Machine Learning Derived Blood Input for Dynamic PET Images of Rat Heart

• URL: http://arxiv.org/abs/2505.15488v1
• Date: Wed, 21 May 2025 13:10:44 GMT
• Title: Machine Learning Derived Blood Input for Dynamic PET Images of Rat Heart
• Authors: Shubhrangshu Debsarkar, Bijoy Kundu,
• Abstract summary: We developed a dual output model for simultaneous estimation of model corrected blood input function (MCIF) and kinetic rate constants for dynamic FDG PET images of rat heart in vivo.<n>We used semi-automated segmentation and then formulated a Long-Short-Term Memory (LSTM) cell network to train and predict MCIF in test data using a concatenation of IDIFs and myocardial inputs.<n>The model midpoint was able to achieve a 56.4% improvement over previous Mean Squared Error (MSE)
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Dynamic FDG PET imaging study of n = 52 rats including 26 control Wistar-Kyoto (WKY) rats and 26 experimental spontaneously hypertensive rats (SHR) were performed using a Siemens microPET and Albira trimodal scanner longitudinally at 1, 2, 3, 5, 9, 12 and 18 months of age. A 15-parameter dual output model correcting for spill over contamination and partial volume effects with peak fitting cost functions was developed for simultaneous estimation of model corrected blood input function (MCIF) and kinetic rate constants for dynamic FDG PET images of rat heart in vivo. Major drawbacks of this model are its dependence on manual annotations for the Image Derived Input Function (IDIF) and manual determination of crucial model parameters to compute MCIF. To overcome these limitations, we performed semi-automated segmentation and then formulated a Long-Short-Term Memory (LSTM) cell network to train and predict MCIF in test data using a concatenation of IDIFs and myocardial inputs and compared them with reference-modeled MCIF. Thresholding along 2D plane slices with two thresholds, with T1 representing high-intensity myocardium, and T2 representing lower-intensity rings, was used to segment the area of the LV blood pool. The resultant IDIF and myocardial TACs were used to compute the corresponding reference (model) MCIF for all data sets. The segmented IDIF and the myocardium formed the input for the LSTM network. A k-fold cross validation structure with a 33:8:11 split and 5 folds was utilized to create the model and evaluate the performance of the LSTM network for all datasets. To overcome the sparseness of data as time steps increase, midpoint interpolation was utilized to increase the density of datapoints beyond time = 10 minutes. The model utilizing midpoint interpolation was able to achieve a 56.4% improvement over previous Mean Squared Error (MSE).

