Improving the efficiency of small animal 3D‐printed compensator IMRT with beamlet intensity total variation regularization

Liu, Xinmin; Slyke, Alexander L. Van; Pearson, Erik; Shoniyozov, Khayrullo; Redler, Gage; Wiersma, R

doi:10.1002/mp.15764

Cited by 4 publications

(2 citation statements)

References 25 publications

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“…Yet, conformal dose distribution needs to be contextualized with 3D organ contours, which are often absent in pre-clinical workflows. This challenge is amplified with the emergence of small animal intensity-modulated radiotherapy (IMRT) [46][47][48][49][50][51], which replicates human radiotherapy practices by requiring meticulous organ delineation to meet specific 3D dosimetric objectives. While manual delineation of organs is routinely performed for human patient treatment, it is less practical in preclinical studies due to time, resource, and expertise constraints.…”

Section: Discussionmentioning

confidence: 99%

Anatomy-constrained synthesis for spleen segmentation improvement in unpaired mouse micro-CT scans with 3D CycleGAN

Jiang,

Xu,

Sheng

2024

Biomed. Phys. Eng. Express

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Objective: Auto-segmentation in mouse micro-CT enhances the efficiency and consistency of preclinical experiments but often struggles with low-native-contrast and morphologically complex organs, such as the spleen, resulting in poor segmentation performance. While CT contrast agents can improve organ conspicuity, their use complicates experimental protocols and reduces feasibility. We developed a 3D Cycle Generative Adversarial Network (CycleGAN) incorporating anatomy-constrained U-Net models to leverage contrast-enhanced CT (CECT) insights to improve unenhanced native CT (NACT) segmentation. Approach: We employed a standard CycleGAN with an anatomical loss function to synthesize virtual CECT images from unpaired NACT scans at two different resolutions. Prior to training, two U-Nets were trained to automatically segment six major organs in NACT and CECT datasets, respectively. These pretrained 3D U-Nets were integrated during the CycleGAN training, segmenting synthetic images, and comparing them against ground truth annotations. The compound loss within the CycleGAN maintained anatomical fidelity. Full image processing was achieved for low-resolution datasets, while high-resolution datasets employed a patch-based method due to GPU memory constraints. Automated segmentation was applied to original NACT and synthetic CECT scans to evaluate CycleGAN performance using the Dice Similarity Coefficient (DSC) and the 95th percentile Hausdorff Distance (HD95p).Main Results:High-resolution scans showed improved auto-segmentation, with an average DSC increase from 0.728 to 0.773 and a reduced HD95p from 1.19 mm to 0.94 mm. Low-resolution scans benefited more from synthetic contrast, showing a DSC increase from 0.586 to 0.682 and an HD95p reduction from 3.46 mm to 1.24 mm. Significance:Implementing CycleGAN to synthesize CECT scans substantially improved the visibility of the mouse spleen, leading to more precise auto-segmentation. This approach shows the potential in preclinical imaging studies where contrast agent use is impractical.

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Section: Discussionmentioning

confidence: 99%

Anatomy-constrained synthesis for spleen segmentation improvement in unpaired mouse micro-CT scans with 3D CycleGAN

Jiang,

Xu,

Sheng

2024

Biomed. Phys. Eng. Express

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“…Simple modifications to the existing platform to perform dose painting is impractically slow, yet the dosimetry is still unsatisfactory (van Hoof et al 2019). Recently, 3D printed compensators have been employed for small animal IMRT (Redler et al 2021, Liu et al 2022. The method circumvents the need for an MLC but requires a new compensator to be printed for each experimental field.…”

Section: Introductionmentioning

confidence: 99%

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

Jiang¹,

Lyu²,

Abdelhamid³

et al. 2022

Phys. Med. Biol.

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Objective: Intensity-modulated radiotherapy (IMRT) is widely used in clinical radiotherapy, treating varying malignancies with conformal doses. As the test field for clinical translation, preclinical small animal experiments need to mimic the human radiotherapy condition, including IMRT. However, small animal IMRT is a systematic challenge due to the lack of corresponding hardware and software for miniaturized targets. Approach: The sparse orthogonal collimators (SOC) based on the direct rectangular aperture optimization (RAO) substantially simplified the hardware for miniaturization. This study investigates and evaluates a significantly improved RAO algorithm for complex mouse irradiation using SOC. Because the Kronecker product representation of the rectangular aperture is the main limitation of the computational performance, we reformulated matrix multiplication in the data fidelity term using multiplication with small matrices instead of the Kronecker product of the dose loading matrices. Solving the optimization problem was further accelerated using the Fast Iterative Shrinkage-Thresholding Algorithm (FISTA). Main results: Four mouse cases, including a liver, a brain tumor, a concave U-target, and a complex total marrow irradiation (TMI) case, were included in this study with manually delineated targets and OARs. Seven coplanar-field SOC IMRT plans were compared with IMRT plans using an idealistic multi-leaf collimator (MLC). For the first three cases with simpler and smaller targets, the differences between SOC plans and MLC plans in the planning target volumes (PTV) statistics are within 1%. For the TMI case, the SOC plans are superior in reducing hot spots (also termed Dmax) of PTV, kidneys, lungs, heart, and bowel by 20.5%, 31.5%, 24.67%, 20.13%, and 17.78%, respectively. On average, in four cases in this study, the SOC plan conformity is comparable to that of the MLC’s with lightly increased R50 and Integral Dose by 2.23% and 2.78%. Significance: The significantly improved SOC optimization method allows fast plan creation in under 1 minute for smaller targets and makes complex TMI planning feasible while achieving comparable dosimetry to hypothetical IMRT with a theoretical MLC.

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Robust Automated Mouse Micro-CT Segmentation Using Swin UNEt TRansformers

Jiang,

Xu,

et al. 2024

Bioengineering

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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.

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Improving the efficiency of small animal 3D‐printed compensator IMRT with beamlet intensity total variation regularization

Cited by 4 publications

References 25 publications

Anatomy-constrained synthesis for spleen segmentation improvement in unpaired mouse micro-CT scans with 3D CycleGAN

Anatomy-constrained synthesis for spleen segmentation improvement in unpaired mouse micro-CT scans with 3D CycleGAN

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

Robust Automated Mouse Micro-CT Segmentation Using Swin UNEt TRansformers

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