Misclassification of females and males in cardiovascular magnetic resonance parametric mapping: the importance of sex-specific normal ranges for diagnosis of health vs. disease.

AIMS: Cardiovascular magnetic resonance parametric mapping enables non-invasive quantitative myocardial tissue characterization. Human myocardium has normal ranges of T1 and T2 values, deviation from which may indicate disease or change in physiology. Normal myocardial T1 and T2 values are affected by biological sex. Consequently, normal ranges created with insufficient numbers of each sex may result in sampling biases, misclassification of healthy values vs. disease, and even misdiagnoses. In this study, we investigated the impact of using male normal ranges for classifying female cases as normal or abnormal (and vice versa). METHODS AND RESULTS: One hundred and forty-two healthy volunteers (male and female) were scanned on two Siemens 3T MR systems, providing averaged global myocardial T1 and T2 values on a per-subject basis. The Monte Carlo method was used to generate simulated normal ranges from these values to estimate the statistical accuracy of classifying healthy female or male cases correctly as 'normal' when using sex-specific vs. mixed-sex normal ranges. The normal male and female T1- and T2-mapping values were significantly different by sex, after adjusting for age and heart rate. CONCLUSION: Using 15 healthy volunteers who are not sex specific to establish a normal range resulted in a typical misclassification of up to 36% of healthy females and 37% of healthy males as having abnormal T1 values and up to 16% of healthy females and 12% of healthy males as having abnormal T2 values. This paper highlights the potential adverse impact on diagnostic accuracy that can occur when local normal ranges contain insufficient numbers of both sexes. Sex-specific reference ranges should thus be routinely adopted in clinical practice.

Quality control-driven deep ensemble for accountable automated segmentation of cardiac magnetic resonance LGE and VNE images.

Background: Late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) imaging is the gold standard for non-invasive myocardial tissue characterisation. However, accurate segmentation of the left ventricular (LV) myocardium remains a challenge due to limited training data and lack of quality control. This study addresses these issues by leveraging generative adversarial networks (GAN)-generated virtual native enhancement (VNE) images to expand the training set and incorporating an automated quality control-driven (QCD) framework to improve segmentation reliability. Methods: A dataset comprising 4,716 LGE images (from 1,363 patients with hypertrophic cardiomyopathy and myocardial infarction) was used for development. To generate additional clinically validated data, LGE data were augmented with a GAN-based generator to produce VNE images. LV was contoured on these images manually by clinical observers. To create diverse candidate segmentations, the QCD framework involved multiple U-Nets, which were combined using statistical rank filters. The framework predicted the Dice Similarity Coefficient (DSC) for each candidate segmentation, with the highest predicted DSC indicating the most accurate and reliable result. The performance of the QCD ensemble framework was evaluated on both LGE and VNE test datasets (309 LGE/VNE images from 103 patients), assessing segmentation accuracy (DSC) and quality prediction (mean absolute error (MAE) and binary classification accuracy). Results: The QCD framework effectively and rapidly segmented the LV myocardium (<1 s per image) on both LGE and VNE images, demonstrating robust performance on both test datasets with similar mean DSC (LGE: 0.845±0.075; VNE: 0.845±0.071; p=ns). Incorporating GAN-generated VNE data into the training process consistently led to enhanced performance for both individual models and the overall framework. The quality control mechanism yielded a high performance (MAE=0.043, accuracy=0.951) emphasising the accuracy of the quality control-driven strategy in predicting segmentation quality in clinical settings. Overall, no statistical difference (p=ns) was found when comparing the LGE and VNE test sets across all experiments. Conclusions: The QCD ensemble framework, leveraging GAN-generated VNE data and an automated quality control mechanism, significantly improved the accuracy and reliability of LGE segmentation, paving the way for enhanced and accountable diagnostic imaging in routine clinical use.

Impact of age and telomere length on circulating T cells and rejection risk after lung transplantation for idiopathic pulmonary fibrosis.

