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Lijun Lu

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6 papers
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6

JBHI Journal 2026 Journal Article

Multicontrast MR-Guided Diffusion Model for Ultra-Low-Dose Brain PET Denoising in Temporal Lobe Epilepsy

  • Xiaolong Niu
  • Jieqin Lv
  • Zanting Ye
  • Yibo Wei
  • Yijun Lu
  • Xuanbin Wu
  • Wenxiang Yi
  • Pengcheng Ran

Positron Emission Tomography (PET) is a critical imaging modality in nuclear medicine but requires radioactive tracer administration, which increases radiation exposure risks. While recent studies have investigated MR-guided low-dose PET denoising, they neglect two critical factors: the synergistic roles of multicontrast MR images and disease-specific denoising requirements. In this work, we propose a diffusion model that integrates T1-weighted, T2 fluid attenuated inversion recovery (T2 FLAIR), and hippocampal-optimized (T2 HIPPO) MR sequences to achieve ultra-low-dose PET denoising tailored for temporal lobe epilepsy (TLE). Our parallel cross-modal fusion (PCMF) module employs dedicated encoders to extract cross-modal features—which are dynamically integrated via attention mechanisms. Extensive experiments demonstrate that our method outperforms other approaches in preserving image quality. The PSNR and SSIM obtained were 37. 0251 $\pm$ 1. 5215 dB and 0. 9760 $\pm$ 0. 0057 (p < 0. 01). Compared to the PET-only baseline model (IDDPM), our method achieved improvements of 8. 4% in PSNR and 1. 7% in SSIM, particularly excelling in diagnostically relevant temporal and hippocampal regions. This method provides a novel pathway for disease-specific PET denoising and has the potential to be generalized to other neurodegenerative diseases.

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JBHI Journal 2026 Journal Article

TriFuse-Net: A Tri-Branch PET/CT Fusion Pyramid Network Enhanced by Lesion-Guided Structural-Metabolic Attention for Lung Cancer Diagnosis and Prognosis

  • Yuyu Liu
  • Jieqin Lv
  • Fangfang Yang
  • Huiqin Wu
  • Xiang Pan
  • Li Wang
  • Han Bai
  • Shunfang Wang

Diagnosis and prognosis of lung cancer via PET/CT imaging have long been major clinical concerns. However, existing multimodal approaches often focus on feature aggregation rather than cross-modal interactive collaboration, failing to capture the structural-metabolic correlations and multi-scale synergy essential for characterizing complex lesions. Therefore, this study proposes TriFuse-Net, a tri-branch PET/CT fusion pyramid network (FPN) enhanced by lesion-guided structural-metabolic attention (LSMA) to improve both diagnosis and prognosis prediction tasks. The model is composed of two identical unimodal branches (PET/CT) and one pyramid branch with an interacting channel and spatial attention. The pyramid structure enables bidirectional multiscale feature extraction and fusion, capturing both local details and global semantic information of lesions. Comprehensive experiments validated the model's superiority across three clinical tasks. TriFuse-Net achieved a C-index of 0. 747 for progression-free survival (PFS) prediction, showing improvements of 14. 7% and 11. 0% over ResNet-CT and ResNet-PET, respectively. Additionally, the clinical-integrated model (TriFuse-Net-Cli) achieved AUCs of 0. 947 for differentiating lung cancer from tuberculosis and 0. 937 for identifying lymph-node metastasis. Ablation studies further confirmed the essential contributions of both FPN and LSMA. In summary, the proposed framework demonstrates that integrating multi-scale structural-metabolic relationships significantly enhances diagnosis and prognosis in lung cancer.

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JBHI Journal 2020 Journal Article

Multi-Level Multi-Modality Fusion Radiomics: Application to PET and CT Imaging for Prognostication of Head and Neck Cancer

  • Wenbing Lv
  • Saeed Ashrafinia
  • Jianhua Ma
  • Lijun Lu
  • Arman Rahmim

To characterize intra-tumor heterogeneity comprehensively, we propose a multi-level fusion strategy to combine PET and CT information at the image-, matrix-and feature-levels towards improved prognosis. Specifically, we developed fusion radiomics in the context of 3 prognostic outcomes in a multi-center setting (4 centers) involving 296 head & neck cancer patients. Eight clinical parameters were first utilized to build a (1) clinical model. We also built models by extracting 127 radiomics features from (2) PET images alone; (3–8) PET and CT images fused via wavelet-based fusion (WF) using CT-weights of 0. 2, 0. 4, 0. 6 and 0. 8, gradient transfer fusion (GTF), and guided filtering-based fusion (GFF); (9) fused matrices (sumMat); (10–11) fused features constructed via feature averaging (avgFea) and feature concatenation (conFea); and finally, (12) CT images alone; above models were also expanded to include both clinical and radiomics features. Seven variations of training and testing partitions were investigated. Highest performance in 5, 6 and 5 partitions was achieved by image-level fusion strategies for RFS, MFS and OS prediction, respectively. Among all partitions, WF0. 6 and WF0. 8 showed significantly higher performance than CT model for RFS (C-index: 0. 60 ± 0. 04 vs. 0. 56 ± 0. 03, p-value: 0. 015) and MFS (C-index: 0. 71 ± 0. 13 vs. 0. 62 ± 0. 08, p-value: 0. 020) predictions, respectively. In partition CER 23 vs. 14, WF0. 6 significantly outperformed Clinical model for RFS prediction (C-index: 0. 67 vs. 0. 53, p-value: 0. 003); both avgFea and WF0. 6 showed C-index of 0. 64 and significantly higher than that of PET only (C-index: 0. 51, p-value: 0. 018 and 0. 031, respectively) for OS prediction. Fusion radiomics modeling showed varying improvements compared to single modality models for different outcome predictions in different partitions, highlighting the importance of generalizing radiomics models. Image-level fusion holds potential to capture more useful characteristics.

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