Post-contrast T1 Research Articles

Left ventricular T1-mapping in diastole versus systole in patients with mitral regurgitation

Cardiovascular magnetic resonance T1-mapping enables myocardial tissue characterisation, and is capable of quantifying both intracellular and extracellular volume. T1-mapping is conventionally performed in diastole, however, we hypothesised that systolic readout would reduce variability due to a reduction in myocardial blood volume. This study investigated whether T1-mapping in systole alters T1 values compared to diastole and whether reproducibility alters in atrial fibrillation compared to sinus rhythm. We prospectively identified 103 consecutive patients recruited to the Mitral FINDER study who had T1 mapping in systole and diastole. These patients had moderate or severe mitral regurgitation and a high incidence of ventricular dilatation and atrial fibrillation. T1, ECV and goodness-of-fit (R2) values of the T1 times were calculated offline using Circle cvi42 and in house-developed software. Systolic T1 mapping was associated with fewer myocardial segments being affected by artefact compared to diastolic T1 mapping [217/2472 (9%) vs 515/2472 (21%)]. Mean native T1 values were not significantly different when measured in systole and diastole (985 ± 26 ms vs 988 ± 29 respectively; p = 0.061) and mean post-contrast values showed similar good agreement (462 ± 32 ms vs 459 ± 33 respectively, p = 0.052). No clinically significant differences in ECV, native T1 and post-contrast T1 were identified between diastolic and systolic T1 maps in males versus females, or in patients with permanent atrial fibrillation versus sinus rhythm. A statistically significant improvement in R2 value was observed with systolic over diastolic T1 mapping in all analysed maps (n = 411) (96.2 ± 1.4% vs 96.0 ± 1.4%; p < 0.001) and in subgroup analyses [Sinus rhythm: 96.1 ± 1.4 vs 96.3 ± 1.4 (n = 327); p < 0.001. AF: 95.5 ± 1.3 vs 95.9 ± 1.2 (n = 80); p < 0.001] [Males: 95.8 ± 1.4 vs 96.1 ± 1.3 (n = 264); p < 0.001; Females: 96.2 ± 1.3 vs 96.4 ± 1.4 (n = 143); p = 0.009]. In conclusion, myocardial T1 mapping is associated with similar T1 and ECV values in systole and diastole. Furthermore, systolic acquisition is less prone to gating artefact in arrhythmia.

Magnetic resonance imaging spectrum of COVID-associated rhino-orbital-cerebral mucormycosis and assessment of anatomical severity.

To describe the extent and imaging findings of COVID-associated rhino-orbital-cerebral mucormycosis on magnetic resonance imaging and to evaluate the value of MRI severity score in grading the extent of involvement. Proven cases of ROCM with a history of concurrent or recently (<6weeks) treated COVID-19 underwent MRI at the initial presentation. Findings were charted for each anatomical structure and the extent of involvement was scored for sinonasal, extra-sinus soft tissues, orbits, and brain. MR severity score was defined by summing up the individual scores of each compartment (sinonasal 20, orbital 20, soft tissue 10, and brain 10) and a total score out of 60 was assigned. A total of 47 patients were included in our study with variable involvement of sinonasal compartment (n = 43), extra-sinus soft tissue (n = 25), orbits (n = 23), and brain (n = 17). In the sinonasal compartment, T2, DWI, and post-contrast T1 were the most useful sequences. A significantly higher mean sinonasal score was associated with mortality (p = 0.007). In the orbits, a combination of STIR (orbital fat and extraconal muscles), DWI (optic nerves), and post-contrast images (superior ophthalmic vein) were the most accurate sequences. A higher mean orbital score was associated with vision loss (p = 0.001). Patients with uncontrolled diabetes had greater extent of cranial involvement. A combination of magnetic resonance sequences is required to correctly evaluate the involvement of individual structures and thus to assign the correct MR scoring. The proposed MR severity score can effectively and objectively evaluate the severity of COVID-associated ROCM.