BACKGROUND: Most idiopathic pulmonary fibrosis (IPF) lung transplant recipients (IPF-LTRs) have short telomere (ST) length. Inherited mutations in telomere-related genes are associated with the development of T cell immunodeficiency. Despite this, IPF-LTRs with telomere-related rare variants are not protected from acute cellular rejection (ACR). We set out to determine the impact of both age and telomere length on the circulating T cell compartment and ACR burden of IPF-LTRs. METHODS: We identified 106 IPF-LTRs who had telomere length testing using flowFISH (57 with short telomeres and 49 with long telomeres) as well as a subset from both cohorts who had cryopreserved PBMC at least 1 time point, 6 months posttransplantation. Circulating T cells from before transplantation and at 6 and 12 months posttransplantation were analyzed using multiparameter flow cytometry to study phenotype and functional capacity, and bulk T cell receptor sequencing was performed to study repertoire diversity. Linear regression was used to study the relationship of age and telomere length on early (within 1 year) and late (between 1 and 2 years) ACR. RESULTS: IPF-LTRs with ST were found to have premature "aging" of their circulating T cell compartment, with age-agnostic elevations in posttransplant terminal differentiation of CD8+ T cells, increased granzyme B positivity of both CD8+ and CD4+ T cells, upregulation of the exhaustion marker, CD57, and chemotactic protein CCR5, and enhanced T cell receptor clonal expansion. Additionally, we found a significant decline in early ACR burden with increasing age, but only in the ST cohort. CONCLUSIONS: IPF-LTRs with ST have premature "aging" of their circulating T cell compartment posttransplantation and a clear age-related decline in ACR burden.

Lung transplant recipients with telomere-mediated pulmonary fibrosis have increased risk for hematologic complications.

Idiopathic pulmonary fibrosis lung transplant recipients (IPF-LTRs) are enriched for short telomere length (TL) and telomere gene rare variants. A subset of patients with nontransplant short-TL are at increased risk for bone marrow (BM) dysfunction. We hypothesized that IPF-LTRs with short-TL and/or rare variants would be at increased risk for posttransplant hematologic complications. Data were extracted from a retrospective cohort of 72 IPF-LTRs and 72 age-matched non-IPF-LTR controls. Genetic assessment was done using whole genome sequencing or targeted sequence panel. TL was measured using flow cytometry and fluorescence in-situ hybridization (FlowFISH) and TelSeq software. The majority of the IPF-LTR cohort had short-TL, and 26% of IPF-LTRs had rare variants. Compared to non-IPF controls, short-TL IPF-LTRs were more likely to have immunosuppression agents discontinued due to cytopenias (P = .0375), and BM dysfunction requiring BM biopsy was more prevalent (29% vs 4%, P = .0003). IPF-LTRs with short-TL and rare variants had increased requirements for transfusion and growth factor support. Multivariable logistic regression demonstrated that short-TL, rare variants, and lower pretransplant platelet counts were associated with BM dysfunction. Pretransplant TL measurement and genetic testing for rare telomere gene variants identified IPF-LTRs at increased risk for hematologic complications. Our findings support stratification for telomere-mediated pulmonary fibrosis in lung transplant candidates.

COVID-19 convalescent plasma boosts early antibody titer and does not influence the adaptive immune response.

Multiple randomized, controlled clinical trials have yielded discordant results regarding the efficacy of convalescent plasma in outpatients, with some showing an approximately 2-fold reduction in risk and others showing no effect. We quantified binding and neutralizing antibody levels in 492 of the 511 participants from the Clinical Trial of COVID-19 Convalescent Plasma in Outpatients (C3PO) of a single unit of COVID-19 convalescent plasma (CCP) versus saline infusion. In a subset of 70 participants, peripheral blood mononuclear cells were obtained to define the evolution of B and T cell responses through day 30. Binding and neutralizing antibody responses were approximately 2-fold higher 1 hour after infusion in recipients of CCP compared with saline plus multivitamin, but levels achieved by the native immune system by day 15 were almost 10-fold higher than those seen immediately after CCP administration. Infusion of CCP did not block generation of the host antibody response or skew B or T cell phenotype or maturation. Activated CD4+ and CD8+ T cells were associated with more severe disease outcome. These data show that CCP leads to a measurable boost in anti-SARS-CoV-2 antibodies but that the boost is modest and may not be sufficient to alter disease course.

Cross-Regulation of F-Box Protein FBXL2 with T-bet and TNF-α during Acute and Chronic Lung Allograft Rejection.

