Deep Learning for Multi‐sequence MRI Lung Segmentation

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Implementable Deep Learning for Multi‐sequence Proton MRI Lung Segmentation: A Multi‐center, Multi‐vendor, and Multi‐disease Study

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Deep Learning for Multi‐sequence MRI Lung Segmentation

Implementable Deep Learning for Multi‐sequence Proton MRI Lung Segmentation: A Multi‐center, Multi‐vendor, and Multi‐disease Study

by Joshua R. Astley BEng, Alberto M. Biancardi PhD, Paul J. C. Hughes PhD, Helen Marshall PhD, Guilhem J. Collier PhD, Ho-Fung Chan PhD, Laura C. Saunders PhD, Laurie J. Smith PhD, Martin L. Brook MSc, Roger Thompson PhD, Sarah Rowland-Jones MD, Sarah Skeoch PhD, Stephen M. Bianchi PhD, Matthew Q. Hatton MD, Najib M. Rahman DPhil, Ling-Pei Ho PhD, Chris E. Brightling PhD, Louise V. Wain PhD, Amisha Singapuri BSc, Rachael A. Evans PhD, Alastair J. Moss PhD, Gerry P. McCann MD, Stefan Neubauer MD, Betty Raman DPhil, C-MORE/PHOSP-COVID Collaborative Group, Jim M. Wild PhD, Bilal A. Tahir PhD on behalf of the TRISTAN Consortium


 Journal of Magnetic Resonance Imaging 58, Nr. 4 (Oktober 2023): 1030–44. doi: 10.1002/jmri.28643

Abstract

Background
Recently, deep learning via convolutional neural networks (CNNs) has largely superseded conventional methods for proton (1H)-MRI lung segmentation. However, previous deep learning studies have utilized single-center data and limited acquisition parameters.

Purpose
Develop a generalizable CNN for lung segmentation in 1H-MRI, robust to pathology, acquisition protocol, vendor, and center.

Study type
Retrospective.

Population
A total of 809 1H-MRI scans from 258 participants with various pulmonary pathologies (median age (range): 57 (6–85); 42% females) and 31 healthy participants (median age (range): 34 (23–76); 34% females) that were split into training (593 scans (74%); 157 participants (55%)), testing (50 scans (6%); 50 participants (17%)) and external validation (164 scans (20%); 82 participants (28%)) sets.

Field Strength/Sequence
1.5-T and 3-T/3D spoiled-gradient recalled and ultrashort echo-time 1H-MRI.

Assessment
2D and 3D CNNs, trained on single-center, multi-sequence data, and the conventional spatial fuzzy c-means (SFCM) method were compared to manually delineated expert segmentations. Each method was validated on external data originating from several centers. Dice similarity coefficient (DSC), average boundary Hausdorff distance (Average HD), and relative error (XOR) metrics to assess segmentation performance.

Statistical Tests
Kruskal–Wallis tests assessed significances of differences between acquisitions in the testing set. Friedman tests with post hoc multiple comparisons assessed differences between the 2D CNN, 3D CNN, and SFCM. Bland–Altman analyses assessed agreement with manually derived lung volumes. A P value of <0.05 was considered statistically significant.

Results
The 3D CNN significantly outperformed its 2D analog and SFCM, yielding a median (range) DSC of 0.961 (0.880–0.987), Average HD of 1.63 mm (0.65–5.45) and XOR of 0.079 (0.025–0.240) on the testing set and a DSC of 0.973 (0.866–0.987), Average HD of 1.11 mm (0.47–8.13) and XOR of 0.054 (0.026–0.255) on external validation data.

Data Conclusion
The 3D CNN generated accurate 1H-MRI lung segmentations on a heterogenous dataset, demonstrating robustness to disease pathology, sequence, vendor, and center.

