MRI/S

      Publications

      • Asmail Muftah, SM Shermer, Frank C Langbein. Texture Feature Analysis for Classification of Early-Stage Prostate Cancer in mpMRI. Proc AI in Healthcare (AIiH), Swansea, UK, September 2024.

        [Details]

      • E Alwadee, X Sun, Y Qin, FC Langbein. Assessing and Enhancing the Robustness of Brain Tumor Segmentation using a Probabilistic Deep Learning Architecture. Proc ISMRM and ISMRT Annual Meeting and Exhibition, Singapore, May 2024.


        [Details]

      • E Alwadee, X Sun, Y Qin, FC Langbein. LATUP-Net: A Lightweight 3D Attention U-Net with Parallel Convolutions for Brain Tumor Segmentation. Preprint, 2024.

        [Details]

      • M. Chandler, C. Jenkins, S. M. Shermer, F. C. Langbein. MRSNet: Metabolite Quantification from Edited Magnetic Resonance Spectra With Convolutional Neural Network. Submitted, 2019.

        [Details]

      • C. Jenkins, M. Chandler, F. C. Langbein, S. M. Shermer. Benchmarking GABA Quantification: A Ground Truth Data Set and Comparative Analysis of TARQUIN, LCModel, jMRUI and Gannet. Submitted, 2021.

        [Details]

      • Christopher Jenkins, Max Chandler, Frank Langbein, Sophie Shermer. Quantification of edited magnetic resonance spectroscopy: a comparative phantom based study of analysis methods. ISMRM 27th Annual Meeting & Exhibition, Montréal, QC, Canada, 11th-16th May 2019.

        [Details]

      Presentations

      • E Alwadee, X Sun, Y Qin, FC Langbein. Assessing and Enhancing the Robustness of Brain Tumor Segmentation using a Probabilistic Deep Learning Architecture. Proc ISMRM and ISMRT Annual Meeting and Exhibition, Singapore, May 2024.


        [Details]

      • Frank C Langbein. Metabolite Quantification with AI from MR Spectra. Cardiff/University of Chinese Academy of Sciences (UCAS) Workshop on Visual Computing, 29th January 2024.

        [Details]

      • Zien Ma, Oktay Karakus, Sophie Shermer, Frank Langbein. Quantification of Metabolites in Magnetic Resonance Spectra with Deep Learning: Insights on Simulated and Real Data. Presentation at the One Day Meeting: Synthetic Data for Machine Learning, The British Machine Vision Association and Society for Pattern Recognition, Wednesday 8 November 2023.

        [Details]

      • FC Langbein. Control and Machine Learning for Magnetic Resonance Spectroscopy. Keynote talk, Frontiers of Intelligent Computing: Theory and Applications (FICTA), 11-12 April 2023.

        [Details]

      • S Schirmer, F Langbein, C Jenkins, M Chandler. Design of novel MRI pulse sequences for GABA quantification using optimal control. In: 4th Int Symp on MRS of GABA, 2017.

        [Details]

      • C Jenkins, M Chandler, F Langbein, S Schirmer. Modelling, Optimization and QA for Magnetic Resonance Spectroscopy. All Wales Medical Physics and Engineering Summer Meeting, short talk and poster, 2017.

        [Details]

      • M Chandler, F Langbein, C Jenkins, S Schirmer. Quantum Control for Magnetic Resonance Spectroscopy. All Wales Medical Physics and Engineering Summer Meeting, poster, 2017.

        [Details]

      Codes

      Data Sets