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• Journal article
  Liu Y, Li Y, Jiang M, Wang S, Ye S, Walsh S, Yang Get al., 2024,

  SOCR-YOLO : Small Objects Detection Algorithm in Medical Images

  , INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY, Vol: 34, ISSN: 0899-9457
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• Journal article
  Zhong W, Zhang H, Gao Z, Hau WK, Yang G, Liu X, Xu Let al., 2024,

  Distraction-aware hierarchical learning for vascular structure segmentation in intravascular ultrasound images

  , COMPUTERIZED MEDICAL IMAGING AND GRAPHICS, Vol: 115, ISSN: 0895-6111
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• Journal article
  Liu S, Li X, Zhang Y, Deng Y, Li Z, Zhu Y, Li X, Shang Y, Yang G, Zhan X, Li Y, Ren Het al., 2024,

  A bibliometric study of the intellectual base and global research hotspots for single-cell sequencing [2009-2022] in breast cancer

  , HELIYON, Vol: 10
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• Journal article
  Wang C, Lyu J, Wang S, Qin C, Guo K, Zhang X, Yu X, Li Y, Wang F, Jin J, Shi Z, Xu Z, Tian Y, Hua S, Chen Z, Liu M, Sun M, Kuang X, Wang K, Wang H, Li H, Chu Y, Yang G, Bai W, Zhuang X, Wang H, Qin J, Qu Xet al., 2024,

  CMRxRecon: A publicly available <i>k</i>-space dataset and benchmark to advance deep learning for cardiac MRI

  , SCIENTIFIC DATA, Vol: 11
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• Journal article
  Shi Z, Jiang M, Li Y, Wei B, Wang Z, Wu Y, Tan T, Yang Get al., 2024,

  MLC: Multi-level consistency learning for semi-supervised left atrium segmentation

  , EXPERT SYSTEMS WITH APPLICATIONS, Vol: 244, ISSN: 0957-4174
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• Journal article
  Wang G, Li W, Zhou M, Zhu H, Yang G, Yap CHet al., 2024,

  4D foetal cardiac ultrasound image detection based on deep learning with weakly supervised localisation for rapid diagnosis of evolving hypoplastic left heart syndrome

  , CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY, ISSN: 2468-6557
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• Journal article
  Shatalina E, Onwordi EC, Whitehurst T, Whittington A, Mansur A, Arumuham A, Ben S, Berry A, Marques TR, Gunn RN, Natesan S, Nour MM, Rabiner EA, Wall MB, Howes ODet al., 2024,

  The relationship between SV2A levels, neural activity, and cognitive function in healthy humans: A [11C]UCB-J PET and fMRI study

  , IMAGING NEUROSCIENCE, Vol: 2
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  • Citations: 1
• Journal article
  Huo Z, Wen K, Luo Y, Neji R, Kunze KP, Ferreira PF, Pennell DJ, Scott AD, Nielles-Vallespin Set al., 2024,

  Referenceless Nyquist ghost correction outperforms standard navigator-based method and improves efficiency of in vivo diffusion tensor cardiovascular magnetic resonance

  , Magnetic Resonance in Medicine, Vol: 91, Pages: 2403-2416, ISSN: 0740-3194

  PURPOSE: The study aims to assess the potential of referenceless methods of EPI ghost correction to accelerate the acquisition of in vivo diffusion tensor cardiovascular magnetic resonance (DT-CMR) data using both computational simulations and data from in vivo experiments. METHODS: Three referenceless EPI ghost correction methods were evaluated on mid-ventricular short axis DT-CMR spin echo and STEAM datasets from 20 healthy subjects at 3T. The reduced field of view excitation technique was used to automatically quantify the Nyquist ghosts, and DT-CMR images were fit to a linear ghost model for correction. RESULTS: Numerical simulation showed the singular value decomposition (SVD) method is the least sensitive to noise, followed by Ghost/Object method and entropy-based method. In vivo experiments showed significant ghost reduction for all correction methods, with referenceless methods outperforming navigator methods for both spin echo and STEAM sequences at b = 32, 150, 450, and 600   smm - 2 $$ {\mathrm{smm}}^{-2} $$ . It is worth noting that as the strength of the diffusion encoding increases, the performance gap between the referenceless method and the navigator-based method diminishes. CONCLUSION: Referenceless ghost correction effectively reduces Nyquist ghost in DT-CMR data, showing promise for enhancing the accuracy and efficiency of measurements in clinical practice without the need for any additional reference scans.

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• Journal article
  Nan Y, Ser JD, Tang Z, Tang P, Xing X, Fang Y, Herrera F, Pedrycz W, Walsh S, Yang Get al., 2024,

  Fuzzy Attention Neural Network to Tackle Discontinuity in Airway Segmentation

  , IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, Vol: 35, Pages: 7391-7404, ISSN: 2162-237X
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  • Citations: 41
• Journal article
  Boudreau M, Karakuzu A, Cohen-Adad J, Bozkurt E, Carr M, Castellaro M, Concha L, Doneva M, Dual SA, Ensworth A, Foias A, Fortier V, Gabr RE, Gilbert G, Glide-Hurst CK, Grech-Sollars M, Hu S, Jalnefjord O, Jovicich J, Keskin K, Koken P, Kolokotronis A, Kukran S, Lee NG, Levesque IR, Li B, Ma D, Maedler B, Maforo NG, Near J, Pasaye E, Ramirez-Manzanares A, Statton B, Stehning C, Tambalo S, Tian Y, Wang C, Weiss K, Zakariaei N, Zhang S, Zhao Z, Stikov Net al., 2024,

  Repeat it without me: Crowdsourcing the T₁ mapping common ground via the ISMRM reproducibility challenge

  , MAGNETIC RESONANCE IN MEDICINE, ISSN: 0740-3194
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