@article{li_2024_efficient,
  author = {Li, Laquan and Jiang, Chuangbo and Yu, Lei and Zeng, Xianhua and Zheng, Shenhai},
  month = {08},
  pages = {108980},
  publisher = {Pergamon},
  title = {Efficient model-informed co-segmentation of tumors on PET/CT driven by clustering and classification information},
  doi = {10.1016/j.compbiomed.2024.108980},
  url = {https://www.sciencedirect.com/science/article/pii/S0010482524010655},
  volume = {180},
  year = {2024},
  journal = {Computers in Biology and Medicine}
}

@article{huang_2025_3d,
  author = {Huang, Huan and Qiu, Liheng and Yang, Shenmiao and Li, Longxi and Nan, Jiaofen and Li, Yanting and Han, Chuang and Zhu, Fubao and Zhao, Chen and Zhou, Weihua},
  month = {06},
  pages = {4371-4389},
  title = {3D lymphoma segmentation on PET/CT images via multi-scale information fusion with cross-attention},
  doi = {10.1002/mp.17763},
  url = {https://pubmed.ncbi.nlm.nih.gov/40111352/},
  volume = {52},
  year = {2025},
  journal = {Medical physics}
}

@misc{oh_2025_petctfoundationmodel,
  author = {Oh, Yujin and Seifert, Robert and Cao, Yihan and Clement, Christoph and Ferdinandus, Justin and Lapa, Constantin and Liebich, Alessandro and Amon, Michelle and Enke, Johanna and Song, Sifan and Meng, Runqi and Zeng, Fang and Guo, Ning and Li, Xiang and Heidari, Pedram and Rominger, Axel and Shi, Kuangyu and Li, Quanzheng},
  month = {03},
  title = {Developing a PET/CT Foundation Model for Cross-Modal Anatomical and Functional Imaging},
  url = {https://arxiv.org/abs/2503.02824},
  year = {2025},
  organization = {arXiv.org}
}

@article{ahamed_2023_towards,
  author = {Ahamed, S and Xu, Y and R. Dodhia and Weeks, W and Ferres, J L and A. Rahmim},
  month = {11},
  title = {Towards an automated Segment Anything Model (SAM) for lesion segmentation in oncological PET/CT images},
  doi = {10.1109/nssmicrtsd49126.2023.10338080},
  url = {https://ieeexplore.ieee.org/document/10338080},
  year = {2023},
  journal = {2023 IEEE Nuclear Science Symposium, Medical Imaging Conference and International Symposium on Room-Temperature Semiconductor Detectors (NSS MIC RTSD)}
}

@article{kumar_2024_is,
  author = {Kumar, Lalith and Beyer, Thomas},
  month = {06},
  pages = {995-997},
  publisher = {Society of Nuclear Medicine and Molecular Imaging},
  title = {Is Automatic Tumor Segmentation on Whole-Body<sup>18</sup>F-FDG PET Images a Clinical Reality?},
  doi = {10.2967/jnumed.123.267183},
  url = {https://pmc.ncbi.nlm.nih.gov/articles/PMC11218718},
  urldate = {2025-10-12},
  volume = {65},
  year = {2024},
  journal = {Journal of Nuclear Medicine}
}

@article{fang_2025_multimodal,
  author = {Fang, Zhijie and Liu, Yibin and Du, Yu and Wang, Feng and Mok, Greta},
  month = {06},
  pages = {251671-251671},
  publisher = {Society of Nuclear Medicine},
  title = {Multi-modal Segment Anything Model (mmSAM) for Lesion Segmentation in Multi-tracer Oncologic PET/CT},
  url = {https://jnm.snmjournals.org/content/66/supplement_1/251671},
  urldate = {2025-10-12},
  volume = {66},
  year = {2025},
  journal = {Journal of Nuclear Medicine}
}

@misc{ren_2024_segment,
  author = {Ren, Jintao and Rasmussen, Mathis and Nijkamp, Jasper and Eriksen, Jesper Grau and Korreman, Stine},
  title = {Segment anything model for head and neck tumor segmentation with CT, PET and MRI multi-modality images},
  url = {https://arxiv.org/abs/2402.17454},
  urldate = {2025-10-12},
  year = {2024},
  organization = {arXiv.org}
}