Chronic lung allograft dysfunction is the major barrier to long-term survival in lung transplant recipients. Evidence supports type 1 alloimmunity as the predominant response in acute/chronic lung rejection, but the immunoregulatory mechanisms remain incompletely understood. We studied the combinatorial F-box E3 ligase system: F-box protein 3 (FBXO3; proinflammatory) and F-box and leucine-rich repeat protein 2 (FBXL2; anti-inflammatory and regulates TNFR-associated factor [TRAF] protein). Using the mouse orthotopic lung transplant model, we evaluated allografts from BALB/c → C57BL/6 (acute rejection; day 10) and found significant induction of FBXO3 and diminished FBXL2 protein along with elevated T-bet, IFN-γ, and TRAF proteins 1-5 compared with isografts. In the acute model, treatment with costimulation blockade (MR1/CTLA4-Ig) resulted in attenuated FBXO3, preserved FBXL2, and substantially reduced T-bet, IFN-γ, and TRAFs 1-5, consistent with a key role for type 1 alloimmunity. Immunohistochemistry revealed significant changes in the FBXO3/FBXL2 balance in airway epithelia and infiltrating mononuclear cells during rejection compared with isografts or costimulation blockade-treated allografts. In the chronic lung rejection model, DBA/2J/C57BL/6F1 > DBA/2J (day 28), we observed persistently elevated FBXO3/FBXL2 balance and T-bet/IFN-γ protein and similar findings from lung transplant recipient lungs with chronic lung allograft dysfunction versus controls. We hypothesized that FBXL2 regulated T-bet and found FBXL2 was sufficient to polyubiquitinate T-bet and coimmunoprecipitated with T-bet on pulldown experiments and vice versa in Jurkat cells. Transfection with FBXL2 diminished T-bet protein in a dose-dependent manner in mouse lung epithelial cells. In testing type 1 cytokines, TNF-α was found to negatively regulate FBXL2 protein and mRNA levels. Together, our findings show the combinatorial E3 ligase FBXO3/FBXL2 system plays a role in the regulation of T-bet through FBXL2, with negative cross-regulation of TNF-α on FBXL2 during lung allograft rejection.

Artificial Intelligence for Contrast-Free MRI: Scar Assessment in Myocardial Infarction Using Deep Learning-Based Virtual Native Enhancement.

BACKGROUND: Myocardial scars are assessed noninvasively using cardiovascular magnetic resonance late gadolinium enhancement (LGE) as an imaging gold standard. A contrast-free approach would provide many advantages, including a faster and cheaper scan without contrast-associated problems. METHODS: Virtual native enhancement (VNE) is a novel technology that can produce virtual LGE-like images without the need for contrast. VNE combines cine imaging and native T1 maps to produce LGE-like images using artificial intelligence. VNE was developed for patients with previous myocardial infarction from 4271 data sets (912 patients); each data set comprises slice position-matched cine, T1 maps, and LGE images. After quality control, 3002 data sets (775 patients) were used for development and 291 data sets (68 patients) for testing. The VNE generator was trained using generative adversarial networks, using 2 adversarial discriminators to improve the image quality. The left ventricle was contoured semiautomatically. Myocardial scar volume was quantified using the full width at half maximum method. Scar transmurality was measured using the centerline chord method and visualized on bull's-eye plots. Lesion quantification by VNE and LGE was compared using linear regression, Pearson correlation (R), and intraclass correlation coefficients. Proof-of-principle histopathologic comparison of VNE in a porcine model of myocardial infarction also was performed. RESULTS: VNE provided significantly better image quality than LGE on blinded analysis by 5 independent operators on 291 data sets (all P<0.001). VNE correlated strongly with LGE in quantifying scar size (R, 0.89; intraclass correlation coefficient, 0.94) and transmurality (R, 0.84; intraclass correlation coefficient, 0.90) in 66 patients (277 test data sets). Two cardiovascular magnetic resonance experts reviewed all test image slices and reported an overall accuracy of 84% for VNE in detecting scars when compared with LGE, with specificity of 100% and sensitivity of 77%. VNE also showed excellent visuospatial agreement with histopathology in 2 cases of a porcine model of myocardial infarction. CONCLUSIONS: VNE demonstrated high agreement with LGE cardiovascular magnetic resonance for myocardial scar assessment in patients with previous myocardial infarction in visuospatial distribution and lesion quantification with superior image quality. VNE is a potentially transformative artificial intelligence-based technology with promise in reducing scan times and costs, increasing clinical throughput, and improving the accessibility of cardiovascular magnetic resonance in the near future.

Modulation of tissue resident memory T cells by glucocorticoids after acute cellular rejection in lung transplantation.

Acute cellular rejection is common after lung transplantation and is associated with an increased risk of early chronic rejection. We present combined single-cell RNA and TCR sequencing on recipient-derived T cells obtained from the bronchoalveolar lavage of three lung transplant recipients with rejection and compare them with T cells obtained from the same patients after treatment of rejection with high-dose systemic glucocorticoids. At the time of rejection, we found an oligoclonal expansion of cytotoxic CD8+ T cells that all persisted as tissue resident memory T cells after successful treatment. Persisting CD8+ allograft-resident T cells have reduced gene expression for cytotoxic mediators after therapy with glucocorticoids but accumulate around airways. This clonal expansion is discordant with circulating T cell clonal expansion at the time of rejection, suggesting in situ expansion. We thus highlight the accumulation of cytotoxic, recipient-derived tissue resident memory T cells within the lung allograft that persist despite the administration of high-dose systemic glucocorticoids. The long-term clinical consequences of this persistence have yet to be characterized.

