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Ilicak E, Thater G, Ozdemir S, Zapp J, Schad LR, Schoenberg SO, Zöllner FG, Weis M. Functional lung imaging of 2-year-old children after congenital diaphragmatic hernia repair using dynamic mode decomposition MRI. Eur Radiol 2024; 34:3761-3772. [PMID: 37940710 PMCID: PMC11166761 DOI: 10.1007/s00330-023-10335-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 09/07/2023] [Accepted: 09/14/2023] [Indexed: 11/10/2023]
Abstract
OBJECTIVES To investigate the feasibility of non-contrast-enhanced functional lung imaging in 2-year-old children after congenital diaphragmatic hernia (CDH) repair. METHODS Fifteen patients after CDH repair were examined using non-contrast-enhanced dynamic magnetic resonance imaging (MRI). For imaging two protocols were used during free-breathing: Protocol A with high temporal resolution and Protocol B with high spatial resolution. The dynamic images were then analysed through a recently developed post-processing method called dynamic mode decomposition (DMD) to obtain ventilation and perfusion maps. The ventilation ratios (VRatio) and perfusion ratios (QRatio) of ipsilateral to contralateral lung were compared to evaluate functional differences. Lastly, DMD MRI-based perfusion results were compared with perfusion parameters obtained using dynamic contrast-enhanced (DCE) MRI to assess agreement between methods. RESULTS Both imaging protocols successfully generated pulmonary ventilation (V) and perfusion (Q) maps in all patients. Overall, the VRatio and QRatio values were 0.84 ± 0.19 and 0.70 ± 0.24 for Protocol A, and 0.88 ± 0.18 and 0.72 ± 0.23 for Protocol B, indicating reduced ventilation ( p < 0.05 ) and perfusion ( p < 0.01 ) on the ipsilateral side. Moreover, there is a very strong positive correlation ( r > 0.89 , p < 0.01 ) and close agreement between DMD MRI-based perfusion values and DCE MRI-based perfusion parameters. CONCLUSIONS DMD MRI can obtain pulmonary functional information in 2-year-old CDH patients. The results obtained with DMD MRI correlate with DCE MRI, without the need for ionising radiation or exposure to contrast agents. While further studies with larger cohorts are warranted, DMD MRI is a promising option for functional lung imaging in CDH patients. CLINICAL RELEVANCE STATEMENT We demonstrate that pulmonary ventilation and perfusion information can be obtained in 2-year-old patients after CDH repair, without the need for ionising radiation or contrast agents by utilising non-contrast-enhanced MRI acquisitions together with dynamic mode decomposition analysis. KEY POINTS • Non-contrast-enhanced functional MR imaging is a promising option for functional lung imaging in 2-year-old children after congenital diaphragmatic hernia. • DMD MRI can generate pulmonary ventilation and perfusion maps from free-breathing dynamic acquisitions without the need for ionising radiation or contrast agents. • Lung perfusion parameters obtained with DMD MRI correlate with perfusion parameters obtained using dynamic contrast-enhanced MRI.
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Affiliation(s)
- Efe Ilicak
- Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.
- Mannheim Institute for Intelligent Systems in Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.
| | - Greta Thater
- Department of Clinical Radiology and Nuclear Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Safa Ozdemir
- Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Mannheim Institute for Intelligent Systems in Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Jascha Zapp
- Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Mannheim Institute for Intelligent Systems in Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Lothar R Schad
- Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Mannheim Institute for Intelligent Systems in Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Stefan O Schoenberg
- Department of Clinical Radiology and Nuclear Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Frank G Zöllner
- Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.
- Mannheim Institute for Intelligent Systems in Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.
| | - Meike Weis
- Department of Clinical Radiology and Nuclear Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.
