Journal of Medical Physics最新文献

Purpose: The purpose of the study was to quantify set-up errors and derive optimal clinical target volume to Planning Target Volume (PTV) margins for rectal cancer patients undergoing radiotherapy, while also evaluating the influence of body mass index (BMI) on set-up accuracy.

Materials and methods: Data from 41 patients and 1102 daily cone-beam computed tomography (CBCT) scans were analyzed. For each patient and fraction, the mediolateral (X), craniocaudal (Y), and antero-posterior (Z) translational shifts were measured. Population systematic (Σ) and random (σ) errors were calculated from per-patient summary statistics (see Methods), and PTV margins were derived using the van Herk formula (margin = 2.5 Σ +0.7 σ).

Results: The mean systematic set-up error was small: 0.04 mm (X), 0.57 mm (Y), and 0.13 mm (Z), reflecting high reproducibility of daily image-guided positioning. Using Σ and σ, the derived PTV margins were 7.4 mm (X), 9.1 mm (Y), and 9.7 mm (Z). A moderate positive correlation between BMI and the Z-axis set-up error was observed (r = 0.55, P = 0.002). Overall, 23.1% of fractions required corrections >5 mm, underlining the value of daily CBCT.

Conclusion: Nonuniform, axis-specific margins are essential to accommodate anatomical and physiological variability in rectal cancer radiotherapy. The use of daily CBCT significantly enhanced set-up precision. Findings align with ICRU 62/83 and QUANTEC recommendations and support individualized planning approaches, especially in diverse patient populations where BMI and pelvic anatomy may affect positioning accuracy.

The radioisotope lutetium-177 (¹⁷⁷Lu) has been proposed for the use of radioimmunotherapy in nuclear medicine. Currently, ¹⁷⁷Lu is primarily generated through neutron activation in nuclear reactors, whereas cyclotron-based production can also be explored. In this study, cyclotron located in Karaj with a deuteron energy of 15 MeV was considered to simulate the production of ¹⁷⁷Lu using Ytterbium-176 target. Here, two types of interaction cross-sections (d,p) and (d,x) were analyzed via Back-Shifted Fermi Gas Model (BSFM) by Talys code. In addition, the production yield of the two types of interactions mentioned at different energies was calculated. The Fermi gas model is an idealized system of non-interacting particles. In reality, however, effects such as Pauli blocking, hole heating, and two-body losses in molecular Fermi gases hinder cooling processes and reduce efficiency. The use of more complex nuclear level density prescriptions, which account for interactions, can improve agreement between calculations and reported data.

Aim: This study aimed to address critical gaps in magnetic resonance imaging (MRI) training among medical physicists in Türkiye by developing and evaluating a structured virtual course focused on foundational and clinically relevant MRI physics.

Materials and methods: A 9-week virtual course was developed through a collaboration between the Turkish Medical Physics Association and international MRI experts. The curriculum covered 13 core topics, including nuclear magnetic resonance, spatial encoding, MRI safety, and MR-guided radiotherapy. Live instruction was delivered in English by volunteer physicists and radiologists. A total of 160 medical physicists enrolled, and 95 completed a baseline survey assessing self-reported knowledge across all topics. Weekly postlecture evaluations were conducted. Knowledge gains were assessed using the Wilcoxon signed-rank test.

Results: Statistical analysis revealed significant improvements in self-reported knowledge across all 13 topics (P < 0.001). High rates of weekly engagement and positive feedback from participants further supported the course's relevance and accessibility.

Conclusions: This virtual course addressed critical training gaps in MRI physics among Türkiye's medical physics workforce. The initiative offers a scalable and adaptable model for professional development in contexts where access to imaging technology has outstripped training. By linking technical education to clinical application, the program reinforces the importance of human capital in realizing the full potential of MRI in therapeutic settings.

Purpose: The present study aimed to compare the dosimetric parameters for a fractional re-imaging approach using computed tomographic (CT) scans before the first and third fractions with hybrid brachytherapy for carcinoma cervix patients.

Materials and methods: Patients with cervical carcinoma who received radical chemoradiation and hybrid brachytherapy were prospectively included for the analysis. The first fraction was delivered on day 1 after applicator insertion and the second and third fractions were delivered on day 2, 6 h apart. Two plans were created for each patient using CT-based planning, one before the first fraction (Plan 1) and the other before the third fraction (Plan 2). The dose prescribed was 7 Gy per fraction to high-risk clinical target volume (HRCTV). The dosimetric parameters compared between the two plans were D90 HRCTV, D98 IRCTV, TRAK, D0.1cc, and D2cc to the bladder, rectum, sigmoid, and dose to the ICRU 89 radiation therapy (RV) point, respectively.