CD4+ T-Cell Dysfunction in Severe COVID-19 Disease Is Tumor Necrosis Factor-α/Tumor Necrosis Factor Receptor 1-Dependent.

Rationale: Lymphopenia is common in severe coronavirus disease (COVID-19), yet the immune mechanisms are poorly understood. As inflammatory cytokines are increased in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, we hypothesized a role in contributing to reduced T-cell numbers. Objectives: We sought to characterize the functional SARS-CoV-2 T-cell responses in patients with severe versus recovered, mild COVID-19 to determine whether differences were detectable. Methods: Using flow cytometry and single-cell RNA sequence analyses, we assessed SARS-CoV-2-specific responses in our cohort. Measurements and Main Results: In 148 patients with severe COVID-19, we found lymphopenia was associated with worse survival. CD4+ lymphopenia predominated, with lower CD4+/CD8+ ratios in severe COVID-19 compared with patients with mild disease (P < 0.0001). In severe disease, immunodominant CD4+ T-cell responses to Spike-1 (S1) produced increased in vitro TNF-α (tumor necrosis factor-α) but demonstrated impaired S1-specific proliferation and increased susceptibility to activation-induced cell death after antigen exposure. CD4+TNF-α+ T-cell responses inversely correlated with absolute CD4+ counts from patients with severe COVID-19 (n = 76; R = -0.797; P < 0.0001). In vitro TNF-α blockade, including infliximab or anti-TNF receptor 1 antibodies, strikingly rescued S1-specific CD4+ T-cell proliferation and abrogated S1-specific activation-induced cell death in peripheral blood mononuclear cells from patients with severe COVID-19 (P < 0.001). Single-cell RNA sequencing demonstrated marked downregulation of type-1 cytokines and NFκB signaling in S1-stimulated CD4+ cells with infliximab treatment. We also evaluated BAL and lung explant CD4+ T cells recovered from patients with severe COVID-19 and observed that lung T cells produced higher TNF-α compared with peripheral blood mononuclear cells. Conclusions: Together, our findings show CD4+ dysfunction in severe COVID-19 is TNF-α/TNF receptor 1-dependent through immune mechanisms that may contribute to lymphopenia. TNF-α blockade may be beneficial in severe COVID-19.

Rate of recipient-derived alveolar macrophage development and major histocompatibility complex cross-decoration after lung transplantation in humans.

Alveolar macrophages (AM) play critical roles in lung tissue homeostasis, host defense, and modulating lung injury. The rate of AM turnover (donor AM replacement by circulating monocytes) after transplantation has been incompletely characterized. Furthermore, the anatomic pattern of recipient-derived lung macrophages repopulation has not been reported, nor has their ability to accumulate and present donor major histocompatibility complex (a process we refer to as MHC cross-decoration). We longitudinally characterized the myeloid content of bronchoalveolar lavage (BAL) and biopsy specimens of lung transplant recipients and found a biphasic rate in AM turnover in the allograft, with a rapid turnover perioperatively, accelerated by both the type of induction immunosuppression and the presence of primary graft dysfunction. We found that recipient myeloid cells with cell surface AM phenotype repopulated the lung in a disorganized pattern, comprised mainly of large clusters of cells. Finally, we show that recipient AM take up and present donor peptide-MHC complexes yet are not able to independently induce an in vitro alloreactive response by circulating recipient T cells.

Lung transplantation for idiopathic pulmonary fibrosis enriches for individuals with telomere-mediated disease.