Deep Learning for Multi‐sequence MRI Lung Segmentation
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Longitudinal Changes in a Bleomycin Rat Model by DCE-MRI

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Contrast Enhanced Longitudinal Changes Observed in an Experimental Bleomycin-Induced Lung Fibrosis Rat Model by Radial DCE-MRI at 9.4T

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Longitudinal Changes in a Bleomycin Rat Model by DCE-MRI

Contrast Enhanced Longitudinal Changes Observed in an Experimental Bleomycin-Induced Lung Fibrosis Rat Model by Radial DCE-MRI at 9.4T

by In ‘T Zandt, René, Irma Mahmutovic Persson, Marta Tibiletti, Karin Von Wachenfeldt, Geoff J. M. Parker, Lars E. Olsson on behalf of the TRISTAN Consortium


PLOS ONE 19, Nr. 9 (27. September 2024): e0310643. doi: 10.1371/journal.pone.0310643

Abstract

Identifying biomarkers in fibrotic lung disease is key for early anti-fibrotic intervention. Dynamic contrast-enhanced (DCE) MRI offers valuable perfusion-related insights in fibrosis but adapting human MRI methods to rodents poses challenges. Here, we explored these translational challenges for the inflammatory and fibrotic phase of a bleomycin lung injury model in rats. Eleven male Sprague-Dawley rats received a single intratracheal dose of bleomycin (1000iU), four control rats received saline. Imaging was performed on days 7 and 28 post-induction. Ultra-short echo time imaging was used to image the lung for 7 minutes after which Clariscan was injected intravenously. Lung signal changes were measured for an additional 21 minutes. Images were reconstructed with a sliding-window approach, providing a temporal resolution of 10 seconds per image. After imaging on day 28, animals were euthanized, and lungs were collected for histology. Bleomycin-exposed rats initially exhibited reduced body weight, recovering to control levels after 20 days. Lung volume increased in bleomycin animals from 4.4±0.9 ml in controls to 5.5±0.5 ml and 6.5±1.2 ml on day 7 and 28. DCE-MRI showed no change of initial gradient of relative enhancement in the curves between controls and bleomycin animals on day 7 and 28 post-induction. On day 7, the DCE-MRI washout phase in bleomycin animals had higher signals than the saline group and than observed at a later time point. Lung pixels were binned in 7 enhancement classes. On day 28, the size of low relative enhancement bins almost doubled in volume compared to controls and animals on day 7 post-induction. Histology on day 28 suggests that findings could be explained by changes in lung tissue density due to lung volume increase. Adapting this clinical MRI method to rodents at 9.4T remains a challenge. Future studies may benefit from lower field strength MRI combined with higher temporal resolution DCE-MRI.

Longitudinal Changes in a Bleomycin Rat Model by DCE-MRI
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DL for lung cavity estimation from Xe and H MRI

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A Dual-Channel Deep Learning Approach for Lung Cavity Estimation From Hyperpolarized Gas and Proton MRI

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DL for lung cavity estimation from Xe- and H-MRI

A Dual-Channel Deep Learning Approach for Lung Cavity Estimation From Hyperpolarized Gas and Proton MRI

by Joshua R. Astley BEng, Alberto M. Biancardi PhD, Helen Marshall PhD, Paul J. C. Hughes PhD, Guilhem J. Collier PhD, Laurie J. Smith PhD, James A. Eaden PhD, Rod Hughes MD, Jim M. Wild PhD, Bilal A. Tahir PhD


JMRI (2022). doi: 10.1002/jmri.28519

Abstract

Background
Hyperpolarized gas MRI can quantify regional lung ventilation via biomarkers, including the ventilation defect percentage (VDP). VDP is computed from segmentations derived from spatially co-registered functional hyperpolarized gas and structural proton (1H)-MRI. Although acquired at similar lung inflation levels, they are frequently misaligned, requiring a lung cavity estimation (LCE). Recently, single-channel, mono-modal deep learning (DL)-based methods have shown promise for pulmonary image segmentation problems. Multichannel, multimodal approaches may outperform single-channel alternatives.

Purpose
We hypothesized that a DL-based dual-channel approach, leveraging both 1H-MRI and Xenon-129-MRI (129Xe-MRI), can generate LCEs more accurately than single-channel alternatives.

Study Type
Retrospective.

Population
A total of 480 corresponding 1H-MRI and 129Xe-MRI scans from 26 healthy participants (median age [range]: 11 [8–71]; 50% females) and 289 patients with pulmonary pathologies (median age [range]: 47 [6–83]; 51% females) were split into training (422 scans [88%]; 257 participants [82%]) and testing (58 scans [12%]; 58 participants [18%]) sets.

Field Strength/Sequence
1.5-T, three-dimensional (3D) spoiled gradient-recalled 1H-MRI and 3D steady-state free-precession 129Xe-MRI.