Comparison of Apparent Diffusion Coefficient Value Measurement and Contrast-Enhanced T1-Weighted Imaging in Detecting Active Demyelinating Lesions in Multiple Sclerosis

Objective: To find a difference in apparent diffusion coefficient (ADC) values of non-enhancing and enhancing lesions in patients with multiple sclerosis (MS) and to find a cutoff ADC value to predict the enhancement of the MS lesion. Materials and Methods: A retrospective review of magnetic resonance imaging (MRI) of the brain in MS patients between January 2015 and December 2019 was done. The MS plaques in the images were mapped in fluid attenuation inversion recovery (FLAIR) and post-contrast T1- weighted imaging sequences (T1WI). The lesions were categorized into enhancing and non-enhancing lesions based on the presence of enhancement on post-contrast T1WI. The ADC value of each lesion was measured using circular regions of interest (ROI) placed in the enhancing portion. The difference in mean ADC values between the two groups was assessed. Results: There were 22 patients with 194 MS lesions. The mean ADC values of the enhancing lesions at 0.891±0.164×10⁻³ mm²/s were significantly lower than those of the non-enhancing lesions at 1.303±0.280×10⁻³ mm²/s (p&lt;0.001). A cutoff ADC value of 1.117×10⁻³ mm²/s was used to distinguish between the enhancing and the non-enhancing lesions. The best results were obtained with a sensitivity of 93.9%, specificity of 71.4%, positive predictive value (PPV) of 40.3%, negative predictive value (NPV) of 98.3%, and accuracy of 75.3%. Conclusion: ADC value measurement can be used to predict the enhancement of the MS plaque, which may be helpful as a screening tool for active demyelinating plaque in MS patients. Keywords: Multiple sclerosis; Active demyelinating lesion; Active MS; Contrast-enhanced T1-weighted imaging; ADC value

Association between Myocardial Iron Overload and Diffuse Myocardial Fibrosis in Thalassemia Major

Cardiovascular magnetic resonance (CMR) is the gold standard for the non-invasive assessment of myocardial tissue properties. All CMR relaxation times (T1, T2 and T2*) are reduced in presence of iron deposits. In the clinical setting the T2* technique is the method of choice for cardiac iron quantification and it has revolutionized the management of patients with thalassemia major (TM). Myocardial extracellular volume( ECV) by CMR has been introduced as a good surrogate imaging marker of diffuse myocardial fibrosis. This the first study to evaluate the association between ECV values and all three relaxation times in TM. 108 β-TM patients (62 females, 40.16±8.83 years), consecutively enrolled in the Extension-Myocardial Iron Overload in Thalassemia Network, underwent CMR. T2*, T2, and native and post-contrast T1 values were assessed in all 16 myocardial segments. Segmental ECV values were calculated with input of native and post-contrast myocardial segmental and blood pool T1 values and same-day hematocrit. Global value was the mean of all segmental values. Significant MIO (global heart T2*<20ms) was found in 9 (8.3%) patients. Global ECV values were significantly higher in patients with significant MIO than in patients without significant MIO (40.07±10.62% vs. 31.85±5.71%; p=0.049) (Figure 1A). T2 images were acquired in 90 patients, of whom 40 (44.4%) had a normal global heart T2 value, 9 (8.3%) a reduced global heart T2 value, and 41 (45.6%) an increased global heart T2 value. Patients with reduced global heart T2 value showed significantly higher global ECV values than patients with normal (40.25±9.74% vs 30.54±5.49%; p<0.0001) as well as increased (40.25±9.74% vs 32.63±5.37%; p=0.002) global heart T2 value (Figure 1B). Global heart T1 values were normal in 54 (50%) patients, reduced in 50 (46.3%) patients and increased in 4 (3.7%) patients. Global ECV values tended to be higher among patients with increased T1 value (37.13±4.46%) versus both patients with normal or reduced T1 value (31.84±6.52% and 32.93±6.7%, respectively), but the difference among the three groups was not significant (p=0.259) (Figure 1C). The finding about the increased myocardial ECV in TM patients with reduced global heart T2* value seems to suggest that in TM MIO plays a crucial pathophysiological role in the development of diffuse myocardial fibrosis. This hypothesis is supported by the fact that patients with reduced global heart T2 value showed significantly higher global ECV values than patients with normal or increased global heart T2 value. As TM patients can develop chronic inflammation, the increased myocardial T2 value can be due to myocardial inflammation, that counterbalances the iron effects in non-heavily iron-overloaded hearts. Although myocardial inflammation can affect ECV, confounding the correlation between ECV and fibrosis, the absence of a difference in ECV values between patients with normal and increased global heart T2 value seems to suggest the dominance of fibrosis over inflammation. The absence of a link between decreased T1 and increased ECV may be explained by the fact that although in borderline patients T1 mapping has an higher sensitivity for MIO detection in comparison to the T2* technique, T1 values are not specific for iron and show prolongation in presence of diffuse myocardial fibrosis. Longitudinal prospective studies are needed to clarify the temporal association between myocardial iron overload and diffuse fibrosis and to obtain a better understanding of the implications of the presence of diffuse myocardial fibrosis. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