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is the most common indication for lung transplantation in North America and variants in telomere-maintenance genes are the most common identifiable cause of IPF. We reasoned that younger IPF patients are more likely to undergo lung transplantation and we hypothesized that lung transplant recipients would be enriched for individuals with telomere-mediated disease due to the earlier onset and more severe disease in these patients. METHODS: Individuals with IPF who underwent lung transplantation or were evaluated in an interstitial lung disease specialty clinic who did not undergo lung transplantation were examined. Genetic evaluation was completed via whole genome sequencing (WGS) of 426 individuals and targeted sequencing for 5 individuals. Rare variants in genes previously associated with IPF were classified using the American College of Medical Genetics guidelines. Telomere length from WGS data was measured using TelSeq software. Patient characteristics were collected via medical record review. RESULTS: Of 431 individuals, 149 underwent lung transplantation for IPF. The median age of diagnosis of transplanted vs non-transplanted individuals was significantly younger (60 years vs 70 years, respectively, p<0.0001). IPF lung transplant recipients (IPF-LTRs) were twice as likely to have telomere-related rare variants compared to non-transplanted individuals (24% vs 12%, respectively, p=0.0013). IPF-LTRs had shorter telomeres than non-transplanted IPF patients (p=0.0028) and >85% had telomeres below the age-adjusted mean. Post-transplant survival and CLAD were similar amongst IPF-LTRs with rare variants in telomere-maintenance genes compared to those without, as well as in those with short telomeres versus longer telomeres. CONCLUSIONS: There is an enrichment for telomere-maintenance gene variants and short telomeres among IPF-LTRs. However, transplant outcomes of survival and CLAD do not differ by gene variants or telomere length within IPF-LTRs. Our findings support individual with telomere-mediated disease should not be excluded from lung transplantation and focusing research efforts on therapies directed toward individuals with short-telomere mediated disease.

MOCOnet: Robust Motion Correction of Cardiovascular Magnetic Resonance T1 Mapping Using Convolutional Neural Networks.

Background: Quantitative cardiovascular magnetic resonance (CMR) T1 mapping has shown promise for advanced tissue characterisation in routine clinical practise. However, T1 mapping is prone to motion artefacts, which affects its robustness and clinical interpretation. Current methods for motion correction on T1 mapping are model-driven with no guarantee on generalisability, limiting its widespread use. In contrast, emerging data-driven deep learning approaches have shown good performance in general image registration tasks. We propose MOCOnet, a convolutional neural network solution, for generalisable motion artefact correction in T1 maps. Methods: The network architecture employs U-Net for producing distance vector fields and utilises warping layers to apply deformation to the feature maps in a coarse-to-fine manner. Using the UK Biobank imaging dataset scanned at 1.5T, MOCOnet was trained on 1,536 mid-ventricular T1 maps (acquired using the ShMOLLI method) with motion artefacts, generated by a customised deformation procedure, and tested on a different set of 200 samples with a diverse range of motion. MOCOnet was compared to a well-validated baseline multi-modal image registration method. Motion reduction was visually assessed by 3 human experts, with motion scores ranging from 0% (strictly no motion) to 100% (very severe motion). Results: MOCOnet achieved fast image registration (<1 second per T1 map) and successfully suppressed a wide range of motion artefacts. MOCOnet significantly reduced motion scores from 37.1±21.5 to 13.3±10.5 (p < 0.001), whereas the baseline method reduced it to 15.8±15.6 (p < 0.001). MOCOnet was significantly better than the baseline method in suppressing motion artefacts and more consistently (p = 0.007). Conclusion: MOCOnet demonstrated significantly better motion correction performance compared to a traditional image registration approach. Salvaging data affected by motion with robustness and in a time-efficient manner may enable better image quality and reliable images for immediate clinical interpretation.

Cardiac stress T1-mapping response and extracellular volume stability of MOLLI-based T1-mapping methods.

Stress and rest T1-mapping may assess for myocardial ischemia and extracellular volume (ECV). However, the stress T1 response is method-dependent, and underestimation may lead to misdiagnosis. Further, ECV quantification may be affected by time, as well as the number and dosage of gadolinium (Gd) contrast administered. We compared two commonly available T1-mapping approaches in their stress T1 response and ECV measurement stability. Healthy subjects (n = 10, 50% female, 35 ± 8 years) underwent regadenoson stress CMR (1.5 T) on two separate days. Prototype ShMOLLI 5(1)1(1)1 sequence was used to acquire consecutive mid-ventricular T1-maps at rest, stress and post-Gd contrast to track the T1 time evolution. For comparison, standard MOLLI sequences were used: MOLLI 5(3)3 Low (256 matrix) & High (192 matrix) Heart Rate (HR) to acquire rest and stress T1-maps, and MOLLI 4(1)3(1)2 Low & High HR for post-contrast T1-maps. Stress and rest myocardial blood flow (MBF) maps were acquired after IV Gd contrast (0.05 mmol/kg each). Stress T1 reactivity (delta T1) was defined as the relative percentage increase in native T1 between rest and stress. Myocardial T1 values for delta T1 (dT1) and ECV were calculated. Residuals from the identified time dependencies were used to assess intra-method variability. ShMOLLI achieved a greater stress T1 response compared to MOLLI Low and High HR (peak dT1 = 6.4 ± 1.7% vs. 4.8 ± 1.3% vs. 3.8 ± 1.0%, respectively; both p < 0.0001). ShMOLLI dT1 correlated strongly with stress MBF (r = 0.77, p < 0.001), compared to MOLLI Low HR (r = 0.65, p < 0.01) and MOLLI High HR (r = 0.43, p = 0.07). ShMOLLI ECV was more stable to gadolinium dose with less time drift (0.006-0.04% per minute) than MOLLI variants. Overall, ShMOLLI demonstrated less intra-individual variability than MOLLI variants for stress T1 and ECV quantification. Power calculations indicate up to a fourfold (stress T1) and 7.5-fold (ECV) advantage in sample-size reduction using ShMOLLI. Our results indicate that ShMOLLI correlates strongly with increased MBF during regadenoson stress and achieves a significantly higher stress T1 response, greater effect size, and greater ECV measurement stability compared with the MOLLI variants tested.

