An efficient rectangular optimization method for sparse orthogonal collimator based small animal irradiation

Robust Automated Mouse Micro-CT Segmentation Using Swin UNEt TRansformers

Image-guided mouse irradiation is essential to understand interventions involving radiation prior to human studies. Our objective is to employ Swin UNEt TRansformers (Swin UNETR) to segment native micro-CT and contrast-enhanced micro-CT scans and benchmark the results against 3D no-new-Net (nnU-Net). Swin UNETR reformulates mouse organ segmentation as a sequence-to-sequence prediction task using a hierarchical Swin Transformer encoder to extract features at five resolution levels, and it connects to a Fully Convolutional Neural Network (FCNN)-based decoder via skip connections. The models were trained and evaluated on open datasets, with data separation based on individual mice. Further evaluation on an external mouse dataset acquired on a different micro-CT with lower kVp and higher imaging noise was also employed to assess model robustness and generalizability. The results indicate that Swin UNETR consistently outperforms nnU-Net and AIMOS in terms of the average dice similarity coefficient (DSC) and the Hausdorff distance (HD95p), except in two mice for intestine contouring. This superior performance is especially evident in the external dataset, confirming the model's robustness to variations in imaging conditions, including noise and quality, and thereby positioning Swin UNETR as a highly generalizable and efficient tool for automated contouring in pre-clinical workflows.

Exploring Automated Contouring Across Institutional Boundaries: A Deep Learning Approach with Mouse Micro-CT Datasets

Image-guided mouse irradiation is essential to understand interventions involving radiation prior to human studies. Our objective is to employ Swin UNEt Transformers (Swin UNETR) to segment native micro-CT and contrast-enhanced micro-CT scans and benchmark the results against 3D no-new-Net (nnU-Net). Swin UNETR reformulates mouse organ segmentation as a sequence-to-sequence prediction task, using a hierarchical Swin Transformer encoder to extract features at 5 resolution levels, and connects to a Fully Convolutional Neural Network (FCNN)-based decoder via skip connections. The models were trained and evaluated on open datasets, with data separation based on individual mice. Further evaluation on an external mouse dataset acquired on a different micro-CT with lower kVp and higher imaging noise was also employed to assess model robustness and generalizability. Results indicate that Swin UNETR consistently outperforms nnU-Net and AIMOS in terms of average dice similarity coefficient (DSC) and Hausdorff distance (HD95p), except in two mice of intestine contouring. This superior performance is especially evident in the external dataset, confirming the model's robustness to variations in imaging conditions, including noise and quality, thereby positioning Swin UNETR as a highly generalizable and efficient tool for automated contouring in pre-clinical workflows.

Technical note: Multi-MATE, a high-throughput platform for automated image-guided small-animal irradiation

BACKGROUND

Small animal irradiation is essential to study the radiation response of new interventions before or parallel to human therapy. Image-guided radiotherapy (IGRT) and intensity-modulated radiotherapy (IMRT) are recently adopted in small animal irradiation to more closely mimic human treatments. However, sophisticated techniques require exceedingly high time, resources, and expertize that are often impractical.

PURPOSE

We propose a high throughput and high precision platform named Multiple Mouse Automated Treatment Environment (Multi-MATE) to streamline image-guided small animal irradiation.

METHODS

Multi-MATE consists of six parallel and hexagonally arranged channels, each equipped with a transfer railing, a 3D-printed immobilization pod, and an electromagnetic control unit, computer-controlled via an Arduino interface. The mouse immobilization pods are transferred along the railings between the home position outside the radiation field and the imaging/irradiation position at the irradiator isocenter. All six immobilization pods are transferred to the isocenter in the proposed workflow for parallel CBCT scans and treatment planning. The immobilization pods are then sequentially transported to the imaging/therapy position for dose delivery. The positioning reproducibility of Multi-MATE are evaluated using CBCT and radiochromic films.

RESULTS

While parallelizing and automating the image-guided small animal radiation delivery, Multi-MATE achieved the average pod position reproducibility of 0.17 ± 0.04 mm in the superior-inferior direction, 0.20 ± 0.04 mm in the left-right direction, and 0.12 ± 0.02mm in the anterior-posterior direction in repeated CBCT tests. Additionally, in image-guided dose delivery tasks, Multi-MATE demonstrated the positioning reproducibility of 0.17 ± 0.06 mm in the superior-inferior direction, 0.19 ± 0.06 mm in the left-right direction.

CONCLUSIONS

We designed, fabricated, and tested a novel automated irradiation platform, Multi-MATE to accelerate and automate image-guided small animal irradiation. The automated platform minimizes human operation and achieves high setup reproducibility and image-guided dose delivery accuracy. Multi-MATE thus removes a major barrier to implementing high-precision preclinical radiation research.