Results: Fifteen patients were included for the analysis. The mean HRCTV volume was 31 cc. The mean D90 HRCTV was 87.86 Gy EQD2 for Plan 1 versus 84.64 Gy EQD2 for Plan 2, respectively (P = 0.018). On analysis, there was a significant difference between the mean D0.1cc rectum between Plan 1 and Plan 2 (89.57 Gy vs. 115.27 Gy) and the D2cc of the rectum between Plan 1 and Plan 2 (70.9 Gy vs. 77.2 Gy). The bladder, sigmoid, and RV ICRU 89 point dose between the two plans were not significantly different.

Conclusion: In carcinoma cervix patients undergoing hybrid brachytherapy in a single application multiple fraction approach, there can be significant variations in rectum doses. These results indicate that fractional re-imaging is necessary to optimize the plan and spare organ-at-risk.

Aims: In this study, we aimed to optimize the window size of Lee filter for speckle noise reduction in breast ultrasound images (BUSIs) by evaluating the segmentation performance of benign tumors using a U-Net model.

Subjects and methods: Benign tumor images were acquired with added speckle noise (intensity = 0.05) to obtain the noisy images. A Lee filter was applied to noisy images with window sizes of 3 × 3, 5 × 5, 7 × 7, and 9 × 9. To evaluate noise reduction and segmentation performance, noisy and filtered images were used as input images for the U-Net model. The segmentation performance according to various window sizes of the Lee filter was quantitatively evaluated using intersection over union (IoU), peak signal to noise ratio (PSNR), and universal quality image index (UQI).

Results: As a result, a window size of 7 × 7 achieved the highest performance across all evaluation factors. In particular, when comparing the image with a 7 × 7 window size to the noisy image, the segmentation performance exhibited average improvements of approximately 19%, 5%, and 19% in IoU, PSNR, and UQI, respectively, with maximum improvements of approximately 27%, 7%, and 23%.

Conclusions: This study demonstrated that applying the Lee filter with an optimized 7 × 7 window size improved speckle noise reduction and segmentation accuracy in BUSIs, supporting the applicability of deep learning-based tumor segmentation in clinical practice.

Nowadays, the use of nanotechnology in cancer treatment offers exciting possibilities, including the enhanced delivery of doses inside the target, leading to a higher therapeutic ratio. The aim of this study is to review the dose enhancement factor (DEF) of various nanoparticles (NPs) in megavoltage radiotherapy. In vitro, Monte Carlo (MC) and experimental methods were employed in the reviewed studies, and the results indicated that higher DEF values were achieved through in vitro methods. According to the literature, smaller NPs tend to exhibit higher sensitization enhancement ratio (SER) in in vitro studies. In contrast, MC simulations report that NPs with higher concentrations, larger sizes, and higher atomic numbers led to greater DEF values. Smaller NPs penetrated tumors more effectively, whereas larger NPs had greater self-absorption of secondary electrons. It was also demonstrated that the increase in size may also result in higher toxicity and accumulation of NPs in tissues. A suitable balance between therapeutic efficacy, minimal toxicity, and uptake-related issues should be considered. Among the conducted studies, spherical and rod-shaped NPs have been more widely used. In addition, gold (Au) NPs in the size of 100 nm and concentration of 6 mM irradiating by 6 MeV photon beams have shown higher DEF values compared to other NPs in both MC and experimental methods (the maximum DEF reported was 4.71, representing a 53% increase for a 6 mM gel). Meanwhile, bismuth oxide (Bi₂O₃) NPs demonstrated the highest SER in in vitro studies, with a maximum SER of 7.64 for 90 nm particles at a concentration of 0.05 µM.

Aim: This article describes the commissioning of a total body irradiation (TBI) technique using bilateral-parallel-opposed fields at extended source-to-surface distance (SSD).

Material: The measurements are based on the actual patient treatment geometry, which requires the patient to be placed inside a Perspex box. The gaps between the patient and the walls of the Perspex box were filled with rice bags to achieve full scattering conditions. The TBI box's lateral separation can be adjusted to 42 cm, 52 cm, or 62 cm by inserting the removable side walls to either of the three pairs of slots. A Farmer chamber, inserted inside a water-equivalent plastic slab phantom placed under full scattering conditions, was used for depth dose and profile measurements. The extended SSD was 333.5 cm, and the available field size was 132 cm × 132 cm. For output measurement, dose-to-water calibration factors for 6 MV and 15 MV energies were derived for the extended SSD. Bilateral-opposed fields were measured at three different separations to calculate lateral tissue effects.

Result: For the 6 MV and 15 MV beams at a 42 cm separation, the midline-to-surface dose ratios were 1:1.17 and 1:1.08, respectively. As the separation increased, this ratio increased faster for the 6 MV beam and slower for the 15 MV beam. For the end-to-end quality assurance test (monitor unit to dose verification), the noted deviations were - 2.16% for the 6 MV beam and - 1.27% for the 15 MV beam.

Conclusion: This article presents the detailed commissioning and long-term stability of the extended SSD TBI technique.

Purpose: In markerless tumor tracking (MTT) with deep learning, model performance suffers from domain shifts due to noise and anatomical changes. This study aimed to develop a convolutional neural network (CNN) model for real-time MTT segmentation.