Toward Replacing Late Gadolinium Enhancement With Artificial Intelligence Virtual Native Enhancement for Gadolinium-Free Cardiovascular Magnetic Resonance Tissue Characterization in Hypertrophic Cardiomyopathy.

BACKGROUND: Late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) imaging is the gold standard for noninvasive myocardial tissue characterization but requires intravenous contrast agent administration. It is highly desired to develop a contrast agent-free technology to replace LGE for faster and cheaper CMR scans. METHODS: A CMR virtual native enhancement (VNE) imaging technology was developed using artificial intelligence. The deep learning model for generating VNE uses multiple streams of convolutional neural networks to exploit and enhance the existing signals in native T1 maps (pixel-wise maps of tissue T1 relaxation times) and cine imaging of cardiac structure and function, presenting them as LGE-equivalent images. The VNE generator was trained using generative adversarial networks. This technology was first developed on CMR datasets from the multicenter Hypertrophic Cardiomyopathy Registry, using hypertrophic cardiomyopathy as an exemplar. The datasets were randomized into 2 independent groups for deep learning training and testing. The test data of VNE and LGE were scored and contoured by experienced human operators to assess image quality, visuospatial agreement, and myocardial lesion burden quantification. Image quality was compared using a nonparametric Wilcoxon test. Intra- and interobserver agreement was analyzed using intraclass correlation coefficients (ICC). Lesion quantification by VNE and LGE were compared using linear regression and ICC. RESULTS: A total of 1348 hypertrophic cardiomyopathy patients provided 4093 triplets of matched T1 maps, cines, and LGE datasets. After randomization and data quality control, 2695 datasets were used for VNE method development and 345 were used for independent testing. VNE had significantly better image quality than LGE, as assessed by 4 operators (n=345 datasets; P<0.001 [Wilcoxon test]). VNE revealed lesions characteristic of hypertrophic cardiomyopathy in high visuospatial agreement with LGE. In 121 patients (n=326 datasets), VNE correlated with LGE in detecting and quantifying both hyperintensity myocardial lesions (r=0.77-0.79; ICC=0.77-0.87; P<0.001) and intermediate-intensity lesions (r=0.70-0.76; ICC=0.82-0.85; P<0.001). The native CMR images (cine plus T1 map) required for VNE can be acquired within 15 minutes and producing a VNE image takes less than 1 second. CONCLUSIONS: VNE is a new CMR technology that resembles conventional LGE but without the need for contrast administration. VNE achieved high agreement with LGE in the distribution and quantification of lesions, with significantly better image quality.

Deep neural network ensemble for on-the-fly quality control-driven segmentation of cardiac MRI T1 mapping.

Recent developments in artificial intelligence have generated increasing interest to deploy automated image analysis for diagnostic imaging and large-scale clinical applications. However, inaccuracy from automated methods could lead to incorrect conclusions, diagnoses or even harm to patients. Manual inspection for potential inaccuracies is labor-intensive and time-consuming, hampering progress towards fast and accurate clinical reporting in high volumes. To promote reliable fully-automated image analysis, we propose a quality control-driven (QCD) segmentation framework. It is an ensemble of neural networks that integrate image analysis and quality control. The novelty of this framework is the selection of the most optimal segmentation based on predicted segmentation accuracy, on-the-fly. Additionally, this framework visualizes segmentation agreement to provide traceability of the quality control process. In this work, we demonstrated the utility of the framework in cardiovascular magnetic resonance T1-mapping - a quantitative technique for myocardial tissue characterization. The framework achieved near-perfect agreement with expert image analysts in estimating myocardial T1 value (r=0.987,p<.0005; mean absolute error (MAE)=11.3ms), with accurate segmentation quality prediction (Dice coefficient prediction MAE=0.0339) and classification (accuracy=0.99), and a fast average processing time of 0.39 second/image. In summary, the QCD framework can generate high-throughput automated image analysis with speed and accuracy that is highly desirable for large-scale clinical applications.