Assessment
We developed a multimodal DL approach, integrating 129Xe-MRI and 1H-MRI, in a dual-channel convolutional neural network. We compared this approach to single-channel alternatives using manually edited LCEs as a benchmark. We further assessed a fully automatic DL-based framework to calculate VDPs and compared it to manually generated VDPs.

Statistical Tests
Friedman tests with post hoc Bonferroni correction for multiple comparisons compared single-channel and dual-channel DL approaches using Dice similarity coefficient (DSC), average boundary Hausdorff distance (average HD), and relative error (XOR) metrics. Bland–Altman analysis and paired t-tests compared manual and DL-generated VDPs. A P value < 0.05 was considered statistically significant.

Results
The dual-channel approach significantly outperformed single-channel approaches, achieving a median (range) DSC, average HD, and XOR of 0.967 (0.867–0.978), 1.68 mm (37.0–0.778), and 0.066 (0.246–0.045), respectively. DL-generated VDPs were statistically indistinguishable from manually generated VDPs (P = 0.710).

Data Conclusion
Our dual-channel approach generated LCEs, which could be integrated with ventilated lung segmentations to produce biomarkers such as the VDP without manual intervention.

DL for lung cavity estimation from Xe and H MRI
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DIILD mouse model

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Translational chronic drug-induced ILD mouse model, characterized by low-grade inflammation, fibrosis and dilated large airways (conference abstract)

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DIILD mouse model

Translational chronic drug-induced ILD mouse model, characterized by low-grade inflammation, fibrosis and dilated large airways

by I Mahmutovic Persson, J Liu, R In 'T Zandt, N Fransén Petterson, A Örbom, H Falk-Håkansson, C Carvalho, K Von Wachenfeldt, L E Olsson


European Respiratory Journal 2022 60: 581 (conference abstract). doi: 10.1183/13993003.congress-2022.581

Abstract

Many systemically administrated drugs have reportedly shown to cause drug-induced interstitial lung disease (DIILD). Early disease detection is important in order to gain the best treatment outcomes. Here, we aimed to develop non-invasive MRI biomarkers, to allow for assessment of disease progression in a chronic model of bleomycin (BL)-induced ILD.

Methods: C57BL/6 mice received i.p. injections of BL (or Saline as control) 2 d/wk, for 4 wks. MRI (RARE and UTE sequences) was performed in wks 3 and 4, as well as 1-2 wks after final dosing. Lung sections from each group were stained with Masson’s-Trichrome followed by modified Ashcroft scoring.

Results: BL-challenged mice showed increased lung/body weight-ratio (p<0.05) while significant signs of low-grade inflammation and fibrosis (p<0.05) were found by histological analysis, indicating lesions emanating from the vascular side. Fibrosis progression was most apparent during the resting period (4+2wk) (p<0.001). These changes were also visualized by MRI (RARE), with increasing lesion size over time (p<0.05). MRI (UTE) analysis also showed increasing airway diameter during disease progression in the BL group.

Conclusion: With non-invasive MRI we could map the lesions and follow the progression of dilated airways over time. This model is clinically relevant and therefore suitable to use for studying DIILD as well as progressive fibrosis.

DIILD mouse model
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Methotrexate DIILD in rodents

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Methotrexate induces DIILD sporadically and at low incidence in rodents, similar to clinical scenario in humans (conference abstract)
 

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Methotrexate DIILD in rodents

Methotrexate induces DIILD sporadically and at low incidence in rodents, similar to clinical scenario in humans

by N Fransén Pettersson, C Carvalho, I Mahmutovic Persson, H Falk-Håkansson, J Liu, L E Olsson, K Von Wachenfeldt


European Respiratory Journal 2022 60: 2542 (conference abstract). doi: 10.1183/13993003.congress-2022.2542

Abstract

Drug-induced interstitial lung disease (DIILD) is underdiagnosed with increasing incidence. To better diagnose and treat DIILD, translational animal models are warranted. Methotrexate (MTX) is commonly used in patients who present with DIILD. Studies indicate that MTX alone does not induce DIILD, rather underlying genetic/environmental factors or combination therapies seem to have an impact on DIILD development. Here, we attempted to develop a translational chronic model of MTX-induced ILD.

