PURPOSE: To develop an image-processing pipeline for semiautomated (SA) and reproducible analysis of hyperpolarized gas lung ventilation and proton anatomical magnetic resonance imaging (MRI) scan pairs. To compare results from the software for total lung volume (TLV), ventilated volume (VV), and percentage lung ventilated volume (%VV) calculation to the current manual "basic" method and a K-means segmentation method. MATERIALS AND METHODS: Six patients were imaged with hyperpolarized 3 He and same-breath lung 1 H MRI at 1.5T and six other patients were scanned with hyperpolarized 129 Xe and separate-breath 1 H MRI. One expert observer and two users with experience in lung image segmentation carried out the image analysis. Spearman (R), Intraclass (ICC) correlations, Bland-Altman limits of agreement (LOA), and Dice Similarity Coefficients (DSC) between output lung volumes were calculated. RESULTS: When comparing values of %VV, agreement between observers improved using the SA method (mean; R = 0.984, ICC = 0.980, LOA = 7.5%) when compared to the basic method (mean; R = 0.863, ICC = 0.873, LOA = 14.2%) nonsignificantly (pR = 0.25, pICC = 0.25, and pLOA = 0.50 respectively). DSC of VV and TLV masks significantly improved (P < 0.01) using the SA method (mean; DSCVV = 0.973, DSCTLV = 0.980) when compared to the basic method (mean; DSCVV = 0.947, DSCTLV = 0.957). K-means systematically overestimated %VV when compared to both basic (mean overestimation = 5.0%) and SA methods (mean overestimation = 9.7%), and had poor agreement with the other methods (mean ICC; K-means vs. basic = 0.685, K-means vs. SA = 0.740). CONCLUSION: A semiautomated image processing software was developed that improves interobserver agreement and correlation of lung ventilation volume percentage when compared to the currently used basic method and provides more consistent segmentations than the K-means method. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:640-646.
PURPOSE: To develop an image-processing pipeline for semiautomated (SA) and reproducible analysis of hyperpolarized gas lung ventilation and proton anatomical magnetic resonance imaging (MRI) scan pairs. To compare results from the software for total lung volume (TLV), ventilated volume (VV), and percentage lung ventilated volume (%VV) calculation to the current manual "basic" method and a K-means segmentation method. MATERIALS AND METHODS: Six patients were imaged with hyperpolarized 3 He and same-breath lung 1 H MRI at 1.5T and six other patients were scanned with hyperpolarized 129 Xe and separate-breath 1 H MRI. One expert observer and two users with experience in lung image segmentation carried out the image analysis. Spearman (R), Intraclass (ICC) correlations, Bland-Altman limits of agreement (LOA), and Dice Similarity Coefficients (DSC) between output lung volumes were calculated. RESULTS: When comparing values of %VV, agreement between observers improved using the SA method (mean; R = 0.984, ICC = 0.980, LOA = 7.5%) when compared to the basic method (mean; R = 0.863, ICC = 0.873, LOA = 14.2%) nonsignificantly (pR = 0.25, pICC = 0.25, and pLOA = 0.50 respectively). DSC of VV and TLV masks significantly improved (P < 0.01) using the SA method (mean; DSCVV = 0.973, DSCTLV = 0.980) when compared to the basic method (mean; DSCVV = 0.947, DSCTLV = 0.957). K-means systematically overestimated %VV when compared to both basic (mean overestimation = 5.0%) and SA methods (mean overestimation = 9.7%), and had poor agreement with the other methods (mean ICC; K-means vs. basic = 0.685, K-means vs. SA = 0.740). CONCLUSION: A semiautomated image processing software was developed that improves interobserver agreement and correlation of lung ventilation volume percentage when compared to the currently used basic method and provides more consistent segmentations than the K-means method. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:640-646.
Authors: Marcus J Couch; Robert Thomen; Nikhil Kanhere; Raymond Hu; Felix Ratjen; Jason Woods; Giles Santyr Journal: J Cyst Fibros Date: 2019-03-25 Impact factor: 5.482
Authors: Joshua R Astley; Alberto M Biancardi; Paul J C Hughes; Helen Marshall; Laurie J Smith; Guilhem J Collier; James A Eaden; Nicholas D Weatherley; Matthew Q Hatton; Jim M Wild; Bilal A Tahir Journal: Sci Rep Date: 2022-06-22 Impact factor: 4.996
Authors: Paul J C Hughes; Laurie Smith; Ho-Fung Chan; Bilal A Tahir; Graham Norquay; Guilhem J Collier; Alberto Biancardi; Helen Marshall; Jim M Wild Journal: J Appl Physiol (1985) Date: 2018-11-09
Authors: Helen Marshall; Guilhem J Collier; Christopher S Johns; Ho-Fung Chan; Graham Norquay; Rod A Lawson; Jim M Wild Journal: J Magn Reson Imaging Date: 2018-09-29 Impact factor: 4.813
Authors: Benjamin J Pippard; Mary A Neal; Adam M Maunder; Kieren G Hollingsworth; Alberto Biancardi; Rod A Lawson; Holly Fisher; John N S Matthews; A John Simpson; Jim M Wild; Peter E Thelwall Journal: Magn Reson Med Date: 2021-01-28 Impact factor: 4.668
Authors: Grace T Mussell; Helen Marshall; Laurie J Smith; Alberto M Biancardi; Paul J C Hughes; David J Capener; Jody Bray; Andrew J Swift; Smitha Rajaram; Alison M Condliffe; Guilhem J Collier; Chris S Johns; Nick D Weatherley; Jim M Wild; Ian Sabroe Journal: ERJ Open Res Date: 2021-09-27