Cardiovascular magnetic resonance stress and rest T1-mapping using regadenoson for detection of ischemic heart disease compared to healthy controls.

BACKGROUND: Adenosine stress T1-mapping on cardiovascular magnetic resonance (CMR) can differentiate between normal, ischemic, infarcted, and remote myocardial tissue classes without the need for contrast agents. Regadenoson, a selective coronary vasodilator, is often used in stress perfusion imaging when adenosine is contra-indicated, and has advantages in ease of administration, safety profile, and clinical workflow. We aimed to characterize the regadenoson stress T1-mapping response in healthy individuals, and to investigate its ability to differentiate between myocardial tissue classes in patients with coronary artery disease (CAD). METHODS: Eleven healthy controls and 25 patients with CAD underwent regadenoson stress perfusion CMR, as well as rest and stress ShMOLLI T1-mapping. Native T1 values and stress T1 reactivity were derived for normal myocardium in healthy controls and for different myocardial tissue classes in patients with CAD. RESULTS: Healthy controls had normal myocardial native T1 values at rest (931 ± 22 ms) with significant global regadenoson stress T1 reactivity (δT1 = 8.2 ± 0.8% relative to baseline; p < 0.0001). Infarcted myocardium had significantly higher resting T1 (1215 ± 115 ms) than ischemic, remote, and normal myocardium (all p < 0.0001) with an abolished stress T1 response (δT1 = -0.8% [IQR: -1.9-0.5]). Ischemic myocardium had elevated resting T1 compared to normal (964 ± 57 ms; p < 0.01) with an abolished stress T1 response (δT1 = 0.5 ± 1.6%). Remote myocardium in patients had comparable resting T1 to normal (949 ms [IQR: 915-973]; p = 0.06) with blunted stress reactivity (δT1 = 4.3% [IQR: 3.1-6.3]; p < 0.0001). CONCLUSIONS: Healthy controls demonstrate significant stress T1 reactivity during regadenoson stress. Regadenoson stress and rest T1-mapping is a viable alternative to adenosine and exercise for the assessment of CAD and can distinguish between normal, ischemic, infarcted, and remote myocardium.

Quality assurance of quantitative cardiac T1-mapping in multicenter clinical trials - A T1 phantom program from the hypertrophic cardiomyopathy registry (HCMR) study.

BACKGROUND: Quantitative cardiovascular magnetic resonance T1-mapping is increasingly used for myocardial tissue characterization. However, the lack of standardization limits direct comparability between centers and wider roll-out for clinical use or trials. PURPOSE: To develop a quality assurance (QA) program assuring standardized T1 measurements for clinical use. METHODS: MR phantoms manufactured in 2013 were distributed, including ShMOLLI T1-mapping and reference T1 and T2 protocols. We first studied the T1 and T2 dependency on temperature and phantom aging using phantom datasets from a single site over 4 years. Based on this, we developed a multiparametric QA model, which was then applied to 78 scans from 28 other multi-national sites. RESULTS: T1 temperature sensitivity followed a second-order polynomial to baseline T1 values (R2 > 0.996). Some phantoms showed aging effects, where T1 drifted up to 49% over 40 months. The correlation model based on reference T1 and T2, developed on 1004 dedicated phantom scans, predicted ShMOLLI-T1 with high consistency (coefficient of variation 1.54%), and was robust to temperature variations and phantom aging. Using the 95% confidence interval of the correlation model residuals as the tolerance range, we analyzed 390 ShMOLLI T1-maps and confirmed accurate sequence deployment in 90%(70/78) of QA scans across 28 multiple centers, and categorized the rest with specific remedial actions. CONCLUSIONS: The proposed phantom QA for T1-mapping can assure correct method implementation and protocol adherence, and is robust to temperature variation and phantom aging. This QA program circumvents the need of frequent phantom replacements, and can be readily deployed in multicenter trials.

Type-1 immunity and endogenous immune regulators predominate in the airway transcriptome during chronic lung allograft dysfunction.