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Liu C, Liu H, Li Y, Xiao Z, Wang Y, Guo H, Luo J. Establishing a 4D-CT lung function related volumetric dose model to reduce radiation pneumonia. Sci Rep 2024; 14:12589. [PMID: 38824238 PMCID: PMC11144207 DOI: 10.1038/s41598-024-63251-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Accepted: 05/27/2024] [Indexed: 06/03/2024] Open
Abstract
In order to study how to use pulmonary functional imaging obtained through 4D-CT fusion for radiotherapy planning, and transform traditional dose volume parameters into functional dose volume parameters, a functional dose volume parameter model that may reduce level 2 and above radiation pneumonia was obtained. 41 pulmonary tumor patients who underwent 4D-CT in our department from 2020 to 2023 were included. MIM Software (MIM 7.0.7; MIM Software Inc., Cleveland, OH, USA) was used to register adjacent phase CT images in the 4D-CT series. The three-dimensional displacement vector of CT pixels was obtained when changing from one respiratory state to another respiratory state, and this three-dimensional vector was quantitatively analyzed. Thus, a color schematic diagram reflecting the degree of changes in lung CT pixels during the breathing process, namely the distribution of ventilation function strength, is obtained. Finally, this diagram is fused with the localization CT image. Select areas with Jacobi > 1.2 as high lung function areas and outline them as fLung. Import the patient's DVH image again, fuse the lung ventilation image with the localization CT image, and obtain the volume of fLung different doses (V60, V55, V50, V45, V40, V35, V30, V25, V20, V15, V10, V5). Analyze the functional dose volume parameters related to the risk of level 2 and above radiation pneumonia using R language and create a predictive model. By using stepwise regression and optimal subset method to screen for independent variables V35, V30, V25, V20, V15, and V10, the prediction formula was obtained as follows: Risk = 0.23656-0.13784 * V35 + 0.37445 * V30-0.38317 * V25 + 0.21341 * V20-0.10209 * V15 + 0.03815 * V10. These six independent variables were analyzed using a column chart, and a calibration curve was drawn using the calibrate function. It was found that the Bias corrected line and the Apparent line were very close to the Ideal line, The consistency between the predicted value and the actual value is very good. By using the ROC function to plot the ROC curve and calculating the area under the curve: 0.8475, 95% CI 0.7237-0.9713, it can also be determined that the accuracy of the model is very high. In addition, we also used Lasso method and random forest method to filter out independent variables with different results, but the calibration curve drawn by the calibration function confirmed poor prediction performance. The function dose volume parameters V35, V30, V25, V20, V15, and V10 obtained through 4D-CT are key factors affecting radiation pneumonia. Establishing a predictive model can provide more accurate lung restriction basis for clinical radiotherapy planning.
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Affiliation(s)
- Chunmei Liu
- Department of Radiation Oncology, The Second Hospital of Hebei Medical University, 215 West Heping Road, Shijiazhuang, 050000, Hebei, China
| | - Huizhi Liu
- Department of Radiation Oncology, The Second Hospital of Hebei Medical University, 215 West Heping Road, Shijiazhuang, 050000, Hebei, China
| | - Yange Li
- Department of Radiation Oncology, The Second Hospital of Hebei Medical University, 215 West Heping Road, Shijiazhuang, 050000, Hebei, China
| | - Zhiqing Xiao
- Department of Radiation Oncology, The Second Hospital of Hebei Medical University, 215 West Heping Road, Shijiazhuang, 050000, Hebei, China
| | - Yanqiang Wang
- Department of Radiation Oncology, The Second Hospital of Hebei Medical University, 215 West Heping Road, Shijiazhuang, 050000, Hebei, China
| | - Han Guo
- Department of Radiation Oncology, The Second Hospital of Hebei Medical University, 215 West Heping Road, Shijiazhuang, 050000, Hebei, China
| | - Jianmin Luo
- Department of Hematology, The Second Hospital of Hebei Medical University, 215 West Heping Road, Shijiazhuang, 050000, Hebei, China.