MTX was given to mice or rats, by different routes/concentrations/time points (Table). Histological assessment was the main readout in all studies, with additional MRI and lung function measurements, in selected animal groups. Histology showed limited fibrosis and/or inflammation in MTX treated animals, which developed in some of the animals, across various treatment groups. Appeared lung lesions resided from the vasculature. In cases of detected ILD, also systemic effects in other organs were present. However, there was no correlation between disease severity/dose/frequency of MTX administration.

Conclusion: Neither disease incidence nor severity correlated with MTX concentration or exposure route/time. These observations correspond to clinical observations, where MTX treatment alone does not seem to induce DIILD. Therefore, using MTX only as a DIILD-inducing agent in preclinical research poses great challenges.

Methotrexate DIILD in rodents
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MTT and pulmonary blood flow

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Meta-analysis of mean transit time and pulmonary blood flow in the lung (conference abstract)
 

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MEta-analysis of MTT and pulomary blood flow

Meta-analysis of mean transit time and pulmonary blood flow in the lung

by M Tibiletti, L Edwards, J Naish, G J M Parker, J C Waterton


European Respiratory Journal 2022 60: 2147. doi: 10.1183/13993003.congress-2022.2147

Abstract

Introduction: CT and MRI can assess mean transit time (MTT) and pulmonary blood flow (PBF) in the lung. We aimed to determine whether MTT and PBF in the healthy lung are consistent across studies and differentiated from results in disease.

Method: A systematic literature search was conducted in PubMed to identify studies that quantified MTT and/or PBF in the lung. Inclusion criteria were limited to MRI or CT, English language, human subjects, injection of intravenous contrast agent, and quantitative values determined by indicator dilution theory. The weighted mean and standard deviation (SD) of MTT and PBF were estimated from the healthy volunteers’ (HV) values reported, weighted by number of subjects.

Results: We identified 34 studies for meta-analysis after exclusions, summarised in figure 1. In HV, weighted MTT was 5.91±1.84s (10 studies) and the weighted PBF 246±93 ml/100ml/min (14 studies).

Conclusion: MTT was consistent across studies in healthy volunteers and similarly in diseased subjects, with few values outside of the normal range. In comparison, PBF values were consistently markedly reduced in multiple diseases.

MTT and pulmonary blood flow
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longitudinal lung UTE-MRI vs CT for ILD

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Longitudinal comparison of quantitative UTE lung MRI and CT biomarkers in interstitial lung disease (conference abstract)

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Longitudinal comparison of lung UTE-MRI and CT

Longitudinal comparison of quantitative UTE lung MRI and CT biomarkers in interstitial lung disease

by Ho-Fung Chan, Timothy J Baldwin, Harry Barker, Neil J Stewart, James A Eaden, Paul J.C Hughes, Nicholas D Weatherley, Joshua Astley, Bilal A Tahir, Kevin M Johnson, Ronald A Karwoski, Brian J Bartholmai, Marta Tibiletti, Colm T Leonard, Sarah Skeoch, Nazia Chaudhuri, Ian N Bruce, Geoff J.M Parker, Stephen M Bianchi, and Jim M Wild


ISMRM 2022 conference abstract

Synopsis
UTE lung MRI approaches the diagnostic quality of CT, opening up the possibility for longitudinal follow-up of interstitial lung disease (ILD) progression. Two quantitative biomarkers of UTE lung signal were developed for monitoring longitudinal change in ILD and benchmarked against quantitative CT CALIPER measurements. Normalized UTE lung signal and UTE high percentage (based on 95% cutoff of healthy UTE lung values) was significantly different between nine healthy volunteers and sixteen ILD patients. Longitudinal change in UTE biomarkers correlated with change in CT CALIPER ILD% in the ILD patients, and most-strongly correlated to CT ground-glass changes in the lung parenchyma.