Chronic lung allograft dysfunction (CLAD) remains the major complication limiting long-term survival among lung transplant recipients (LTRs). Limited understanding of CLAD immunopathogenesis and a paucity of biomarkers remain substantial barriers for earlier detection and therapeutic interventions for CLAD. We hypothesized the airway transcriptome would reflect key immunologic changes in disease. We compared airway brush-derived transcriptomic signatures in CLAD (n = 24) versus non-CLAD (n = 21) LTRs. A targeted assessment of the proteome using concomitant bronchoalveolar lavage (BAL) fluid for 24 cytokines/chemokines and alloimmune T cell responses was performed to validate the airway transcriptome. We observed an airway transcriptomic signature of differential genes expressed (DGEs) in CLAD marked by Type-1 immunity and striking upregulation of two endogenous immune regulators: indoleamine 2, 3 dioxygenase 1 (IDO-1) and tumor necrosis factor receptor superfamily 6B (TNFRSF6B). Advanced CLAD staging was associated with a more intense airway transcriptome signature. In a validation cohort using the identified signature, we found an area under the curve (AUC) of 0.77 for CLAD LTRs. Targeted proteomic analyses revealed a predominant Type-1 profile with detection of IFN-γ, TNF-α, and IL-1ß as dominant CLAD cytokines, correlating with the airway transcriptome. The airway transcriptome provides novel insights into CLAD immunopathogenesis and biomarkers that may impact diagnosis of CLAD.

Standardization of T1-mapping in cardiovascular magnetic resonance using clustered structuring for benchmarking normal ranges.

BACKGROUND: Cardiovascular magnetic resonance T1-mapping is increasingly used for tissue characterization, commonly based on Modified Look-Locker Inversion recovery (MOLLI). However, there are numerous MOLLI variants with differing normal ranges. This lack of standardization presents confusion and difficulty in inter-center comparisons, hindering widespread adoption of T1-mapping. METHODS: To address this, we performed a structured literature search for native left ventricular myocardial T1-mapping in healthy humans measured using MOLLI variants at 1.5 and 3 Tesla, across scanner vendors. We then used k-means clustering to structure normal MOLLI-T1 values according to magnetic field strength, and investigated correlations between common imaging parameters: repetition time (TR), echo time (TE), flip angle (FA). RESULTS: We analyzed data from 2207 healthy controls in 76 independent reports. Normal MOLLI-T1 standard deviations varied by 11-fold, and dependencies on TE, TR, and FA differed between 1.5 T and 3 T, thwarting meaningful T1 standardization even within a single field strength, including the use of Z-score. However, divergent MOLLI-T1 norms may be structured using data clustering. For 1.5 T, two clusters emerged: Cluster11.5T: T1 = 958 ± 16 ms (n = 1280); Cluster21.5T: T1 = 1027 ± 19 ms (n = 386). For 3 T, three clusters emerged: Cluster13T: T1 = 1160 ± 21 ms (n = 330); Cluster23T: T1 = 1067 ± 18 ms (n = 178); Cluster33T: T1 = 1227 ± 19 ms (n = 41). We then propose the concept of an online calculator for assigning local norms to a known MOLLI-T1 cluster, allowing benchmarking against published norms. CONCLUSION: Clustered structuring allows T1 standardization of widely-divergent MOLLI variants, benchmarking local norms (usually based on smaller samples) against published norms (larger samples). This may increase confidence and quality control in method implementation, facilitating wider clinical adoption of T1-mapping.

Deep learning with attention supervision for automated motion artefact detection in quality control of cardiac T1-mapping.

Cardiac magnetic resonance quantitative T1-mapping is increasingly used for advanced myocardial tissue characterisation. However, cardiac or respiratory motion can significantly affect the diagnostic utility of T1-maps, and thus motion artefact detection is critical for quality control and clinically-robust T1 measurements. Manual quality control of T1-maps may provide reassurance, but is laborious and prone to error. We present a deep learning approach with attention supervision for automated motion artefact detection in quality control of cardiac T1-mapping. Firstly, we customised a multi-stream Convolutional Neural Network (CNN) image classifier to streamline the process of automatic motion artefact detection. Secondly, we imposed attention supervision to guide the CNN to focus on targeted myocardial segments. Thirdly, when there was disagreement between the human operator and machine, a second human validator reviewed and rescored the cases for adjudication and to identify the source of disagreement. The multi-stream neural networks demonstrated 89.8% agreement, 87.4% ROC-AUC on motion artefact detection with the human operator in the 2568 T1 maps. Trained with additional supervision on attention, agreements and AUC significantly improved to 91.5% and 89.1%, respectively (p < 0.001). Rescoring of disagreed cases by the second human validator revealed that human operator error was the primary cause of disagreement. Deep learning with attention supervision provides a quick and high-quality assurance of clinical images, and outperforms human operators.