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Moher Alsady T, Ruschepaul J, Voskrebenzev A, Klimes F, Poehler GH, Vogel-Claussen J. Estimating ventilation correlation coefficients in the lungs using PREFUL-MRI in chronic obstructive pulmonary disease patients and healthy adults. Magn Reson Med 2024; 91:2142-2152. [PMID: 38217450 DOI: 10.1002/mrm.29982] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2023] [Revised: 11/14/2023] [Accepted: 12/01/2023] [Indexed: 01/15/2024]
Abstract
PURPOSE Various parameters of regional lung ventilation can be estimated using phase-resolved functional lung (PREFUL)-MRI. The parameter "ventilation correlation coefficient (Vent-CC)" was shown advantageous because it assesses the dynamics of regional air flow. Calculating Vent-CC depends on a voxel-wise comparison to a healthy reference flow curve. This work examines the effect of placing a reference region of interest (ROI) in various lung quadrants or in different coronal slices. Furthermore, algorithms for automated ROI selection are presented and compared in terms of test-retest repeatability. METHODS Twenty-eight healthy subjects and 32 chronic obstructive pulmonary disease (COPD) patients were scanned twice using PREFUL-MRI. Retrospective analyses examined the homogeneity of air flow curves of various reference ROIs using cross-correlation. Vent-CC and ventilation defect percentage (VDP) calculated using various reference ROIs were compared using one-way analysis of variance (ANOVA). The coefficient of variation was calculated for Vent-CC and VDP when using different reference selection algorithms. RESULTS Flow-volume curves were highly correlated between ROIs placed at various lung quadrants in the same coronal slice (r > 0.97) with no differences in Vent-CC and VDP (ANOVA: p > 0.5). However, ROIs placed at different coronal slices showed lower correlation coefficients and resulted in significantly different Vent-CC and VDP values (ANOVA: p < 0.001). Vent-CC and VDP showed higher repeatability when calculated using the presented new algorithm. CONCLUSION In COPD and healthy cohorts, assessing regional ventilation dynamics using PREFUL-MRI in terms of the Vent-CC metric showed higher repeatability using a new algorithm for selecting a homogenous reference ROI from the same slice.
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Affiliation(s)
- Tawfik Moher Alsady
- Institute for Diagnostic and Interventional Radiology, Hannover Medical School, Hannover, Lower Saxony, Germany
- Biomedical Research in End-Stage and Obstructive Lung Disease (BREATH), German Center for Lung Research, Hannover, Lower Saxony, Germany
| | - Jakob Ruschepaul
- Institute for Diagnostic and Interventional Radiology, Hannover Medical School, Hannover, Lower Saxony, Germany
- Biomedical Research in End-Stage and Obstructive Lung Disease (BREATH), German Center for Lung Research, Hannover, Lower Saxony, Germany
| | - Andreas Voskrebenzev
- Institute for Diagnostic and Interventional Radiology, Hannover Medical School, Hannover, Lower Saxony, Germany
- Biomedical Research in End-Stage and Obstructive Lung Disease (BREATH), German Center for Lung Research, Hannover, Lower Saxony, Germany
| | - Filip Klimes
- Institute for Diagnostic and Interventional Radiology, Hannover Medical School, Hannover, Lower Saxony, Germany
- Biomedical Research in End-Stage and Obstructive Lung Disease (BREATH), German Center for Lung Research, Hannover, Lower Saxony, Germany
| | - Gesa Helen Poehler
- Institute for Diagnostic and Interventional Radiology, Hannover Medical School, Hannover, Lower Saxony, Germany
- Biomedical Research in End-Stage and Obstructive Lung Disease (BREATH), German Center for Lung Research, Hannover, Lower Saxony, Germany
| | - Jens Vogel-Claussen
- Institute for Diagnostic and Interventional Radiology, Hannover Medical School, Hannover, Lower Saxony, Germany
- Biomedical Research in End-Stage and Obstructive Lung Disease (BREATH), German Center for Lung Research, Hannover, Lower Saxony, Germany
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Klimeš F, Obert AJ, Scheller J, Wernz MM, Voskrebenzev A, Gutberlet M, Grimm R, Suhling H, Müller RA, Kaireit TF, Glandorf J, Moher Alsady T, Wacker F, Vogel-Claussen J. Comparison of Free-Breathing 3D Phase-Resolved Functional Lung (PREFUL) MRI With Dynamic 19 F Ventilation MRI in Patients With Obstructive Lung Disease and Healthy Volunteers. J Magn Reson Imaging 2024. [PMID: 38214459 DOI: 10.1002/jmri.29221] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 12/20/2023] [Accepted: 12/20/2023] [Indexed: 01/13/2024] Open