@misc{li_2025_meddinov3,
  author = {Li, Yuheng and Wu, Yizhou and Lai, Yuxiang and Hu, Mingzhe and Yang, Xiaofeng},
  title = {MedDINOv3: How to adapt vision foundation models for medical image segmentation?},
  url = {https://arxiv.org/abs/2509.02379},
  urldate = {2025-10-12},
  year = {2025},
  organization = {arXiv.org}
}

@misc{zhang_2025_seganypet,
  author = {Zhang, Yichi and Xue, Le and Zhang, Wenbo and Li, Lanlan and Liu, Yuchen and Jiang, Chen and Cheng, Yuan and Qi, Yuan},
  title = {SegAnyPET: Universal Promptable Segmentation from Positron Emission Tomography Images},
  url = {https://arxiv.org/abs/2502.14351v1},
  urldate = {2025-10-12},
  year = {2025},
  organization = {arXiv.org}
}

@article{nguyen_2024_necessity,
  author = {Nguyen, Eric and Liu, Hengjie and Ruan, Dan},
  month = {10},
  title = {Necessity and impact of specialization of large foundation model for medical segmentation tasks},
  doi = {10.1002/mp.17470},
  urldate = {2024-11-24},
  year = {2024},
  journal = {Medical Physics}
}

@misc{rokuss_2024_from,
  author = {Rokuss, Maximilian and Kovacs, Balint and Kirchhoff, Yannick and Xiao, Shuhan and Ulrich, Constantin and Maier-Hein, Klaus H and Isensee, Fabian},
  title = {From FDG to PSMA: A Hitchhiker's Guide to Multitracer, Multicenter Lesion Segmentation in PET/CT Imaging},
  url = {https://arxiv.org/abs/2409.09478},
  urldate = {2025-10-12},
  year = {2024},
  organization = {arXiv.org}
}

@misc{he_2024_vista3d,
  author = {He, Yufan and Guo, Pengfei and Tang, Yucheng and Myronenko, Andriy and Nath, Vishwesh and Xu, Ziyue and Yang, Dong and Zhao, Can and Simon, Benjamin and Belue, Mason and Harmon, Stephanie and Turkbey, Baris and Xu, Daguang and Li, Wenqi},
  title = {VISTA3D: A Unified Segmentation Foundation Model For 3D Medical Imaging},
  url = {https://arxiv.org/abs/2406.05285},
  year = {2024},
  organization = {arXiv.org}
}

@misc{liu_2025_does,
  author = {Liu, Che and Chen, Yinda and Shi, Haoyuan and Lu, Jinpeng and Jian, Bailiang and Pan, Jiazhen and Cai, Linghan and Wang, Jiayi and Zhang, Yundi and Li, Jun and Bercea, Cosmin I and Ouyang, Cheng and Chen, Chen and Xiong, Zhiwei and Wiestler, Benedikt and Wachinger, Christian and Rueckert, Daniel and Bai, Wenjia and Arcucci, Rossella},
  title = {Does DINOv3 Set a New Medical Vision Standard?},
  url = {https://arxiv.org/abs/2509.06467},
  urldate = {2025-10-24},
  year = {2025},
  organization = {arXiv.org}


@article{gatidis_2022_a,
  author = {Gatidis, Sergios and Hepp, Tobias and Früh, Marcel and La Fougère, Christian and Nikolaou, Konstantin and Pfannenberg, Christina and Schölkopf, Bernhard and Küstner, Thomas and Cyran, Clemens and Rubin, Daniel},
  month = {10},
  title = {A whole-body FDG-PET/CT Dataset with manually annotated Tumor Lesions},
  doi = {10.1038/s41597-022-01718-3},
  urldate = {2023-02-22},
  volume = {9},
  year = {2022},
  journal = {Scientific Data}
}