NIMG-08. AN INTEGRATED INFORMATICS MODEL COMBINING CLINICAL FACTORS, RADIOMICS AND A NOVEL CONNECTOMICS FRAMEWORK TO DISTINGUISH PATHOLOGICALLY-PROVEN RADIONECROSIS FROM PROGRESSION IN TREATED BRAIN METASTASES

Abstract PURPOSE/OBJECTIVE(S) To distinguish radionecrosis (RN) from true progression (TP) in brain metastases treated with stereotactic radiosurgery (SRS), we apply machine learning to create a multi-domain model that incorporates clinical factors, multiparametric radiomics(mpRads), and tumor connectomics, a novel MRI-based complex graph theory framework that describes the intricate network of relationships within the tumor and surrounding tissue. MATERIALS/METHODS Metastases treated with SRS that had pathologic confirmation of RN vs. TP after imaging progression were included from a single institution. Regions of interest were manually segmented using the single largest diameter of the T1 post-contrast(T1C) lesion plus the corresponding area of T2 FLAIR hyperintensity. We developed an Integrated Radiomics Informatics System (IRIS) based on an isomap support vector machine (IsoSVM) model to classify TP from RN using leave-one-out cross-validation (LOOCV). Class imbalance was resolved using differential misclassification weighting during model training using IRIS. Area under the receiver operating characteristic (AUC-ROC) and AUC-PR (precision recall) analysis were performed. RESULTS We analyzed 135 lesions in 110 patients. There were 43 cases (31.9%) of RN and 92 cases (68.1%) of TP. The top-performing connectomics features were degree centrality (increased with RN) and average path length (decreased with RN), suggesting greater “connectivity” and increased similarity in intralesional features between the T1C and FLAIR signal regions in RN cases. The top-performing radiomics feature was multidimensional entropy (increased in TP), demonstrating greater heterogeneity in TP cases. Finally, the top-performing clinical features were prior RT before SRS, histology, and treated lesion size. The LOOCV IsoSVM model successfully classified TP from RN with an AUC-ROC of 0.84 (95% CI: 0.77-0.90) and AUC-PR of 0.90 (95% CI: 0.82-0.95). The F1 score was 0.89. CONCLUSION Our novel machine-learning framework was able to efficiently combine features from multiple domains (i.e., radiomics, connectomics, and clinical factors) to distinguish pathologically-proven TP from RN with excellent discrimination.

NIMG-80. A RADIOMICS-BASED MODEL PREDICTIVE OF OVERALL SURVIVAL IN GLIOBLASTOMA USING MULTI-INSTITUTIONAL PREOPERATIVE MRI SCANS

Abstract PURPOSE Though patients with glioblastoma generally have poor overall survival (OS), OS estimates range significantly. We sought to develop a radiomics-based model predictive of OS in glioblastoma using preoperative MRI scans to identify these differences and guide clinical care. METHODS The primary endpoint was OS, determined from the date of surgery to death or last follow-up. T1, FLAIR, T2, and post-contrast T1 (T1c) MRI sequences were obtained for all patients. Using HD-GLIO-AUTO, lesion segmentation yielded three masks corresponding to the regions of contrast enhancement, non-enhancement, and total volume. Radiomics features were extracted from each region from the T2 and T1c sequences independently (i.e. either from T2 or T1c) and in combination. Age at surgery and gender were also considered. The data was split into training (75%) and test (25%) sets, stratifying for age at surgery. Kaplan-Meier analysis and the Coxnet model were used. For Coxnet, Elastic Net was used as the penalty, with optimization of the L1 coefficient using 5-fold cross-validation and evaluation via the concordance index (C-index). RESULTS From 3 institutions, 383 patients were included. Median OS was 395 days: 346 patients died, and 37 were right-censored. Age &amp;gt; 65 years was predictive of decreased OS (p &amp;lt; 0.005), but gender was not (p=0.16). For the Coxnet model, the C-index was 0.67 (standard deviation: 0.01) considering T2 and T1c sequences for the total lesion volume, with a C-index of 0.65 for the test set. For the enhancing volume, the model had a C-index of 0.71 for the test set using T1c sequences. Overall, C-indices ranged from 0.62-0.71. CONCLUSIONS We present a novel radiomics-based model predictive of OS in glioblastoma across a multi-institutional dataset. Ongoing efforts will incorporate immediate postoperative imaging and IDH and MGMT status into the proposed model to further improve prognostic ability and help guide clinical decision-making.

