Volume-Controlled 19 F MR Ventilation Imaging of Fluorinated Gas.

BACKGROUND: 19 F MRI of inhaled gas tracers has developed into a promising tool for pulmonary diagnostics. Prior to clinical use, the intersession repeatability of acquired ventilation parameters must be quantified and maximized. PURPOSE: To evaluate repeatability of static and dynamic 19 F ventilation parameters and correlation with predicted forced expiratory volume in 1 second (FEV1 %pred) with and without inspiratory volume control. STUDY TYPE: Prospective. POPULATION: A total of 30 healthy subjects and 26 patients with chronic obstructive pulmonary disease (COPD). FIELD STRENGTH/SEQUENCE: Three-dimensional (3D) gradient echo pulse sequence with golden-angle stack-of-stars k-space encoding at 1.5 T. ASSESSMENT: All study participants underwent 19 F ventilation MRI over eight breaths with inspiratory volume control (w VC) and without inspiratory volume control (w/o VC), which was repeated within 1 week. Ventilated volume percentage (VVP), fractional ventilation (FV), and wash-in time (WI) were computed. Lung function testing was conducted on the first visit. STATISTICAL TESTS: Correlation between imaging and FEV1 %pred was measured using Pearson correlation coefficient (r). Differences in imaging parameters between first and second visit were analyzed using paired t-test. Repeatability was quantified using intraclass correlation coefficient (ICC) and coefficient of variation (CoV). Minimum detectable effect size (MDES) was calculated with a power analysis for study size n = 30 and a power of 0.8. All hypotheses were tested with a significance level of 5% two sided. RESULTS: Strong and moderate linear correlations with FEV1 %pred for COPD patients were found in almost all imaging parameters. The ICC w VC exceeds the ICC w/o VC for all imaging parameters. CoV was significantly lower w VC for initial VVP in COPD patients, FV, CoV FV, WI and standard deviation (SD) of WI. MDES of all imaging parameters were smaller w VC. DATA CONCLUSION: 19 F gas wash-in MRI with inspiratory volume control increases the correlation and repeatability of imaging parameters with lung function testing. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 2.

3D phase-resolved functional lung ventilation MR imaging in healthy volunteers and patients with chronic pulmonary disease.

PURPOSE: To test the feasibility of 3D phase-resolved functional lung (PREFUL) MRI in healthy volunteers and patients with chronic pulmonary disease, to compare 3D to 2D PREFUL, and to investigate the required temporal resolution to obtain stable 3D PREFUL measurement. METHODS: Sixteen participants underwent MRI using 2D and 3D PREFUL. Retrospectively, the spatial resolution of 3D PREFUL (4 × 4 × 4 mm3 ) was decreased to match the spatial resolution of 2D PREFUL (4 × 4 × 15 mm3 ), abbreviated as 3Dlowres . In addition to regional ventilation (RVent), flow-volume loops were computed and rated by a cross-correlation (CC). Ventilation defect percentage (VDP) maps were obtained. RVent, CC, VDPRVent , and VDPCC were compared for systematic differences between 2D, 3Dlowres , and 3D PREFUL. Dividing the 3D PREFUL data into 4- (≈ 20 phases), 8- (≈ 40 phases), and 12-min (≈ 60 phases) acquisition pieces, the ventilation parameter maps, including the heterogeneity of ventilation time to peak, were tested regarding the required temporal resolution. RESULTS: RVent, CC, VDPRVent , and VDPCC  presented significant correlations between 2D and 3D PREFUL (r = 0.64-0.94). CC and VDPCC  of 2D and 3Dlowres  PREFUL were significantly different (P < .0113). Comparing 3Dlowres  and 3D PREFUL, all parameters were found to be statistically different (P < .0045). CONCLUSION: 3D PREFUL MRI depicts the whole lung volume and breathing cycle with superior image resolution and with likely more precision compared to 2D PREFUL. Furthermore, 3D PREFUL is more sensitive to detect regions of hypoventilation and ventilation heterogeneity compared to 3Dlowres  PREFUL, which is important for early detection and improved monitoring of patients with chronic lung disease.

Repeatability of dynamic 3D phase-resolved functional lung (PREFUL) ventilation MR Imaging in patients with chronic obstructive pulmonary disease and healthy volunteers.

BACKGROUND: A previous study has demonstrated the feasibility of 3D phase-resolved functional lung (PREFUL) MRI in healthy volunteers and patients with chronic pulmonary disease. Before clinical use, the repeatability of the ventilation parameters derived from 3D PREFUL MRI must be determined. PURPOSE: To evaluate repeatability of 3D PREFUL and to compare with pulmonary functional lung testing (PFT). STUDY TYPE: Prospective. POPULATION: Fifty-three healthy subjects and 13 patients with chronic obstructive pulmonary disease (COPD). FIELD STRENGTH/SEQUENCE: A prototype 3D stack-of-stars spoiled-gradient-echo sequence at 1.5 T. ASSESSMENT: Study participants underwent repeated MRI examination (median time interval between scans COPD/healthy subjects [interquartile range]: 7/0 days [6-8/0-0 days]) and one PFT carried out at the time of the baseline MRI. For 3D PREFUL, regional ventilation (RVent) and flow-volume loops were computed and rated by cross-correlation (CC). Also, ventilation time-to-peak (VTTP) was computed. Ventilation defect percentage (VDP) maps were obtained for RVent and CC. STATISTICAL TESTS: Repeatability of 3D PREFUL parameters was evaluated using Bland-Altman analysis, coefficient of variation (COV) and intraclass correlation coefficient (ICC). The relation between 3D PREFUL and PFT measures (forced expiratory volume in 1 second (FEV1 ) and forced vital capacity (FVC) was assessed using the Pearson correlation coefficient (r). RESULTS: In healthy subjects and COPD patients, no significant bias (all P range: 0.09-0.77) and a moderate to good repeatability of RVent, VTTP, and VDPRVent were found (COV range: 0.1%-18.2%, ICC range: 0.51-0.88). For CC and VDPCC moderate repeatability was found (COV range: 0.6%-43.6%, ICC: 0.38-0.60). CC, VDPRVent , and VDPCC showed a good correlation with FEV1 (all |r| > 0.58, all P < 0.05) and FEV1 /FVC ratio (all |r| > 0.62, all P < 0.05). DATA CONCLUSION: 3D PREFUL provided a good repeatability of RVent, VTTP, and VDPRVent and moderate repeatability of CC and VDPCC in healthy volunteers and COPD patients, and correlated well with FEV1 and FEV1 /FVC. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY STAGE: 2.