@article{hecktor_dataset,
title = {Head and neck tumor segmentation in PET/CT: The HECKTOR challenge},
journal = {Medical Image Analysis},
volume = {77},
pages = {102336},
year = {2022},
issn = {1361-8415},
doi = {https://doi.org/10.1016/j.media.2021.102336},
url = {https://www.sciencedirect.com/science/article/pii/S1361841521003819},
author = {Valentin Oreiller and Vincent Andrearczyk and Mario Jreige and Sarah Boughdad and Hesham Elhalawani and Joel Castelli and Martin Vallières and Simeng Zhu and Juanying Xie and Ying Peng and Andrei Iantsen and Mathieu Hatt and Yading Yuan and Jun Ma and Xiaoping Yang and Chinmay Rao and Suraj Pai and Kanchan Ghimire and Xue Feng and Mohamed A. Naser and Clifton D. Fuller and Fereshteh Yousefirizi and Arman Rahmim and Huai Chen and Lisheng Wang and John O. Prior and Adrien Depeursinge},
keywords = {Medical imaging, Head and neck cancer, Oropharynx, Automatic segmentation, Challenge},
}
@misc{simoni_2025_dinov3,
  author = {Siméoni, Oriane and Vo, Huy V and Seitzer, Maximilian and Baldassarre, Federico and Oquab, Maxime and Jose, Cijo and Khalidov, Vasil and Szafraniec, Marc and Yi, Seungeun and Ramamonjisoa, Michaël and Massa, Francisco and Haziza, Daniel and Wehrstedt, Luca and Wang, Jianyuan and Darcet, Timothée and Moutakanni, Théo and Sentana, Leonel and Roberts, Claire and Vedaldi, Andrea and Tolan, Jamie and Brandt, John and Couprie, Camille and Mairal, Julien and Jégou, Hervé and Labatut, Patrick and Bojanowski, Piotr},
  title = {DINOv3},
  url = {https://arxiv.org/abs/2508.10104},
  year = {2025},
  organization = {arXiv.org}
}
@misc{wald_2025_primus,
  author = {Wald, Tassilo and Roy, Saikat and Isensee, Fabian and Ulrich, Constantin and Ziegler, Sebastian and Trofimova, Dasha and Stock, Raphael and Baumgartner, Michael and Köhler, Gregor and Maier-Hein, Klaus},
  title = {Primus: Enforcing Attention Usage for 3D Medical Image Segmentation},
  url = {https://arxiv.org/abs/2503.01835},
  urldate = {2025-11-10},
  year = {2025},
  organization = {arXiv.org}
}

@misc{zhou_2022_ibot,
  author = {Zhou, Jinghao and Wei, Chen and Wang, Huiyu and Shen, Wei and Xie, Cihang and Yuille, Alan and Kong, Tao},
  month = {01},
  title = {iBOT: Image BERT Pre-Training with Online Tokenizer},
  doi = {10.48550/arXiv.2111.07832},
  url = {https://arxiv.org/abs/2111.07832},
  year = {2022},
  organization = {arXiv.org}
}

@article{bommasani_2021_on,
  author = {Bommasani, Rishi},
  month = {08},
  title = {On the Opportunities and Risks of Foundation Models},
  url = {https://arxiv.org/abs/2108.07258},
  volume = {1},
  year = {2021},
  journal = {arXiv:2108.07258 [cs]}
}

@article{zhou_2023_a,
  author = {Zhou, Ce and Li, Qian and Li, Chen and Yu, Jun and Liu, Yixin and Wang, Guangjing and Zhang, Kai and Ji, Cheng and Yan, Qiben and He, Lifang and Peng, Hao and Li, Jianxin and Wu, Jia and Liu, Ziwei and Xie, Pengtao and Xiong, Caiming and Pei, Jian and Yu, Philip S. and Sun, Lichao},
  month = {02},
  title = {A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT},
  url = {https://arxiv.org/abs/2302.09419},
  year = {2023},
  journal = {arXiv:2302.09419 [cs]}
}

@article{caron_2021_emerging,
  author = {Caron, Mathilde and Touvron, Hugo and Misra, Ishan and Jégou, Hervé and Mairal, Julien and Bojanowski, Piotr and Joulin, Armand},
  pages = {9650-9660},
  title = {Emerging Properties in Self-Supervised Vision Transformers},
  url = {https://openaccess.thecvf.com/content/ICCV2021/html/Caron_Emerging_Properties_in_Self-Supervised_Vision_Transformers_ICCV_2021_paper},
  year = {2021},
  journal = {Thecvf.com}
}