NIMG-72. VALIDATION OF CONDITIONAL AND SUPER-RESOLUTION GENERATIVE ADVERSARIAL NETWORKS FOR IMPUTATION OF MISSING BRAIN MRI SEQUENCES

Abstract Missing MRI sequences are common in radiology due to differences in protocols, time constraints, and image artifacts. Missing sequences are a nuisance for subsequent algorithms, such as automated segmentation algorithms requiring multiple MRI sequences (e.g., HD-GLIO, which requires T2, T1, post-contrast T1 (T1c), and FLAIR). Accurate tumor segmentation is a fundamental step in extracting radiomics analysis to use, for example, to predict the IDH status of glioma or their overall survival. We validated a conditional (pix2pix) and super-resolution GAN (SOUP-GAN) for imputing MRI sequences on 285 adult glioma with complete MRI sequences available. We simulated missing T1 sequences and subsequently imputed them from T1c using pix2pix and from sagittal T1 using SOUP-GAN. Using HD-GLIO, we obtained the true segmentations from the complete MRI sequences. HD-GLIO was also used after replacing the original T1 sequence with the one imputed with pix2pix and SOUP-GAN. Thus, there were three sets of segmentations for each subject. We compared T2 and T1c radiomics features extracted with Pyradiomics using segmentations obtained with complete data with features extracted from segmentation obtained with the imputed T1 sequences using intraclass correlation coefficient (ICC) and Bland-Altman (BA) analysis. Median (interquartile range) ICC for shape features extracted from T2 and T1c were 0.82(0.76-0.92) using pix2pix and 0.89(0.86-0.94) using SOUP-GAN. Median ICC for first-order-statistics features were 0.90(0.83-0.93) for T2 and 0.90(0.88-0.92) for T1c using pix2pix, and 0.61(0.22-0.82) for T2 and 0.42(0.34-0.57) for T1c using SOUP-GAN. Median ICC for texture features were 0.86(0.82-0.87) for T2 and 0.87(0.79-0.90) for T1c using pix2pix, and 0.69(0.47-0.81) for T2 and 0.76(0.65-0.83) for T1c using SOUP-GAN. We show that T2 and T1c shape features (e.g., volumetric analysis) can be adequately extracted from segmentations obtained with an algorithm requiring multiple MRI sequences using T1 sequences imputed with GANs; however, the effect of imputation differed for first-order-statistics and texture features.

NIMG-61. IMPROVED GENERALIZABILITY OF RADIOPATHOMIC PROBABILISTIC MAPPING OF TREATMENT-INDUCED EFFECTS WITH PHYSIOLOGIC MR IMAGING AND DEEP LEARNING IN PATIENTS WITH RECURRENT GLIOBLASTOMA