Longitudinal comparison of lung UTE-MRI and CT for ILD
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IB of lung ventilationfrom XE and OE MRI

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Imaging biomarkers of lung ventilation in interstitial lung disease from 129Xe and oxygen enhanced 1H MRI

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Imaging Biomarker of ventilation in ILD

Imaging biomarkers of lung ventilation in interstitial lung disease from 129Xe and oxygen enhanced 1H MRI

by Marta Tibiletti, James A. Eaden, Josephine H. Naisha, Paul J.C. Hughes, John C. Waterton, Matthew J. Heaton, Nazia Chaudhurie, Sarah Skeoch, Ian N. Bruce, Stephen Bianchi, Jim M. Wild, Geoff J.M. Parker


Magn Reson Imag 2023 (95), p. 39-49. doi: 10.1016/j.mri.2022.10.005

Abstract

Purpose
To compare imaging biomarkers from hyperpolarised 129Xe ventilation MRI and dynamic oxygen-enhanced MRI (OE-MRI) with standard pulmonary function tests (PFT) in interstitial lung disease (ILD) patients. To evaluate if biomarkers can separate ILD subtypes and detect early signs of disease resolution or progression.

Study type
Prospective longitudinal.

Population
Forty-one ILD (fourteen idiopathic pulmonary fibrosis (IPF), eleven hypersensitivity pneumonitis (HP), eleven drug-induced ILD (DI-ILD), five connective tissue disease related-ILD (CTD-ILD)) patients and ten healthy volunteers imaged at visit 1. Thirty-four ILD patients completed visit 2 (eleven IPF, eight HP, ten DIILD, five CTD-ILD) after 6 or 26 weeks.

Field strength/sequence
MRI was performed at 1.5 T, including inversion recovery T1 mapping, dynamic MRI acquisition with varying oxygen levels, and hyperpolarised 129Xe ventilation MRI. Subjects underwent standard spirometry and gas transfer testing.

Assessment
Five 1H MRI and two 129Xe MRI ventilation metrics were compared with spirometry and gas transfer measurements.

Statistical test
To evaluate differences at visit 1 among subgroups: ANOVA or Kruskal-Wallis rank tests with correction for multiple comparisons. To assess the relationships between imaging biomarkers, PFT, age and gender, at visit 1 and for the change between visit 1 and 2: Pearson correlations and multilinear regression models.

Results
The global PFT tests could not distinguish ILD subtypes. Percentage ventilated volumes were lower in ILD patients than in HVs when measured with 129Xe MRI (HV 97.4 ± 2.6, CTD-ILD: 91.0 ± 4.8 p = 0.017, DI-ILD 90.1 ± 7.4 p = 0.003, HP 92.6 ± 4.0 p = 0.013, IPF 88.1 ± 6.5 p < 0.001), but not with OE-MRI. 129Xe reported more heterogeneous ventilation in DI-ILD and IPF than in HV, and OE-MRI reported more heterogeneous ventilation in DI-ILD and IPF than in HP or CTD-ILD. The longitudinal changes reported by the imaging biomarkers did not correlate with the PFT changes between visits.

Data conclusion
Neither 129Xe ventilation nor OE-MRI biomarkers investigated in this study were able to differentiate between ILD subtypes, suggesting that ventilation-only biomarkers are not indicated for this task. Limited but progressive loss of ventilated volume as measured by 129Xe-MRI may be present as the biomarker of focal disease progresses. OE-MRI biomarkers are feasible in ILD patients and do not correlate strongly with PFT. Both OE-MRI and 129Xe MRI revealed more spatially heterogeneous ventilation in DI-ILD and IPF.

Imaging Biomarker for Ventilation in ILD
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Population AIF for lung perfusion

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Population Arterial Input Function for Lung Perfusion Imaging (Conference Abstract)

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Population AIF for lung perfusion

Population Arterial Input Function for Lung Perfusion Imaging

Marta Tibiletti, Jo Naish, John C Waterton, Paul JC Hughes, James A Eaden, James M Wild, Geoff JM Parker


ISMRM Conference 2021

Abstract

Introduction: T1-weighted contrast agent (CA)-based perfusion imaging can be used to characterize the first pass of a CA bolus through the lung, allowing for the measurement of blood flow, relative blood volume and mean transit time. 
One of the method’s challenges is the accurate extraction of the Arterial Input Function (AIF), the concentration of CA in a feeding artery. Some of the issues that may arise are: curve sampling at too low temporal resolution for the rapidly changing curve; errors in the peak height due to signal saturation at high CA concentrations; incomplete spoiling; partial volume and inflow effects; and motion. 
Previous investigators have used  consensus or population-based arterial input functions (AIFs) in the analysis of extended dynamic contrast-enhanced MR data. However it is not known whether population-based AIFs are also useful in perfusion imaging based on first-pass DCEMRI.
In this work, we explore the possibility of extracting a population AIF for lung perfusion imaging, detailing the first pass of the CA bolus at high temporal resolution in the pulmonary arteries (PA). The results of the analysis using a measured AIF and the population AIF are compared.
Comments:
A population AIF was obtained from the PA. While there is significant variation among the GV fitting from which the population AIF was obtained, the variation is not related to dose but the AUC is linearly related to dose. When comparing the results of the perfusion analysis within our patient population, the only significant difference was observed in in BV, which is lower when using a population AIF. This is probably due to some of the measured AIF presenting too low AUC.