@misc{sablayrolles_2018_spreading,
  author = {Sablayrolles, Alexandre and Douze, Matthijs and Schmid, Cordelia and Jégou, Hervé},
  title = {Spreading vectors for similarity search},
  url = {https://arxiv.org/abs/1806.03198},
  urldate = {2025-11-10},
  year = {2018},
  organization = {arXiv.org}
}

@article{isensee_2020_nnunet,
  author = {Isensee, Fabian and Jaeger, Paul F. and Kohl, Simon A. A. and Petersen, Jens and Maier-Hein, Klaus H.},
  month = {12},
  pages = {203-211},
  title = {nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation},
  doi = {10.1038/s41592-020-01008-z},
  url = {https://www.nature.com/articles/s41592-020-01008-z},
  volume = {18},
  year = {2020},
  journal = {Nature Methods}
}


@article{ma_2024_segment,
  author = {Ma, Jun and He, Yuting and Li, Feifei and Han, Lin and You, Chenyu and Wang, Bo},
  month = {01},
  title = {Segment anything in medical images},
  doi = {10.1038/s41467-024-44824-z},
  url = {https://arxiv.org/pdf/2304.12306v2.pdf},
  volume = {15},
  year = {2024},
  journal = {Nature Communications}
}
@article{kirillov_2023_segment,
  author = {Kirillov, Alexander and Mintun, Eric and Ravi, Nikhila and Mao, Hanzi and Rolland, Chloe and Gustafson, Laura and Xiao, Tete and Whitehead, Spencer and Berg, Alexander C. and Lo, Wan-Yen and Dollár, Piotr and Girshick, Ross},
  month = {04},
  title = {Segment Anything},
  url = {https://arxiv.org/abs/2304.02643},
  year = {2023},
  journal = {arXiv:2304.02643 [cs]}
}

@article{he_2021_masked,
  author = {He, Kaiming and Chen, Xinlei and Xie, Saining and Li, Yanghao and Dollár, Piotr and Girshick, Ross},
  month = {12},
  title = {Masked Autoencoders Are Scalable Vision Learners},
  url = {https://arxiv.org/abs/2111.06377},
  year = {2021},
  journal = {arXiv:2111.06377 [cs]}
}

@misc{xie_2022_simmim,
  author = {Xie, Zhenda and Zhang, Zheng and Cao, Yue and Lin, Yutong and Bao, Jianmin and Yao, Zhuliang and Dai, Qi and Hu, Han},
  month = {04},
  title = {SimMIM: A Simple Framework for Masked Image Modeling},
  doi = {10.48550/arXiv.2111.09886},
  url = {https://arxiv.org/abs/2111.09886},
  year = {2022},
  organization = {arXiv.org}
}

@article{chen_2020_a,
  author = {Chen, Ting and Kornblith, Simon and Norouzi, Mohammad and Hinton, Geoffrey},
  month = {06},
  title = {A Simple Framework for Contrastive Learning of Visual Representations},
  url = {https://arxiv.org/abs/2002.05709},
  year = {2020},
  journal = {arXiv:2002.05709 [cs, stat]}
}

@article{grill_2020_bootstrap,
  author = {Grill, Jean-Bastien and Strub, Florian and Altché, Florent and Tallec, Corentin and Richemond, Pierre H. and Buchatskaya, Elena and Doersch, Carl and Pires, Bernardo Avila and Guo, Zhaohan Daniel and Azar, Mohammad Gheshlaghi and Piot, Bilal and Kavukcuoglu, Koray and Munos, Rémi and Valko, Michal},
  month = {09},
  title = {Bootstrap your own latent: A new approach to self-supervised Learning},
  url = {https://arxiv.org/abs/2006.07733},
  year = {2020},
  journal = {arXiv:2006.07733 [cs, stat]}
}

@misc{darcet_dinov2,
  author = {Darcet, Timothée and Moutakanni, Théo and Vo, Huy and Szafraniec, Marc and Khalidov, Vasil and Fernandez, Pierre and Haziza, Daniel and Massa, Francisco and El-Nouby, Alaaeldin and Assran, Mahmoud and Ballas, Nicolas and Galuba, Wojciech and Howes, Russell and Huang, Po-Yao and Li, Shang-Wen and Misra, Ishan and Rabbat, Michael and Sharma, Vasu and Synnaeve, Gabriel and Xu, Hu and Jegou, Hervé and Mairal, Julien and Labatut, Patrick and Joulin, Armand and Bojanowski, Piotr},
  title = {DINOv2: Learning Robust Visual Features without Supervision Maxime Oquab},
  url = {https://arxiv.org/pdf/2304.07193}
}