Methods: An Uncertain Feature-refinement Attention Unet (UFA-Unet), designed based on insights into CNN behavior under domain distribution shifts that occur between digitally reconstructed radiographs (DRRs) and kV X-ray fluoroscopic (XF) images, is proposed. A qualitative ablation study was performed to examine the contribution of each UFA-Unet component to segmentation accuracy. The model feasibility of UFA-Unet was evaluated through quantitative and phantom studies. The quantitative study included ten lung cancer cases, each containing two datasets (1^st-plan and 2^nd-plan), with a mean interval of 28 days between four-dimensional computed tomography (4DCT) acquisitions. Patient-specific models were trained on 1^st-plan DRRs and validated using noise-injected 1^st-and 2^nd-plan DRRs. In the phantom study, UFA-Unet was trained with only a single exhalation phase (T50) of 4DCT data and evaluated using dynamic phantom XF images with 25-mm amplitude motion. UFA-Unet was compared against U-Net, Attention-Unet, and Swin-Unet.

Results: The ablation study confirmed that each component suppressed over-activation to improve segmentation accuracy. In the quantitative study, UFA-Unet maintained superior performance compared with conventional models on both 1^st- and 2^nd-plan DRRs with noise injection. Furthermore, in the phantom study, UFA-Unet demonstrated robust tracking under previously unseen respiratory phases, achieving a 95^th percentile 3D error of 0.61-3.13 mm and consistently outperforming conventional models.

Conclusion: UFA-Unet provides accurate, robust, and real-time segmentation, thus demonstrating its suitability for clinical MTT.

Purpose: Manual delineation of pelvic organs remains a critical bottleneck in radiotherapy planning, consuming valuable clinical time and introducing interobserver variability. While existing deep learning approaches have shown promise, they struggle with complex anatomical boundaries and lack seamless integration into clinical workflows. This study addresses these limitations by introducing Dual-Attention Residual Technology Network (DART-Net), the first framework to unify dual-encoder architecture, attention mechanisms, and residual connections for automated segmentation and direct RTSTRUCT generation.

Methods: Our novel architecture processes both raw and normalized computed tomography (CT) volumes simultaneously through parallel encoders, enabling superior feature extraction. The integration of attention mechanisms with residual connections, a combination previously unexplored in pelvic segmentation, allows precise focus on clinically relevant regions when preserving gradient flow. The model was trained on 125 expert-annotated pelvic CT scans with comprehensive data augmentation to ensure anatomical variability representation.

Results: DART-Net achieved state-of-the-art performance with significant improvements over existing methods: Dice Similarity Coefficient scores of 0.965 for bladder (vs. 0.94 with prior methods), 0.908 for prostate, and 0.840 for rectum, with Hausdorff Distance values between 1.4 and 2.5 mm, representing an 18.6% reduction in bladder boundary error compared to the best previous approach. Expert validation by radiologists and radiation oncologists confirmed clinical acceptability of the auto-generated contours. The model uniquely automated bridges the research-practice gap through RTSTRUCT file generation, enabling seamless integration with commercial treatment planning systems.

Conclusion: DART-Net establishes a new benchmark for artificial intelligence-assisted radiotherapy planning by harmonizing technical innovation with clinical practicality. By reducing contouring time and improving anatomical precision, this framework addresses critical workflow inefficiencies in radiation oncology when potentially enhancing treatment outcomes through more consistent organ delineation.

Purpose: Ionizing radiation (IR) is crucial for diagnostic imaging and cancer treatment, but it also causes significant biological damage by generating reactive oxygen species (ROS). These highly reactive molecules damage important biomolecules, such as deoxyribonucleic acid (DNA), lipids, and proteins, which causes oxidative stress, changes in structure, and loss of function. To create ways to protect people from and treat radiation damage, and need to know how these systems work.

Materials and methods: In this study, irradiated cell samples were analyzed to evaluate oxidative modifications among various biomolecular classes. Investigated DNA oxidative lesions, lipid peroxidation products, protein carbonylation, and sulfur oxidation in cysteine and methionine residues. Utilized specific chemical probes (Diphenyl-1-pyrenylphosphine, 2.4-Dinitrophenylhydrazine, Girard's P, dimedone, and Alk-B-Ke) to label oxidative markers. Employed liquid chromatography-tandem mass spectrometry (LC-MS) and LC-MS/MS for quantitative and structural analysis.

Results: The findings indicated significant elevations in oxidative biomarkers following radiation exposure: DNA lesions (~35%), lipid peroxidation (~25%), protein carbonyls (~40%), cysteine/methionine oxidation (~30%), and ROS levels (~50%). These results corroborate that IR elicits extensive oxidative stress at the molecular scale. This thorough procedure demonstrates how chemical probes and LC-MS/MS can detect oxidative biomarkers with high sensitivity.

Conclusions: The study promotes the development of more effective radioprotective and therapeutic strategies in radiation medicine, establishing a foundation for identifying radiation biomarkers.