Abstract Although physiologic (diffusion-weighted and perfusion-weighted) MRI has shown promise in identifying regions of recurrent tumor (rTumor) in patients with glioblastoma suspected of progression, distinguishing treatment-induced effects (TxE) from rTumor on anatomical MRI remains a challenge. Whereas prior larger-scale machine learning (ML)-based studies mostly utilize anatomical imaging alone and/or perform lesion-level predictions, this study aimed to develop a non-invasive, radiopathomic tool for regional probabilistic mapping of TxE using 208 tissue-samples (55 pathologically-confirmed TxE, 153 recurrent glioblastoma) acquired from 107 patients with known spatial coordinates on pre-surgical MRI. We tested the hypothesis that applying a deep-learning (DL) model that included physiological MRI can: 1) more accurately identify areas of TxE that mimic rTumor on anatomical MRI and 2) better generalize to an independent test set than ML-models or a DL-model that uses anatomical MRI alone. An 80/20 split for training/validation was used after 1/3 of the patients were withheld for testing. Oversampling of TxE samples was employed to address class imbalance and an equal proportion of TxE samples was maintained across all datasets. Three ML-models, their ensemble, and a deep 4D-convolutional-neural-network were trained based on normalized anatomical (post-contrast T1, T2-FLAIR), diffusion-weighted (ADC, FA), and DSC perfusion-weighted (PeakHeight, %recovery) images cropped to 10mm-cubic patches centered on the coordinates from where tissue was obtained. Although Random Forest and voting-ensembled ML-models using all imaging and the anatomical DL-model had the best validation performance (AUC=0.81-0.82), these models did not generalize (test AUC=0.58-0.59). The DL-model including physiologic images had slightly lower validation AUC (0.78) but the best overall test AUC (0.795), indicating superior generalizability. Elevated blood volume (nPeakHeight) was the most important feature. Our DL-model’s interpretability was also demonstrated by disrupting class separation after shuffling voxels within each input patch. These results suggest that using deep-learning with physiologic MRI can improve intratumoral classification of TxE from rTumor.

NIMG-79. SPATIALLY MAPPED PREDICTIONS OF EVOLVING TUMOR RESPONSE OF HIGH-GRADE GLIOMA VIA IMAGE-DRIVEN MATHEMATICAL MODELING

Abstract Timely treatment response assessment of high-grade gliomas (HGG), crucial for driving therapeutic decisions, remains a challenge; as HGGs exhibit variable response to treatment within different sub-regions. Current assessment using multi-parametric MRI (mpMRI) depends largely upon follow-up (FU) imaging timepoints for achieving diagnostic certainty, which delays therapeutic interventions. Mathematical modeling (MM) of tumor growth and treatment response can provide spatiotemporal information of HGG evolution in response to treatment, thus allowing for prospective early identification of resilient tumor subregions. AIMS: We aim to initialize and calibrate an image-driven MM framework to forecast HGG response, both at the end of chemoradiotherapy (CRT) and at 3-month FU. METHODS In a prospective clinical study, weekly mpMRIs (post-contrast T1, T2 FLAIR, and diffusion) for patients with HGG receiving CRT were used to describe tumor extent and cellularity. This data collected from baseline (pre-CRT) till week 3 (mid-CRT) was used to calibrate a model family to forecast HGG response for each individual patient at week 6 (end CRT) and at 3-month FU. RESULTS Error between the forecasted and observed responses was assessed globally using percent error in tumor volume, and at the local level by Pearson correlation coefficient (PCC). In an initial cohort of 11 patients, our MM framework predictions had a percent error in tumor volume of less than 8.6% and at week 6 RT and less than 20% at 3 months FU. The PCCs were 0.84 at week 6 RT and 0.72 at 3 months FU. CONCLUSIONS Temporal consistency across this early evaluation of the model predictions show promise of image-driven MM for HGG response forecasting to guide timely personalized assessment and adjustment of treatment.