Related papers

• A robust and versatile deep learning model for prediction of the arterial input function in dynamic small animal $\left[^{18}\ ext{F}\ ight]$FDG PET imaging [14.501528921071456]
  This work proposes a non-invasive, fully convolutional deep learning-based approach (FC-DLIF) to predict input functions directly from PET imaging.<n>FC-DLIF includes a spatial feature extractor acting on the volumetric time frames of the PET sequence, and a temporal feature extractor that predicts the arterial input function.<n>Our deep learning-based input function offers a non-invasive and reliable alternative to arterial blood sampling, proving robust and flexible to temporal shifts and different scan durations.
  arXiv  Detail & Related papers  (2025-07-03T06:55:41Z)
• Cardioformer: Advancing AI in ECG Analysis with Multi-Granularity Patching and ResNet [0.6919386619690135]
  Cardioformer is a novel multi-granularity hybrid model.<n>It integrates cross-channel patching, hierarchical residual learning, and a two-stage self-attention mechanism.<n>It consistently outperforms four state-of-the-art baselines.
  arXiv  Detail & Related papers  (2025-05-08T16:44:21Z)
• Finetuning and Quantization of EEG-Based Foundational BioSignal Models on ECG and PPG Data for Blood Pressure Estimation [53.2981100111204]
  Photoplethysmography and electrocardiography can potentially enable continuous blood pressure (BP) monitoring.<n>Yet accurate and robust machine learning (ML) models remains challenging due to variability in data quality and patient-specific factors.<n>In this work, we investigate whether a model pre-trained on one modality can effectively be exploited to improve the accuracy of a different signal type.<n>Our approach achieves near state-of-the-art accuracy for diastolic BP and surpasses by 1.5x the accuracy of prior works for systolic BP.
  arXiv  Detail & Related papers  (2025-02-10T13:33:12Z)
• Model-driven Heart Rate Estimation and Heart Murmur Detection based on Phonocardiogram [4.5546756241897235]
  This study utilizes a publicly available phonocardiogram (PCG) dataset to estimate heart rate. We extend the best-performing model to a multi-task learning framework for simultaneous heart rate estimation and murmur detection.
  arXiv  Detail & Related papers  (2024-07-25T22:56:21Z)
• Deep Learning Based Apparent Diffusion Coefficient Map Generation from Multi-parametric MR Images for Patients with Diffuse Gliomas [1.5267759610392577]
  Apparent diffusion coefficient (ADC) maps derived from diffusion weighted (DWI) MRI provide functional measurements about the water molecules in tissues. This study aims to develop a deep learning framework to synthesize ADC maps from multi-parametric MR images.
  arXiv  Detail & Related papers  (2024-07-02T19:08:40Z)
• DA-Flow: Dual Attention Normalizing Flow for Skeleton-based Video Anomaly Detection [52.74152717667157]
  We propose a lightweight module called Dual Attention Module (DAM) for capturing cross-dimension interaction relationships in-temporal skeletal data. It employs the frame attention mechanism to identify the most significant frames and the skeleton attention mechanism to capture broader relationships across fixed partitions with minimal parameters and flops.
  arXiv  Detail & Related papers  (2024-06-05T06:18:03Z)
• 3D Lymphoma Segmentation on PET/CT Images via Multi-Scale Information Fusion with Cross-Attention [6.499725732124126]
  This study aims to develop a precise segmentation method for diffuse large B-cell lymphoma (DLBCL) lesions. We propose a 3D dual-branch encoder segmentation method using shifted window transformers and a Multi-Scale Information Fusion (MSIF) module. The model was trained and validated on a dataset of 165 DLBCL patients using 5-fold cross-validation.
  arXiv  Detail & Related papers  (2024-02-04T05:25:12Z)
• Improving Diffusion Models for ECG Imputation with an Augmented Template Prior [43.6099225257178]
  noisy and poor-quality recordings are a major issue for signals collected using mobile health systems. Recent studies have explored the imputation of missing values in ECG with probabilistic time-series models. We present a template-guided denoising diffusion probabilistic model (DDPM), PulseDiff, which is conditioned on an informative prior for a range of health conditions.
  arXiv  Detail & Related papers  (2023-10-24T11:34:15Z)
• Three-dimensional micro-structurally informed in silico myocardium -- towards virtual imaging trials in cardiac diffusion weighted MRI [58.484353709077034]
  We propose a novel method to generate a realistic numerical phantom of myocardial microstructure. In-silico tissue models enable evaluating quantitative models of magnetic resonance imaging.
  arXiv  Detail & Related papers  (2022-08-22T22:01:44Z)
• COVID-MTL: Multitask Learning with Shift3D and Random-weighted Loss for Automated Diagnosis and Severity Assessment of COVID-19 [39.57518533765393]
  There is an urgent need for automated methods to assist accurate and effective assessment of COVID-19. We present an end-to-end multitask learning framework (COVID-MTL) that is capable of automated and simultaneous detection (against both radiology and NAT) and severity assessment of COVID-19.
  arXiv  Detail & Related papers  (2020-12-10T08:30:46Z)
• Segmentation of the Myocardium on Late-Gadolinium Enhanced MRI based on 2.5 D Residual Squeeze and Excitation Deep Learning Model [55.09533240649176]
  The aim of this work is to develop an accurate automatic segmentation method based on deep learning models for the myocardial borders on LGE-MRI. A total number of 320 exams (with a mean number of 6 slices per exam) were used for training and 28 exams used for testing. The performance analysis of the proposed ensemble model in the basal and middle slices was similar as compared to intra-observer study and slightly lower at apical slices.
  arXiv  Detail & Related papers  (2020-05-27T20:44:38Z)

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.