Abstract
BACKGROUND Non-contrast-enhanced 1 H magnetic resonance imaging (MRI) with full lung coverage shows promise for assessment of regional lung ventilation but a comparison with direct ventilation measurement using 19 F MRI is lacking. PURPOSE To compare ventilation parameters calculated using 3D phase-resolved functional lung (PREFUL) MRI with 19 F MRI. STUDY TYPE Prospective. POPULATION Fifteen patients with asthma, 14 patients with chronic obstructive lung disease, and 13 healthy volunteers. FIELD STRENGTH/SEQUENCE A 3D gradient-echo pulse sequence with golden-angle increment and stack-of-stars encoding at 1.5 T. ASSESSMENT All participants underwent 3D PREFUL MRI and 19 F MRI. For 3D PREFUL, static regional ventilation (RVent) and dynamic flow-volume cross-correlation metric (FVL-CM) were calculated. For both parameters, ventilation defect percentage (VDP) values and ventilation defect (VD) maps (including a combination of both parameters [VDPCombined ]) were determined. For 19 F MRI, images from eight consecutive breaths under volume-controlled inhalation of perfluoropropane were acquired. Time-to-fill (TTF) and wash-in (WI) parameters were extracted. For all 19 F parameters, a VD map was generated and the corresponding VDP values were calculated. STATISTICAL TESTS For all parameters, the relationship between the two techniques was assessed using a Spearman correlation (r). Differences between VDP values were compared using Bland-Altman analysis. For regional comparison of VD maps, spatial overlap and Sørensen-Dice coefficients were computed. RESULTS 3D PREFUL VDP values were significantly correlated to VDP measures by 19 F (r range: 0.59-0.70). For VDPRVent , no significant bias was observed with VDP of the third and fourth breath (bias range = -6.8:7.7%, P range = 0.25:0.30). For VDPFVL-CM , no significant bias was found with VDP values of fourth-eighth breaths (bias range = -2.0:12.5%, P range = 0.12:0.75). The overall spatial overlap of all VD maps increased with each breath, ranging from 61% to 81%, stabilizing at the fourth breath. DATA CONCLUSION 3D PREFUL MRI parameters showed moderate to strong correlation with 19 F MRI. Depending on the 3D PREFUL VD map, the best regional agreement was found to 19 F VD maps of third-fifth breath. LEVEL OF EVIDENCE 3 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Filip Klimeš
- Institute of Diagnostic and Interventional Radiology, Hannover Medical School, Hanover, Germany
- Biomedical Research in Endstage and Obstructive Lung Disease Hannover (BREATH), Member of the German Centre for Lung Research, Hanover, Germany
| | - Arnd J Obert
- Department of Radiation Oncology, University Hospital Würzburg, Würzburg, Germany
| | - Julienne Scheller
- Institute of Diagnostic and Interventional Radiology, Hannover Medical School, Hanover, Germany
- Biomedical Research in Endstage and Obstructive Lung Disease Hannover (BREATH), Member of the German Centre for Lung Research, Hanover, Germany
| | - Marius M Wernz
- Institute of Diagnostic and Interventional Radiology, Hannover Medical School, Hanover, Germany
- Biomedical Research in Endstage and Obstructive Lung Disease Hannover (BREATH), Member of the German Centre for Lung Research, Hanover, Germany
| | - Andreas Voskrebenzev
- Institute of Diagnostic and Interventional Radiology, Hannover Medical School, Hanover, Germany
- Biomedical Research in Endstage and Obstructive Lung Disease Hannover (BREATH), Member of the German Centre for Lung Research, Hanover, Germany
| | - Marcel Gutberlet
- Institute of Diagnostic and Interventional Radiology, Hannover Medical School, Hanover, Germany
- Biomedical Research in Endstage and Obstructive Lung Disease Hannover (BREATH), Member of the German Centre for Lung Research, Hanover, Germany
| | - Robert Grimm
- MR Application Predevelopment, Siemens Healthineers AG, Erlangen, Germany
| | - Hendrik Suhling
- Biomedical Research in Endstage and Obstructive Lung Disease Hannover (BREATH), Member of the German Centre for Lung Research, Hanover, Germany
- Department of Respiratory Medicine, Hannover Medical School, Hanover, Germany
| | - Robin A Müller