Artificially-generated consolidations and balanced augmentation increase performance of U-net for lung parenchyma segmentation on MR images.

PURPOSE: To improve automated lung segmentation on 2D lung MR images using balanced augmentation and artificially-generated consolidations for training of a convolutional neural network (CNN). MATERIALS AND METHODS: From 233 healthy volunteers and 100 patients, 1891 coronal MR images were acquired. Of these, 1666 images without consolidations were used to build a binary semantic CNN for lung segmentation and 225 images (187 without consolidations, 38 with consolidations) were used for testing. To increase CNN performance of segmenting lung parenchyma with consolidations, balanced augmentation was performed and artificially-generated consolidations were added to all training images. The proposed CNN (CNNBal/Cons) was compared to two other CNNs: CNNUnbal/NoCons-without balanced augmentation and artificially-generated consolidations and CNNBal/NoCons-with balanced augmentation but without artificially-generated consolidations. Segmentation results were assessed using Sørensen-Dice coefficient (SDC) and Hausdorff distance coefficient. RESULTS: Regarding the 187 MR test images without consolidations, the mean SDC of CNNUnbal/NoCons (92.1 ± 6% (mean ± standard deviation)) was significantly lower compared to CNNBal/NoCons (94.0 ± 5.3%, P = 0.0013) and CNNBal/Cons (94.3 ± 4.1%, P = 0.0001). No significant difference was found between SDC of CNNBal/Cons and CNNBal/NoCons (P = 0.54). For the 38 MR test images with consolidations, SDC of CNNUnbal/NoCons (89.0 ± 7.1%) was not significantly different compared to CNNBal/NoCons (90.2 ± 9.4%, P = 0.53). SDC of CNNBal/Cons (94.3 ± 3.7%) was significantly higher compared to CNNBal/NoCons (P = 0.0146) and CNNUnbal/NoCons (P = 0.001). CONCLUSIONS: Expanding training datasets via balanced augmentation and artificially-generated consolidations improved the accuracy of CNNBal/Cons, especially in datasets with parenchymal consolidations. This is an important step towards a robust automated postprocessing of lung MRI datasets in clinical routine.

Comparison of phase-resolved functional lung (PREFUL) MRI derived perfusion and ventilation parameters at 1.5T and 3T in healthy volunteers.

PURPOSE: The purpose of this study is to evaluate the influence of different field strengths on perfusion and ventilation parameters, SNR and CNR derived by PREFUL MRI using predefined sequence parameters. METHODS: Data sets of free breathing 2d FLASH lung MRI were acquired from 15 healthy subjects at 1.5T and 3T (Magnetom Avanto and Skyra, Siemens Healthcare, Erlangen, Germany) with a maximum period of 3 days in between. The processed functional parameters regional ventilation (RVent), perfusion (Q), quantified perfusion (QQuant), perfusion defect percentage (QDP), ventilation defect percentage (VDP) and ventilation-perfusion match (VQM) were compared for systematic differences. Signal- and contrast-to-noise ratio (SNR and CNR) of both acquisitions were analyzed. RESULTS: RVent, Q, VDP, SNR and CNR presented no significant differences between 1.5T and 3T. QQuant (1.5T vs. 3T, P = 0.04), and QDP (1.5T vs. 3T, P≤0.01) decreased significantly at 3T. Consequently, VQM increased significantly (1.5T vs. 3T, P≤0.01). Skewness and kurtosis of the Q-values increased significantly at 3T (P≤0.01). The mean Sørensen-Dice coefficients between both series were 0.91 for QDP and 0.94 for VDP. The Bland-Altman analysis of both series showed mean differences of 4.29% for QDP, 1.23% for VDP and -5.15% for VQM. Using the above-mentioned parameters for three-day repeatability at two different scanners and field strengths, the retrospective power calculation showed, that a sample size of 15 can detect differences of 3.7% for QDP, of 2.9% for VDP and differences of 2.6% for VQM. CONCLUSION: Significant differences in QDP may be related to field inhomogeneities, which is expressed by increasing skewness and kurtosis at 3T. QQuant reveals only poor reproducibility between 1.5T and 3T. RVent, Q, VDP, SNR and CNR were not altered significantly at the used sequence parameters. Healthy participants with minimal defects present high spatial agreement of QDP and VDP.