Journal of Medical Physics最新文献

Background and aim: Cephalometry is usually carried out to assess the type and severity of malocclusion, while the role of cephalometrics in the field of forensic sciences has not been explored until recently. The aim of the present study was to evaluate the role of lateral cephalograms as valuable antemortem tools during the forensic identification of individuals during mass disasters.

Materials and methods: The present retrospective study included analysis of the archival records of 120 subjects who had attained complete skeletal growth at the time of initiation of the orthodontic treatment, while their pre- and posttreatment cephalometric records were analyzed to assess variations, if any, in terms of certain defined morphological landmarks, and the data obtained were subjected to statistical analysis.

Results: The results obtained in the present study revealed no statistical significance in terms of the variations observed for the included reference planes in either case of skull base and/or maxilla and mandible (P > 0.05), revealing the relatively stable nature of the selected planes and reference lines holding high clinical significance to be used for forensic applications during disaster victim identification.

Conclusions: The findings of the present study, with plausible explanations, come with practical implications involving distinct landmark identification on lateral cephalograms as a potential forensic tool in the personal identification of individuals during mass disasters while also highlighting the fact that combination of other methods along with lateral cephalograms can provide much precision and accuracy during the forensic identification of individuals in case of mass disasters.

Purpose: Our aim is to compare treatment plans with vaginal markers used in radiotherapy target volume definition with those without markers, which we have not encountered in our literature searches, and to investigate whether patients with endometrial cancer (EC) can undergo treatment with a single simulation tomography.

Materials and methods: A total of twenty patients with EC were randomly selected. All patients underwent a computerized tomography in the supine position. During the scan, the arms were folded over the chest, and the feet were fixed with a foot immobilizer. Tomography scans were performed with a vaginal marker. A total of four treatment plans were made by a medical physicist using tomography images with both vaginal markers (CT Set 1) and 0 Hounsfield units assigned to the vaginal marker (CT Set 2) under the same conditions.

Results: The mean doses of maximum and mean of planning target volume (PTV) and critical organs were derived from the data obtained from the treatment plans created using CT Set 1 and CT Set 2. No significant differences were observed regarding PTV or critical organs when comparing the plans of CT Set 1 and CT Set 2 in both Treatment Planning Systems.

Conclusions: Dosimetric comparison of plans with and without vaginal markers, no difference was found in terms of dosimetric parameters. Therefore, we suggested that a clinical decision can be made to plan with vaginal markers, thus ensuring that the patient does not receive an unnecessary second tomography dose.

Research on acoustic signals generated by the human body remains limited but holds significant potential for advancing medical diagnostics. This review comprehensively explores various body sounds, including cardiovascular sounds, bowel sounds, brain sounds, musculoskeletal sounds, respiratory and lung sounds, and swallowing sounds. For each category, the review examines the origin, frequency range, acquisition techniques, transducers, and signal processing methods. Special emphasis is placed on the diagnostic applications of these sounds, highlighting their clinical relevance. Recent advancements in medical technology, particularly digital auscultation and machine learning algorithms, are revolutionizing the analysis of body sounds, offering new tools for accurate and efficient diagnostics. This review provides a thorough overview of current research on body sound acquisition and processing methods, underscores their significance in medical practice, and identifies existing knowledge gaps and future research opportunities.

Aim: This study evaluated the feasibility of reusing thermoplastic masks in radiation oncology to reduce environmental waste and financial costs.

Materials and methods: The study conducted from July 2024 to December 2024 involved 70 thermoplastic masks across three types: 3-clamp brain masks, 5-clamp head-and-neck masks, and 4-clamp chest/abdomen/pelvis masks. Measurements of fresh masks were compared to those after molding and re-flattening post-treatment. Dimensions were recorded in superior-inferior (SI) and lateral directions at specific anatomical levels.

