Sensitivity analysis of biological washout and depth selection for a machine learning based dose verification framework in proton therapy

• URL: http://arxiv.org/abs/2212.11352v1
• Date: Wed, 21 Dec 2022 20:43:35 GMT
• Title: Sensitivity analysis of biological washout and depth selection for a machine learning based dose verification framework in proton therapy
• Authors: Shixiong Yu, Yuxiang Liu, Zongsheng Hu, Haozhao Zhang, Pengyu Qi, Hao Peng
• Abstract summary: Dose verification based on proton-induced positron emitters is a promising quality assurance tool. To move a step closer towards practical application, the sensitivity analysis of two factors needs to be performed: biological washout and depth selection. Our proposed AI framework shows good immunity to the perturbation associated with two factors.
• Score: 19.718172235291647
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Dose verification based on proton-induced positron emitters is a promising quality assurance tool and may leverage the strength of artificial intelligence. To move a step closer towards practical application, the sensitivity analysis of two factors needs to be performed: biological washout and depth selection. selection. A bi-directional recurrent neural network (RNN) model was developed. The training dataset was generated based upon a CT image-based phantom (abdomen region) and multiple beam energies/pathways, using Monte-Carlo simulation (1 mm spatial resolution, no biological washout). For the modeling of biological washout, a simplified analytical model was applied to change raw activity profiles over a period of 5 minutes, incorporating both physical decay and biological washout. For the study of depth selection (a challenge linked to multi field/angle irradiation), truncations were applied at different window lengths (100, 125, 150 mm) to raw activity profiles. Finally, the performance of a worst-case scenario was examined by combining both factors (depth selection: 125 mm, biological washout: 5 mins). The accuracy was quantitatively evaluated in terms of range uncertainty, mean absolute error (MAE) and mean relative errors (MRE). Our proposed AI framework shows good immunity to the perturbation associated with two factors. The detection of proton-induced positron emitters, combined with machine learning, has great potential to implement online patient-specific verification in proton therapy.

