ enables the advanced evaluation of CT values distribution through histogram analysis and noise characterization (noise maps and noise power spectrum (NPS)). The interactive GUIs allow the user to perform the CT data analysis adapting the output to their own requirements. The use case for CT-DImQ was CT anthropomorphic phantom data (thorax phantom with 3D-printed TPU lung vessel trees and PA-12 nodules) obtained under clinical conditions and two dose levels reconstructed with FBP, IR and DLR.

Results: Histograms showed in general an underestimation of CT-values for the 3D printed materials and a overestimation for Teflon (spine surrogate), in DLR images, while air and PMMA-soft tissue were the same as with IR and FBP. Noise maps showed a strong noise reduction in uniform phantom parts, and to a lesser extent in vessel and nodules edges. NPS showed noise reduction with increasing dose, especially with FBP and iterative reconstruction, and differences in the noise reduction frequency dependence among reconstruction methods, which was corroborated visually by the more uniform appearance of vessels and nodules in DLR images.

Conclusion: CT-DImQ combined with anthropomorphic phantoms represents a step towards advanced image quality evaluation in CT, closer to the clinical reality with patients and able to evaluate advanced reconstruction methods trained with patient data.

Purpose: To compare radiation doses and image quality from pediatric head CT examinations performed at a children's hospital and general regional hospitals, and to propose local diagnostic reference levels (LDRLs).

Methods: Data were retrospectively collected from five scanners in three hospitals; only routine non-specialized examinations were included. Patients were grouped by age: <1, 1-5, 6-10, and 11-15 years. LDRLs were determined as the 75th percentile of the pooled dose indices distributions and modeled as continuous functions of patients' AP thickness and age using quantile regression. Samples of 25 studies from each scanner were randomly selected and blinded for qualitative and quantitative image quality analysis. Differences in radiation doses between scanners were assessed using the Kruskal-Wallis test. Factors potentially affecting image quality were evaluated using the Pearson correlation coefficient.

Results: The dose survey included 358 studies, of which 125 were assessed for image quality. LDRLs were established as discrete quantities in each age group and modeled as an exponential-type function. Differences in dose indices between scanners were statistically significant (p-value <0.05) for all age categories. Pearson correlation coefficients demonstrated a weak correlation between radiation dose and the image quality total score across all scanners.

Conclusion: Significant dose variability was observed among scanners, with only a weak association between radiation dose and image quality. Examinations at a specialized pediatric facility resulted in higher radiation doses compared to general hospitals in the province. The derived LDRLs provide a useful benchmark for optimizing pediatric head CT and standardizing imaging across hospitals.

Currently the two radiopharmaceuticals approved by the AEMPS that include ¹⁷⁷Lu are [¹⁷⁷Lu]Lu-DOTA-TATE for the treatment of neuroendocrine tumours and [¹⁷⁷Lu]Lu-PSMA-617 for the treatment of mCRPC. The RD601/2019 states that for all medical exposure of patients for radiotherapeutic purposes, exposures of target volumes shall be individually planned and their delivery appropriately verified taking into account that doses to non-target volumes and tissues shall be as low as reasonably achievable and consistent with the intended radiotherapeutic purpose of the exposure. However, for both radiopharmaceuticals the treatment is performed by administering cycles of 7.4 GBq of the radiopharmaceutical - 4 cycles for [¹⁷⁷Lu]Lu-DOTA-TATE and 6 cycles for [¹⁷⁷Lu]Lu-PSMA-617. This implies that treatments are not planned in advance, but can at least be verified by dosimetry. The purpose of the present document is to provide recommendations for the dosimetry of patients in treatments with the aforementioned radiopharmaceuticals.

Background: Radiomics has emerged as a promising approach for predicting radiotherapy (RT)- induced xerostomia in head and neck cancer (HNC) patients, potentially enabling more personalized treatment strategies.

Methods: This systematic review was conducted in accordance with PRISMA guidelines and protocol registered in PROSPERO (CRD420251039081). A comprehensive search of PubMed, Scopus, Web of Science, and the Cochrane Library was done between January 2010 and April 2025. Also, quality assessment of included studies was performed using the radiomics quality score (RQS) tool.

Results: This systematic review included 32 eligible studies with 4,167 HNC patients. The RQS ranged from 7 to 33 out of 36, with a mean of 15.13 (42.0 %). A dose-dependent relationship between radiation dose and xerostomia severity was observed. V₃₀ > 50 % and V₄₀ > 60 % doses to the parotids were associated with moderate-to-severe xerostomia (Grades 2-3), affecting 50 % of patients in some studies. IMRT still resulted in moderate-to-severe xerostomia when these dose thresholds were exceeded. Additionally, submandibular glands were also critical, especially with V₄₀ > 60 % doses. Delta-radiomics refers to the analysis of changes in radiomic features over time, typically before and after treatment, to assess tissue response. Delta-radiomics outperformed static radiomics most of studies, particularly with MRI or MVCT. The highest performance was reported with an AUC of 0.97 for a CT + MRI ensemble machine learning model, and an R² of 0.98 for a delta-radiomics MRI model.

Conclusions: Radiomics-based models, particularly those using delta features and multimodal imaging, show high potential for accurate xerostomia prediction in HNC.