BIOM-48. HOXD12 IS AN INDEPENDENTLY PROGNOSTIC AGE-ASSOCIATED INDICATOR FOR POOR SURVIVAL IN OLIGODENDROGLIOMA

Abstract BACKGROUND Oligodendroglioma, IDH-mutant and 1p/19q-codeleted has variable outcomes that are strongly influenced by patient age. This study aimed to discover novel molecular biomarkers that may characterize an aggressive subgroup of oligodendroglioma that is associated with, but independently prognostic of patient age. METHODS Differential gene expression analysis, gene ontology analysis, and gene set enrichment analysis were conducted on TCGA RNA-seq data (N=169) to identify genes and pathways that differ significantly between older and younger patients. Genes whose expression was associated with age and survival in multivariate analyses were further studied using DNA methylation (N=171). The prognostic value of their expression and methylation profiles was then compared to known genomic biomarkers, radiographic features, and histopathologic features. RNA-seq and DNA methylation validation data were obtained from the CGGA (N=44) and a cohort published by Capper et al. (N=144), respectively. RESULTS Highly activated pathways in older oligodendrogliomas were linked to developmental transcription factors. Overexpression of HOXD12 was associated with patient age and survival in the TCGA (FDR&amp;lt; 0.01, FDR=1e-5) and the CGGA (p=0.03, p&amp;lt; 1e-3). Hypermethylation of HOXD12 was associated with age, tumor grade, and survival in the TCGA (p&amp;lt; 1e-5, p&amp;lt; 0.001, p&amp;lt; 1e-3) and with age and tumor grade in Capper et al. (p&amp;lt; 0.02, p=0.001). In the TCGA, HOXD12 hypermethylation and overexpression were independently prognostic of NOTCH1 and PIK3CA mutations, loss of 15q, and MYC activation as well as mitotic figures, Ki-67 index, microvascular proliferation, and necrosis. HOXD12 overexpression was also independently prognostic of T1-post contrast enhancement, whole tumor volume, and peritumoral edema volume. We developed a risk-stratification nomogram predicting 5- and 10-year overall survival using patient age, HOXD12 methylation, and WHO grade. CONCLUSIONS HOXD12 overexpression and hypermethylation are associated with an aggressive subtype of oligodendroglioma and may serve as superior prognostic biomarkers than previously reported genomic, radiographic, and histopathologic features.

Automated Inline Myocardial Segmentation of Joint T1 and T2 Mapping Using Deep Learning.

To develop an artificial intelligence (AI) solution for automated segmentation and analysis of joint cardiac MRI short-axis T1 and T2 mapping. In this retrospective study, a joint T1 and T2 mapping sequence was used to acquire 4240 maps from 807 patients across two hospitals between March and November 2020. Five hundred nine maps from 94 consecutive patients were assigned to a holdout testing set. A convolutional neural network was trained to segment the endocardial and epicardial contours with use of an edge probability estimation approach. Training labels were segmented by an expert cardiologist. Predicted contours were processed to yield mapping values for each of the 16 American Heart Association segments. Network segmentation performance and segment-wise measurements on the testing set were compared with those of two experts on the holdout testing set. The AI model was fully integrated using open-source software to run on MRI scanners. A total of 3899 maps (92%) were deemed artifact-free and suitable for human segmentation. AI segmentation closely matched that of each expert (mean Dice coefficient, 0.82 ± 0.07 [SD] vs expert 1 and 0.86 ± 0.06 vs expert 2) and compared favorably with interexpert agreement (Dice coefficient, 0.84 ± 0.06 for expert 1 vs expert 2). AI-derived segment-wise values for native T1, postcontrast T1, and T2 mapping correlated with expert-derived values (R 2 = 0.96, 0.98, and 0.87, respectively, vs expert 1, and 0.97, 0.99, and 0.92 vs expert 2) and fell within the range of interexpert reproducibility (R 2 = 0.97, 0.99, and 0.90, respectively). The AI model has since been deployed at two hospitals, enabling automated inline analysis. Automated inline analysis of joint T1 and T2 mapping allows accurate segment-wise tissue characterization, with performance equivalent to that of human experts.Keywords: MRI, Neural Networks, Cardiac, Heart Supplemental material is available for this article. © RSNA, 2022.

T1 Mapping MOLLI 5(3)3 Acquisition Scheme Yields High Accuracy in 1.5 T Cardiac Magnetic Resonance.

Objectives: To systematically compare two modified Look-Locker inversion recovery (MOLLI) T1 mapping sequences and their impact on (1) myocardial T1 values native, (2) post-contrast and (3) extracellular volume (ECV). Methods: 200 patients were prospectively included for 1.5 T CMR for work-up of ischemic or non-ischemic cardiomyopathies. To determine native and post-contrast T1 for ECV calculation, two different T1 mapping MOLLI acquisition schemes, 5(3)3 (designed for native scans with long T1) and 4(1)3(1)2 (designed for post-contrast scans with short T1), were acquired in identical mid-ventricular short-axis slices. Both schemes were acquired in native and post-contrast scans. Results: Datasets from 163 patients were evaluated (age 55 ± 17 years; 38% female). Myocardial T1 native for 5(3)3 was 1017 ± 42 ms vs. 956 ± 40 ms for 4(1)3(1)2, with mean intraindividual difference -61 ms (p &lt; 0.0001). Post-contrast myocardial T1 in patients was similar for both acquisition schemes, with 494 ± 48 ms for 5(3)3 and 490 ± 45 ms for 4(1)3(1)2 and mean intraindividual difference -4 ms. Myocardial ECV for 5(3)3 was 27.6 ± 4% vs. 27 ± 4% for 4(1)3(1)2, with mean difference -0.6 percentage points (p &lt; 0.0001). Conclusions: The T1 MOLLI 5(3)3 acquisition scheme provides a reliable estimation of myocardial T1 for the clinically relevant range of long and short T1 values native and post-contrast. In contrast, the T1 MOLLI 4(1)3(1)2 acquisition scheme may only be used for post-contrast scans according to its designed purpose.