@article{andreigafita_2025_treatment,
  author = {Andrei Gafita and Schroeder, Jennifer A and Ceci, Francesco and Oldan, Jorge D and Khandani, Amir H and Lecouvet, Frederic E and Solnes, Lilja B and Rowe, Steven P},
  month = {06},
  pages = {jnumed.124.268071-jnumed.124.268071},
  publisher = {Society of Nuclear Medicine and Molecular Imaging},
  title = {Treatment Response Evaluation in Prostate Cancer Using PSMA PET/CT},
  doi = {10.2967/jnumed.124.268071},
  year = {2025},
  journal = {Journal of Nuclear Medicine}
}

@article{guido_2009_combined,
  author = {Guido, A and Fuccio, Lorenzo and Rombi, Barbara and Castellucci, Paolo and Cecconi, Agnese and Feisal Bunkheila and Chiara Fuccio and Emiliano Spezi and Angelini, Anna and Barbieri, Enza},
  month = {03},
  pages = {759-763},
  publisher = {Elsevier BV},
  title = {Combined 18F-FDG-PET/CT Imaging in Radiotherapy Target Delineation for Head-and-Neck Cancer},
  doi = {10.1016/j.ijrobp.2008.04.059},
  urldate = {2023-12-04},
  volume = {73},
  year = {2009},
  journal = {International Journal of Radiation Oncology Biology Physics}
}

@article{heron_2004_hybrid,
  author = {Heron, Dwight E. and Andrade, Regiane S. and Flickinger, John and Johnson, Jonas and Agarwala, Sanjiv S. and Wu, Andrew and Kalnicki, Shalom and Avril, Norbert},
  month = {12},
  pages = {1419-1424},
  title = {Hybrid PET-CT simulation for radiation treatment planning in head-and-neck cancers: A brief technical report},
  doi = {10.1016/j.ijrobp.2004.05.037},
  urldate = {2023-03-03},
  volume = {60},
  year = {2004},
  journal = {International Journal of Radiation Oncology*Biology*Physics}
}

@article{schinagl_2013_pathologybased,
  author = {Schinagl, Dominic A X and Span, Paul N and van den Hoogen, Frank J A and Merkx, Matthias A W and Slootweg, Piet J and Oyen, Wim J G and Kaanders, Johannes H A M},
  month = {12},
  pages = {1828-35},
  title = {Pathology-based validation of FDG PET segmentation tools for volume assessment of lymph node metastases from head and neck cancer},
  doi = {10.1007/s00259-013-2513-9},
  url = {https://pubmed.ncbi.nlm.nih.gov/23942906/},
  volume = {40},
  year = {2013},
  journal = {European journal of nuclear medicine and molecular imaging}
}

@article{vandenbosch_2020_18ffdgpetctbased,
  author = {van den Bosch, Sven and Doornaert, Patricia A.H. and Dijkema, Tim and Zwijnenburg, Ellen M. and Verhoef, Lia C.G. and Hoeben, Bianca A.W. and Kasperts, Nicolien and Smid, Ernst J. and Terhaard, Chris H.J. and Kaanders, Johannes H.A.M.},
  month = {01},
  pages = {107-114},
  title = {18F-FDG-PET/CT-based treatment planning for definitive (chemo)radiotherapy in patients with head and neck squamous cell carcinoma improves regional control and survival},
  doi = {10.1016/j.radonc.2019.07.025},
  urldate = {2021-05-08},
  volume = {142},
  year = {2020},
  journal = {Radiotherapy and Oncology}
}


@article{noh_2025_a,
  author = {Noh, Seungha and Lee, Byoung-Dai},
  month = {06},
  pages = {5825-5858},
  publisher = {AME Publishing Company},
  title = {A narrative review of foundation models for medical image segmentation: zero-shot performance evaluation on diverse modalities},
  doi = {10.21037/qims-2024-2826},
  url = {https://pmc.ncbi.nlm.nih.gov/articles/PMC12209621/},
  urldate = {2025-09-07},
  volume = {15},
  year = {2025},
  journal = {Quantitative Imaging in Medicine and Surgery}
}