Conclusion:
In this work, we have derived a population AIF for perfusion quantification in the lung. This AIF may be of use in settings where measured AIF quality is insufficient to allow reliable quantification.
 

Population AIF for lung perfusion
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129Xe-MRI to Differentiate Fibrosis and Inflammation

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Hyperpolarised 129-xenon MRI in differentiating between fibrotic and inflammatory interstitial lung disease and assessing longitudinal change

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129Xe-MRI to Differentiate Fibrosis and Inflammation

Hyperpolarised 129-xenon MRI in differentiating between fibrotic and inflammatory interstitial lung disease and assessing longitudinal change

by Irma Mahmutovic Persson, Nina Fransén Pettersson, Jian Liu, Hanna Falk Håkansson, Anders Örbom, René JA Eaden, GJ Collier, G Norquay, H-F Chan, PJC Hughes, ND Weatherley, S Rajaram, A Swift, CT Leonard, S Skeoch, N Chaudhuri, GJM Parker, SM Bianchi, JM Wild


Thorax 2021;76:A46-A47. doi: 10.1136/thorax-2020-BTSabstracts.80

Abstract

Introduction and Objectives: Apparent diffusion coefficient (ADC) and mean diffusive length scale (LmD) are diffusion-weighted (DW) MRI measurements of alveolar gas diffusion, providing novel lung microstructure information. Hyperpolarised 129-xenon (129Xe) MR spectroscopy is a quantitative marker of gas exchange, using the ratio of uptake of 129Xe in red blood cells to tissue/plasma (RBC:TP).

The objective was to evaluate hyperpolarised 129Xe MRI in differentiating between fibrotic and inflammatory ILD and assessing longitudinal change.

Methods: A prospective, multicentre study of ILD patients including connective tissue disease ILD (CTD-ILD), drug induced ILD (DI-ILD), hypersensitivity pneumonitis (HP), idiopathic non-specific interstitial pneumonia (iNSIP) and idiopathic pulmonary fibrosis (IPF). Hyperpolarised 129Xe MRI was performed on a 1.5T scanner. Baseline HRCT scan was performed within a year prior to the MRI scan. Semi-quantitative visual CT analysis was performed by two consultant chest radiologists. In the non-IPF subtypes, a ground glass opacity score <2 and ≥2 was used to define fibrotic and inflammatory ILD respectively. All IPF subjects were classified as fibrotic.

Results: To date, 34 patients (5 CTD-ILD, 9 DI-ILD, 7 HP, 2 iNSIP, 11 IPF) have complete MRI scan data for two separate visits (6 weeks apart for DI-ILD/HP/iNSIP and 6 months apart for CTD-ILD/IPF). There were 18 patients in the fibrotic group and 16 in the inflammatory group. At baseline visit there was no significant difference in mean RBC:TP between the fibrotic and inflammatory groups (0.17 vs 0.14; p=0.083), but a significant difference between the fibrotic and inflammatory groups in mean ADC (0.048 vs 0.043; p=0.030) (figure 1a) and mean LmD(261.3 vs 243.4; p=0.017) (figure 1b). In longitudinal change, there was a significant difference in mean RBC:TP between the fibrotic and inflammatory groups (-0.026 vs 0.0016; p=0.023), but no significant difference between the fibrotic and inflammatory groups in mean ADC (0.00089 vs -0.00025; p=0.25) and mean LmD (2.1 vs -0.19; p=0.39).

Conclusions: 129Xe DW-MRI could have a role in differentiating changes in the airway microstructure between fibrotic and inflammatory ILD. 129Xe RBC:TP has sensitivity to longitudinal change with a decline in gas exchange observed in the fibrotic group but not in the inflammatory group.
 

129Xe-MRI to Differentiate Fibrosis and Inflammation
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