Myocardial extracellular volume is a non-invasive tissue marker of heart failure in patients with transposition of the great arteries and systemic right ventricle.

Focal myocardial fibrosis in the systemic right ventricle (RV) is related to ventricular dysfunction and adverse outcome in patients with d-transposition of the great arteries (dTGA) post atrial redirection and those with congenitally corrected TGA (ccTGA). The role of diffuse fibrotic lesions in these conditions remains poorly understood. Our study aimed to investigate diffuse myocardial fibrosis by measuring extracellular volume (ECV) with cardiovascular magnetic resonance (CMR) and to explore correlations between ECV and clinical as well as functional markers of heart failure in patients with TGA and systemic RV. We prospectively included dTGA and ccTGA patients aged ≥14 years and compared them to healthy controls. Standardized CMR included modified Look-Locker Inversion recovery T1 mapping to quantify diffuse myocardial fibrosis in the systemic RV and the subpulmonary left ventricle (LV). The centerline of RV and LV myocardium was marked with a line of interest tool to determine native and post-contrast T1 for quantification of ECV. In total, 13 patients (dTGA: n = 8, ccTGA: n = 5) with a median age of 30.3 years were enrolled. LV ECV was higher in patients than in controls [34% (30%-41%) vs. 26% (23%-27%), p < 0.001], with values increased above the upper limit of normal in 10/13 patients (77%). RV ECV tended to be higher in patients than in controls, albeit without statistical significance [29% (27%-32%) vs. 28% (26%-29%), p = 0.316]. Patients with elevated LV ECV had lower LV ejection fraction than those with normal ECV (52 ± 5% vs. 65 ± 4%, p = 0.007). Correlations with clinical parameters were not observed. LV ECV was significantly higher than RV ECV (p = 0.016) in the patient group. In this study, LV ECV was significantly increased in TGA patients compared to controls, and was associated with LV dysfunction. Our data suggest that ECV may serve as a non-invasive tissue marker of heart failure in TGA with systemic RV. Further research is necessary to evaluate the prognostic implications and the potential role of ECV in monitoring disease progression and guiding therapy, aiming to maintain LV function or train the LV for subaortic location in TGA patients from infancy to adulthood.

Radiomics for differentiation of the posterior fossa pilocytic astrocytoma versus hemangioblastomas in adults. A pilot study

PurposeBoth pilocytic astrocytoma (PA) and hemangioblastoma (HB) are common primary neoplasms of the posterior fossa with similar radiological manifestations. This study was conducted to evaluate the role of Radiomics in differentiating these two conditions in adults. Materials and methodsAfter a retrospective search of our institutional imaging archive, adult patients with a known diagnosis of PA or HB were included. We reviewed each patient's most recent preoperative brain magnetic resonance imaging (MRI). The solid enhancing nodule of each lesion on post-contrast T1 sequence was manually segmented. Multiple Radiomics features were then extracted from each nodule using the Pyradiomics library. Subsequently, the most predictive features were identified by feature selection models. Following this, different machine learning (ML) models were constructed based on these selected features to classify lesions as PA or HB. Finally, we evaluated the performance of each model by leave-one-out cross-validation. ResultsWith inclusion and exclusion criteria, 34 enhancing PA nodules and 39 HB nodules were selected. A total of 115 features were extracted from each enhancing nodule. Twelve characteristics were detected as most predictive of histopathological diagnosis. Among various ML models, the neural network had the best performance in differentiating these two conditions with an AUC of 0.9 and an accuracy of 82%. ConclusionsIn this retrospective study, Radiomics MRI techniques demonstrated high performance in distinguishing adult posterior fossa PA from HB. Future development of Radiomics models may advance presurgical diagnosis of these two conditions when added to routine clinical practice and thus improve patient management.