Results: The 3-clamp brain thermoplastic cast expanded 46% ± 7% in SI directions postmolding. Width increased by 63% ± 16% (chin) and 75% ± 10% (forehead). Re-flattened casts showed 25% ± 4% SI reduction and 19% ± 8% (chin) and 26% ± 5% (forehead) contraction in width. The 5-clamp head-and-neck thermoplastic cast expanded 16% ± 3% SI postmolding, with width expansion of 54% ± 15% (chest), 62% ± 10% (forehead), and 66% ± 5% (chin). Re-flattened casts showed 6% ± 3% SI expansion and 26% ± 5.5%, 40% ± 16%, and 37% ± 17% width enlargement at chest, chin, and forehead levels, respectively. The 4-clamp chest/abdomen/pelvis thermoplastic casts expanded 3% ± 5% (SI) in 26 casts and contracted by 7.5% ± 2.5% in 2 casts postmolding.

Conclusion: Thermoplastic masks demonstrated acceptable dimensional changes while reusing, with consistent structural integrity and minimal compromise in functionality. Single reuse could significantly reduce costs and waste, particularly in resource-limited settings, making this practice a viable option for sustainable health care.

Proton imaging can provide better density resolution and thus higher tissue contrast and does not suffer from artifacts due to beam hardening typically affecting X-ray imaging. Proton radiography generates two-dimensional projection images of an object and has applications in patient alignment and verification procedures in preparation for proton beam radiation therapy. Proton radiography enables fast and effective high-precision lateral alignment of the proton beam and target volume in the patient's body irradiation experiments with limited dose exposure. Our study has clearly demonstrated the potential of a proton microscope for imaging because a proton microscope compensates for image blur using magnetic lenses to the sub-mm level. This innovative work applies physical models of proton transport (including Beth-Bloch energy dissipation, cutoff energy, and Coulomb multiple scattering) to simulate the theory of proton imaging of various human tissues using a proton microscope and study image resolution using Maple software. Our obtained results from this work are given below. (i) To determine the appropriate dose without risks, researchers in the domains of nuclear medicine and radiotherapy must examine the parameters of proton interactions with different tissues. (ii) The current investigation is highly important in the proton radiotherapy and radiography of different human tissues. (iii) The cortical bone has the highest Z_eff values, while the adipose tissue has the lowest values at all proton energies. (vi) CSDA range of protons was higher in high-density tissue until 200 MeV energy protons. (v) Total stopping power of a proton is directly proportional to the energy of protons. (iv) The blurring coefficient, ξ_y(γ), increases with decreasing A.

Background: Images from onboard electronic portal imaging devices (EPID) contain dose information that can be converted into dose maps. A cycle-consistent generative adversarial network (CycleGAN)-based model was developed for two-dimensional (2D) EPID dosimetry, and the dose characteristics of the model were evaluated carefully.

Materials and methods: All the measurements were done on a linac equipped with an EPID detector. This experiment involved: (1) assessing the dose characteristics of the EPID and (2) using a commercial treatment planning system to calculate dose distributions in a slab phantom, which were taken as the ground truth in CycleGAN-based models for converting the EPID images to 2D dose maps. There were about 780 beams delivered to EPID through a slab phantom. There were two normalization methods (NM): I: based on the highest possible value: 65,535 (16 bit); II: by its own maximum pixel value. To evaluate the model, gamma analyses between the ground truth and the output were performed with in-house software; and the dose linearity of the model was checked carefully. A comparative analysis was conducted to evaluate the outcomes stemming from two distinct NMs applied to the input data.

Results: The dose characteristics of the EPID demonstrated exceptional precision. Notably, the beam output factors exhibited considerable variations with the increasing thickness of the phantom. Specifically, when the phantom thickness surpassed 12 cm, the trend lines exhibited a pronounced linearity. Deep learning models efficiently transformed EPID images into planar dose maps, albeit exhibiting dose nonlinearity that could be mitigated by choose the suitable normalization medthods. The mean pass rates of gamma analyses (3 mm, 3%) of data normalized by the way I or II were 85.5%, 97.9%, respectively.

Conclusion: EPID is an excellent flat-panel detector that captures images rich in dose information, which can be effectively transformed into precise planar dose maps using CycleGAN-based models. The trained model could be used in the quality assurance of treatment plans.