- Institute of Diagnostic and Interventional Radiology, Hannover Medical School, Hanover, Germany
- Biomedical Research in Endstage and Obstructive Lung Disease Hannover (BREATH), Member of the German Centre for Lung Research, Hanover, Germany
| | - Till F Kaireit
- Institute of Diagnostic and Interventional Radiology, Hannover Medical School, Hanover, Germany
- Biomedical Research in Endstage and Obstructive Lung Disease Hannover (BREATH), Member of the German Centre for Lung Research, Hanover, Germany
| | - Julian Glandorf
- Institute of Diagnostic and Interventional Radiology, Hannover Medical School, Hanover, Germany
- Biomedical Research in Endstage and Obstructive Lung Disease Hannover (BREATH), Member of the German Centre for Lung Research, Hanover, Germany
| | - Tawfik Moher Alsady
- Institute of Diagnostic and Interventional Radiology, Hannover Medical School, Hanover, Germany
- Biomedical Research in Endstage and Obstructive Lung Disease Hannover (BREATH), Member of the German Centre for Lung Research, Hanover, Germany
| | - Frank Wacker
- Institute of Diagnostic and Interventional Radiology, Hannover Medical School, Hanover, Germany
- Biomedical Research in Endstage and Obstructive Lung Disease Hannover (BREATH), Member of the German Centre for Lung Research, Hanover, Germany
| | - Jens Vogel-Claussen
- Institute of Diagnostic and Interventional Radiology, Hannover Medical School, Hanover, Germany
- Biomedical Research in Endstage and Obstructive Lung Disease Hannover (BREATH), Member of the German Centre for Lung Research, Hanover, Germany
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Ilicak E, Ozdemir S, Zapp J, Schad LR, Zöllner FG. Dynamic mode decomposition of dynamic MRI for assessment of pulmonary ventilation and perfusion. Magn Reson Med 2023; 90:761-769. [PMID: 36989180 DOI: 10.1002/mrm.29656] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 03/03/2023] [Accepted: 03/15/2023] [Indexed: 03/30/2023]
Abstract
PURPOSE To introduce dynamic mode decomposition (DMD) as a robust alternative for the assessment of pulmonary functional information from dynamic non-contrast-enhanced acquisitions. METHODS Pulmonary fractional ventilation and normalized perfusion maps were obtained using DMD from simulated phantoms as well as in vivo dynamic acquisitions of healthy volunteers at 1.5T. The performance of DMD was compared with conventional Fourier decomposition (FD) and matrix pencil (MP) methods in estimating functional map values. The proposed method was evaluated based on estimated signal amplitude in functional maps across varying number of measurements. RESULTS Quantitative assessments performed on phantoms and in vivo measurements indicate that DMD is capable of successfully obtaining pulmonary functional maps. Specifically, compared to FD and MP methods, DMD is able to reduce variations in estimated amplitudes across different number of measurements. This improvement is evident in the fractional ventilation and normalized perfusion maps obtain from phantom simulations with frequency variations and noise, as well as in the maps obtained from in vivo measurements. CONCLUSIONS A robust method for accurately estimating pulmonary ventilation and perfusion related signal changes in dynamic acquisitions is presented. The proposed method uses DMD to obtain functional maps reliably, while reducing amplitude variations caused by differences in number of measurements.
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Affiliation(s)
- Efe Ilicak
- Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Mannheim Institute for Intelligent Systems in Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Safa Ozdemir
- Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Mannheim Institute for Intelligent Systems in Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Jascha Zapp
- Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Mannheim Institute for Intelligent Systems in Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Lothar R Schad
- Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Mannheim Institute for Intelligent Systems in Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Frank G Zöllner
- Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Mannheim Institute for Intelligent Systems in Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
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