Related papers

• Benchmarking the Reproducibility of Brain MRI Segmentation Across Scanners and Time [0.0190469137058137]
  We quantify inter-scan segmentation variability using Dice, Surface Dice, Hausdorff Distance (HD95), and Mean Absolute Percentage Error (MAPE) Our results reveal up to 7-8% volume variation in small subcortical structures such as the amygdala and ventral diencephalon, even under controlled test-retest conditions.
  arXiv  Detail & Related papers  (2025-04-22T14:20:18Z)
• Mechanics and Design of Metastructured Auxetic Patches with Bio-inspired Materials [0.5033155053523042]
  This study focuses on neural networks-based computational modeling of auxetic patches with a sinusoidal metastructure fabricated from silk fibroin. The proposed framework represents a significant advancement in the design of bio-inspired metastructures for medical applications.
  arXiv  Detail & Related papers  (2025-01-08T03:57:20Z)
• A study on deep feature extraction to detect and classify Acute Lymphoblastic Leukemia (ALL) [0.0]
  Acute lymphoblastic leukaemia (ALL) is a blood malignancy that mainly affects adults and children. This study looks into the use of deep learning, specifically Convolutional Neural Networks (CNNs) for the detection and classification of ALL. With an 87% accuracy rate, the ResNet101 model produced the best results, closely followed by DenseNet121 and VGG19.
  arXiv  Detail & Related papers  (2024-09-10T17:53:29Z)
• CAF-YOLO: A Robust Framework for Multi-Scale Lesion Detection in Biomedical Imagery [0.0682074616451595]
  CAF-YOLO is a nimble yet robust method for medical object detection that leverages the strengths of convolutional neural networks (CNNs) and transformers. ACFM module enhances the modeling of both global and local features, enabling the capture of long-term feature dependencies. MSNN improves multi-scale information aggregation by extracting features across diverse scales.
  arXiv  Detail & Related papers  (2024-08-04T01:44:44Z)
• Artificial Neural Networks-based Real-time Classification of ENG Signals for Implanted Nerve Interfaces [7.335832236913667]
  We explore four types of artificial neural networks (ANNs) to extract sensory stimuli from the electroneurographic (ENG) signal measured in the sciatic nerve of rats. Different sizes of the data sets are considered to analyze the feasibility of the investigated ANNs for real-time classification. Our results show that some ANNs are more suitable for real-time applications, being capable of achieving accuracies over $90%$ for signal windows of $100$ and $200,$ms with a low enough processing time to be effective for pathology recovery.
  arXiv  Detail & Related papers  (2024-03-29T15:23:30Z)
• Equipping Computational Pathology Systems with Artifact Processing Pipelines: A Showcase for Computation and Performance Trade-offs [0.7226586370054761]
  We propose a mixture of experts (MoE) scheme for detecting five notable artifacts, including damaged tissue, blur, folded tissue, air bubbles, and histologically irrelevant blood. We developed DL pipelines using two MoEs and two multiclass models of state-of-the-art deep convolutional neural networks (DCNNs) and vision transformers (ViTs) The proposed MoE yields 86.15% F1 and 97.93% sensitivity scores on unseen data, retaining less computational cost for inference than MoE using ViTs.
  arXiv  Detail & Related papers  (2024-03-12T15:22:05Z)
• The Expressive Leaky Memory Neuron: an Efficient and Expressive Phenomenological Neuron Model Can Solve Long-Horizon Tasks [64.08042492426992]
  We introduce the Expressive Memory (ELM) neuron model, a biologically inspired model of a cortical neuron. Our ELM neuron can accurately match the aforementioned input-output relationship with under ten thousand trainable parameters. We evaluate it on various tasks with demanding temporal structures, including the Long Range Arena (LRA) datasets.
  arXiv  Detail & Related papers  (2023-06-14T13:34:13Z)
• Evaluation of the potential of Near Infrared Hyperspectral Imaging for monitoring the invasive brown marmorated stink bug [53.682955739083056]
  The brown marmorated stink bug (BMSB), Halyomorpha halys, is an invasive insect pest of global importance that damages several crops. The present study consists in a preliminary evaluation at the laboratory level of Near Infrared Hyperspectral Imaging (NIR-HSI) as a possible technology to detect BMSB specimens.
  arXiv  Detail & Related papers  (2023-01-19T11:37:20Z)
• In Silico Prediction of Blood-Brain Barrier Permeability of Chemical Compounds through Molecular Feature Modeling [0.0]
  Development of new drugs to treat central nervous system disorders presents unique challenges due to poor penetration efficacy across the blood-brain barrier. In this research, we aim to mitigate this problem through an ML model that analyzes chemical features.
  arXiv  Detail & Related papers  (2022-08-18T19:59:44Z)
• Differentiable Agent-based Epidemiology [71.81552021144589]
  We introduce GradABM: a scalable, differentiable design for agent-based modeling that is amenable to gradient-based learning with automatic differentiation. GradABM can quickly simulate million-size populations in few seconds on commodity hardware, integrate with deep neural networks and ingest heterogeneous data sources.
  arXiv  Detail & Related papers  (2022-07-20T07:32:02Z)
• Medulloblastoma Tumor Classification using Deep Transfer Learning with Multi-Scale EfficientNets [63.62764375279861]
  We propose an end-to-end MB tumor classification and explore transfer learning with various input sizes and matching network dimensions. Using a data set with 161 cases, we demonstrate that pre-trained EfficientNets with larger input resolutions lead to significant performance improvements.
  arXiv  Detail & Related papers  (2021-09-10T13:07:11Z)
• The Causal Neural Connection: Expressiveness, Learnability, and Inference [125.57815987218756]
  An object called structural causal model (SCM) represents a collection of mechanisms and sources of random variation of the system under investigation. In this paper, we show that the causal hierarchy theorem (Thm. 1, Bareinboim et al., 2020) still holds for neural models. We introduce a special type of SCM called a neural causal model (NCM), and formalize a new type of inductive bias to encode structural constraints necessary for performing causal inferences.
  arXiv  Detail & Related papers  (2021-07-02T01:55:18Z)
• Select-ProtoNet: Learning to Select for Few-Shot Disease Subtype Prediction [55.94378672172967]
  We focus on few-shot disease subtype prediction problem, identifying subgroups of similar patients. We introduce meta learning techniques to develop a new model, which can extract the common experience or knowledge from interrelated clinical tasks. Our new model is built upon a carefully designed meta-learner, called Prototypical Network, that is a simple yet effective meta learning machine for few-shot image classification.
  arXiv  Detail & Related papers  (2020-09-02T02:50:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.