@InProceedings{XueSon_UDPET_MICCAI2025,
        author = { Xue, Song AND Wang, Hanzhong AND Chen, Yizhou AND Liu, Fanxuan AND Zhu, Hong AND Viscione, Marco AND Guo, Rui AND Rominger, Axel AND Li, Biao AND Shi, Kuangyu},
        title = { { UDPET: Ultra-low Dose PET Imaging Challenge Dataset } },
        booktitle = {proceedings of Medical Image Computing and Computer Assisted Intervention -- MICCAI 2025},
        year = {2025},
        publisher = {Springer Nature Switzerland},
        volume = {LNCS 15972},
        month = {September},
        page = {616 -- 623}
}

@ARTICLE{mri-styled-pet,
  author={Lin, Yu-Nong and Huang, Shao-Yi and Tsai, Cheng-Han and Wang, Han-Wei and Chung, Meng-Chen and Gong, Enhao and Hsiao, Ing-Tsung and Chen, Kevin T.},
  journal={IEEE Transactions on Radiation and Plasma Medical Sciences}, 
  title={MRI-Styled PET: A Dual Modality Fusion Approach to PET Partial Volume Correction}, 
  year={2025},
  volume={9},
  number={7},
  pages={939-950},
  keywords={Feature extraction;Magnetic resonance imaging;Image fusion;Biomedical imaging;Positron emission tomography;Mathematical models;Image reconstruction;Alzheimer's disease;Accuracy;Functional neuroimaging;Anatomy guided;FDG-positron emission tomography (PET);image fusion;multiscale SSIM;partial volume correction (PVC)},
  doi={10.1109/TRPMS.2025.3549617}
}

@misc{wang2025dinov3testtimetrainingmedical,
      title={DINOv3 with Test-Time Training for Medical Image Registration}, 
      author={Shansong Wang and Mojtaba Safari and Mingzhe Hu and Qiang Li and Chih-Wei Chang and Richard LJ Qiu and Xiaofeng Yang},
      year={2025},
      eprint={2508.14809},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2508.14809}, 
}

@misc{yang_2025_segdino,
  author = {Yang, Sicheng and Wang, Hongqiu and Xing, Zhaohu and Chen, Sixiang and Zhu, Lei},
  title = {SegDINO: An Efficient Design for Medical and Natural Image Segmentation with DINO-V3},
  url = {https://arxiv.org/abs/2509.00833},
  urldate = {2025-12-06},
  year = {2025},
  organization = {arXiv.org}
}

@article{dimitrakopouloustrauss_2023_long,
  author = {Dimitrakopoulou-Strauss, Antonia and Pan, Leyun and Christos Sachpekidis},
  month = {04},
  pages = {3354-3362},
  publisher = {Springer Science+Business Media},
  title = {Long axial field of view (LAFOV) PET-CT: implementation in static and dynamic oncological studies},
  doi = {10.1007/s00259-023-06222-3},
  url = {https://doi.org/10.1007%2Fs00259-023-06222-3},
  urldate = {2024-06-02},
  volume = {50},
  year = {2023},
  journal = {European journal of nuclear medicine and molecular imaging}
}

@article{cook_2025,
title = {The impact of long axial field of view (LAFOV) PET on oncologic imaging},
journal = {European Journal of Radiology},
volume = {183},
pages = {111873},
year = {2025},
issn = {0720-048X},
doi = {https://doi.org/10.1016/j.ejrad.2024.111873},
url = {https://www.sciencedirect.com/science/article/pii/S0720048X24005898},
author = {Gary J.R. Cook and Ian L. Alberts and Thomas Wagner and B.Malene Fischer and Muhummad Sohaib Nazir and David Lilburn},
keywords = {Total body positron emission tomography, Long axial field of view positron emission tomography, Cancer imaging, Cardiovascular imaging},
abstract = {The development of long axial field of view (LAFOV) positron emission tomography coupled with computed tomography (PET/CT) scanners might be considered the biggest step forward in PET imaging since it became a mainstream clinical modality. Despite increased capital and maintenance costs and data storage requirements, the improvement in image quality, significantly faster acquisition times and lower radiopharmaceutical administered activities, allow a high quality and more efficient clinical service. This step change in technology overcomes some of the limitations of standard short axial field of view scanners. It allows simultaneous imaging of all body systems, and with the ability to obtain high temporal resolution data, it increases potential research applications, particularly in multisystem disease or for dosimetry measurements of novel radiopharmaceuticals. The improvements in sensitivity and signal-to-noise facilitates the use of tracers with long half-lives and low administered activity (e.g. [89Zr]-labelled monoclonal antibodies) or very short half-lives (e.g. [82Rb]), opening up applications that hitherto have been challenging. It is early in the evolution of LAFOV PET/CT and the advantages these systems offer have still to be fully realised in providing additional impact in clinical practice. In this article we describe the potential advantages of LAFOV PET technology and some of the clinical and research applications where it has been applied as well as some of the future developments that may enhance the modality further.}
}