In this study, we used Geant4 Monte Carlo simulations to explore how different two-layer combinations of moderator materials can improve the performance of accelerator-based boron neutron capture therapy (BNCT). We tested 16 pairings of aluminum oxide (Al₂O₃), titanium(III) fluoride, lithium bromide (LiBr), and lithium carbonate to see how each affected neutron and gamma radiation levels-both at the beam exit and within a simulated tumor. To evaluate their performance, we applied a weighted scoring system that considered both treatment effectiveness and patient safety. Our results showed that a dual-layer configuration of LiBr (Configuration N) delivered the highest thermal neutron dose to the tumor, making it the most effective for treatment. On the other hand, a double layer of Al₂O₃ (configuration P) excelled in minimizing harmful radiation outside the tumor area. Some setups, like LiBr + Al₂O₃ (configuration G), struck a good balance between efficacy and safety. These insights can help guide the development of more efficient and safer beam-shaping assemblies for clinical BNCT applications.

Purpose: This study presents a deep learning framework for automatic parotid segmentation using three-dimensional (3D) U-Net and attention-augmented 3D U-Net architectures trained with a novel combined loss function tailored for anatomical accuracy and class imbalance.

Materials and methods: A curated dataset of 379 noncontrast head-and-neck computed tomography scans with expert-verified contours was used. Two architectures a residual 3D U-Net and its attention-enhanced variant were implemented using TensorFlow. The networks were trained with both categorical cross-entropy and a proposed combined loss integrating modified Dice Score Coefficient (mDSC) and focal loss (FL) with weights 0.7 and 0.3. The models were evaluated using dice similarity coefficient (DSC), Intersection over Union (IoU), and categorical accuracy. A custom checkpointing strategy was designed to preserve model weights corresponding to both peak DSC and minimum validation loss. The code and pretrained models are hosted on a publicly available GitHub repository at: https://github.com/1aryantyagi/Segmentation-Paper.

Results: The 3D U-Net trained with the combined loss achieved a median Dice score of 0.8835 (left parotid) and 0.8709 (right), with mean IoU values of 0.7672 and 0.7358, indicating strong segmentation accuracy. The U-Net produced comparable results, supporting the combined loss's consistency. Bland-Altman analysis confirmed reduced variability and improved agreement.

Conclusion: The integration of mDSC and FL within a 3D U-Net architecture significantly improves segmentation performance, robustness, and spatial precision. These findings support the clinical feasibility of the proposed framework for automated, reproducible parotid delineation in radiotherapy planning.

Background and purpose: This study aimed to compare dose gradients and peripheral dose spillage across three advanced radiotherapy modalities: C-Arm Linac (TrueBeam STx), Ring Gantry (Radixact^®), and Robotic Arm Linac (CyberKnife^®). The focus was on evaluating their performance in delivering steep dose gradients and minimizing low-dose spillage, which is crucial for reducing toxicities and optimizing treatment outcomes.

Materials and methods: A retrospective analysis of 110 patients with multi-lesion brain tumors treated with stereotactic radiotherapy was conducted. Patients were grouped by target volume size, and treatment plans were created using the three modalities. Dosimetric parameters, including gradient index (GI5-GI50), dose conformity, and homogeneity, were analyzed following ICRU 91 guidelines. Phantom verification using the Rando Phantom and PTW SRS1600 detector ensured clinical reliability.

Results: The Robotic Arm Linac demonstrated the steepest dose gradients and lowest GI values at GI10 and GI5, highlighting superior precision and minimal low-dose spillage (P < 0.05). The C-Arm Linac and ring gantry showed comparable performance at higher GI values (GI20-GI50), with the ring gantry achieving broader dose coverage for larger targets. Phantom validation supported these findings, confirming modality-specific advantages.

Conclusion: Robotic Arm Linac is optimal for precise treatments with stringent organ-at-risk sparing, while C-Arm Linac and ring gantry are better suited for broader dose coverage in complex cases. These results provide guidance for modality selection based on tumor size and clinical needs, with potential for further optimization through artificial intelligence and advanced planning tools.