Volume 20 Issue 3

The purpose of this work was to create and assess a bio-based head phantom made from bio-based resources for external beam radiotherapy dose planning and delivery in brain cancer. The custom-made head phantom was fabricated using Rhizophora spp. bonded with soy flour and lignin, and its potential as phantom material was evaluated in previous studies. Organs at risk and planning target volume were identified using the treatment planning system, which was guided by computed tomography raw images. Thermoluminescent dosimeters were placed into specific holes positioned throughout the head phantom following individual calibration. Head phantom was imaged, planned and irradiated by linear accelerator. The planned predicted doses by treatment planning system at the targeted volume and the organ at risk regions were obtained and compared with the dosimeter doses. The result revealed that the planning target volume and organ at risks were within the dose range calculated by the treatment planning system, except for lens, optic chiasm and brainstem. Verification of the treatment plans was implemented, and good agreement between measured values and those predicted by the treatment planning system was found. The custom-made, bio-based phantom’s preliminary results have proved to be a valuable tool for the treatment dose verification, demonstrating its prospective as potential phantom material for use in radiotherapy.

To address the challenges posed by complex and variable backgrounds coupled with the small-target characteristics of wood surface defects such as knots and cracks, a novel wood surface defect detection model based on improved You Only Look Once version 8 (YOLOv8) is proposed. The model integrates a multi-head mixed self-attention mechanism into the backbone to improve the representation of fine-grained defect features. A learnable dynamic upsampling module replaces traditional nearest-neighbor interpolation to mitigate feature loss during resolution recovery. Additionally, a structural Re-parameterizable Block is adopted to enhance feature expressiveness during inference, and a small-object detection head is added to enhance the detection of small defects while minimizing both missed and incorrect detections. The experimental results demonstrate that the proposed model effectively enhances detection performance, increasing the mAP of the baseline model from 72.9% to 79.5%. Furthermore, the proposed model surpasses other YOLO variants in mAP across all defect categories. This improvement better meets the quality control requirements of wood processing and manufacturing, ensuring the quality of wood products.