@article{mazurowski_2023_segment,
  author = {Mazurowski, Maciej A. and Dong, Haoyu and Gu, Hanxue and Yang, Jichen and Konz, Nicholas and Zhang, Yixin},
  month = {10},
  pages = {102918},
  title = {Segment anything model for medical image analysis: An experimental study},
  doi = {10.1016/j.media.2023.102918},
  url = {https://www.sciencedirect.com/science/article/pii/S1361841523001780},
  urldate = {2023-08-28},
  volume = {89},
  year = {2023},
  journal = {Medical Image Analysis}
}

@misc{cheng_2023_sam,
  author = {Cheng, Dongjie and Qin, Ziyuan and Jiang, Zekun and Zhang, Shaoting and Lao, Qicheng and Li, Kang},
  title = {SAM on Medical Images: A Comprehensive Study on Three Prompt Modes},
  url = {https://arxiv.org/abs/2305.00035},
  year = {2023},
  organization = {arXiv.org}
}

@misc{wang_2024_promise,
  author = {Wang, Jinfeng and Song, Sifan and Wang, Xinkun and Wang, Yiyi and Miao, Yiyi and Su, Jionglong and Kevin, Zhou S},
  title = {ProMISe: Promptable Medical Image Segmentation using SAM},
  url = {https://arxiv.org/abs/2403.04164},
  urldate = {2026-01-22},
  year = {2024},
  organization = {arXiv.org}
}

@article{huang_2024_segment,
  author = {Huang, Yuhao and Yang, Xin and Liu, Lian and Han, Zhiwu and Ao, Chang and Zhou, Xinrui and Chen, Rusi and Yu, Junxuan and Chen, Jiongquan and Chen, Chaoyu and Liu, Sijing and Chi, Haozhe and Hu, Xindi and Yue, Kejuan and Li, Lei and Grau, Vicente and Fan, Deng-Ping and Dong, Fajin and Ni, Dong},
  month = {02},
  pages = {103061-103061},
  publisher = {Elsevier BV},
  title = {Segment anything model for medical images?},
  doi = {10.1016/j.media.2023.103061},
  volume = {92},
  year = {2024},
  journal = {Medical Image Analysis}
}

@article{tang_2021_selfsupervised,
  author = {Tang, Yucheng and Yang, Dong and Li, Wenqi and Roth, Holger and Landman, Bennett and Xu, Daguang and Nath, Vishwesh and Hatamizadeh, Ali},
  month = {11},
  title = {Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysis},
  url = {https://arxiv.org/abs/2111.14791},
  year = {2021},
  journal = {arXiv:2111.14791 [cs]}


@misc{zhou_2022_ibot,
  author = {Zhou, Jinghao and Wei, Chen and Wang, Huiyu and Shen, Wei and Xie, Cihang and Yuille, Alan and Kong, Tao},
  month = {01},
  title = {iBOT: Image BERT Pre-Training with Online Tokenizer},
  doi = {10.48550/arXiv.2111.07832},
  url = {https://arxiv.org/abs/2111.07832},
  year = {2022},
  organization = {arXiv.org}
}

@misc{sablayrolles_2018_koleo,
  author = {Sablayrolles, Alexandre and Douze, Matthijs and Schmid, Cordelia and Jégou, Hervé},
  title = {Spreading vectors for similarity search},
  url = {https://arxiv.org/abs/1806.03198},
  year = {2018},
  organization = {arXiv.org}
}