Extracellular volume and left ventricular hypertrophy by cardiac magnetic resonance are independent predictors of cardiovascular outcome in obesity

This retrospective cohort study investigated for association between increased extracellular volume (ECV) and left ventricular hypertrophy (LVH) by cardiac magnetic resonance (CMR) and cardiovascular composite outcomes in obesity. Native T1 was measured at the ventricular septum. ECV was calculated from native and post-contrast T1 and hematocrit. Cardiovascular (CV) composite outcomes included acute myocardial infarction, unstable angina requiring hospitalization, myocardial revascularization (excluding early revascularization), heart failure, and CV death. A total of 456 patients with a mean follow-up of 2.1 ± 0.4 years were enrolled. LGE and LVH was detected in 30.5% and 9.2%. 107 patients (23.5%) had the composite outcomes. Multivariable analysis revealed that LGE, LVH, and high ECV as independent predictors for cardiovascular composite outcomes The event rate in the LVH and high ECV, the LVH alone, the high ECV alone, and the no-LVH with lower ECV group was 57.1%, 38.1%, 32.6%, and 17.7%, respectively. Assessment of incremental prognostic value by comparing global chi-square showed that high ECV had additional prognostic value on top of LGE, and LVH. LVH and high ECV are independent predictors of CV composite outcomes in obesity. This is the first study that demonstrate the prognostic value of ECV in obese population.

Cardiac MR fingerprinting with a short acquisition window in consecutive patients referred for clinical CMR and healthy volunteers

Cardiac Magnetic Resonance Fingerprinting (cMRF) has been demonstrated to enable robust and accurate T1 and T2 mapping for the detection of myocardial fibrosis and edema. However, the relatively long acquisition window (250 ms) used in previous cMRF studies might leave it vulnerable to motion artifacts in patients with high heart rates. The goal of this study was therefore to compare cMRF with a short acquisition window (154 ms) and low-rank reconstruction to routine cardiac T1 and T2 mapping at 1.5 T. Phantom studies showed that the proposed cMRF had a high T1 and T2 accuracy over a wider range than routine mapping techniques. In 9 healthy volunteers, the proposed cMRF showed small but significant myocardial T1 and T2 differences compared to routine mapping (ΔT1 = 1.5%, P = 0.031 and ΔT2 = − 7.1%, P < 0.001). In 61 consecutive patients referred for CMR, the native T1 values were slightly lower (ΔT1 = 1.6%; P = 0.02), while T2 values did not show statistical difference (ΔT2 = 4.3%; P = 0.11). However, the difference was higher in post-contrast myocardial T1 values (ΔT1 = 12.3%; P < 0.001), which was reflected in the extracellular volume (ΔECV = 2.4%; P < 0.001). Across all subjects, the proposed cMRF had a lower precision when compared to routine techniques, although its higher spatial resolution enabled the visualization of smaller details.

Can Spectroscopic Magnetic Resonance Imaging be Used to Delineate Recurrent Glioblastoma?

<h3>Purpose/Objective(s)</h3> Local glioblastoma cell volumes strongly correlate with whole brain spectroscopic MRI (sMRI) generated relative choline to N-Acetyl-Aspartate ratio (rChoNAA) maps at initial presentation. Significantly more disease is identified when rChoNAA maps, reflective of pathologically elevated metabolism and tumor replacement of normal brain, are combined with T1 post-contrast (T1PC) MRI. Despite primary radiotherapy (RT), glioblastoma almost always recurs. However, post-RT effects confound conventional MRI in recurrent glioblastoma (rGBM) where radiation necrosis appears like rGBM on T1PC, while benign edema and RT-induced leukoencephalopathy enlarge T2/FLAIR volumes. The uncertainty about tumor volume and fear of repeat radiotherapy (re-RT) toxicity in rGBM lead to re-RT volumes that typically only use T1PC and likely underestimate gross disease. We hypothesize that rChoNAA maps may uncover a larger volume of high-risk rGBM for enhanced re-RT targeting and dose escalation. <h3>Materials/Methods</h3> Retrospective analysis of an IRB-approved prospective cohort was performed on 14 rGBM patients that underwent sMRI as part of planning for re-RT. sMRI at 3T was acquired over the whole brain at 5.6 × 5.6 × 5 mm resolution in 15 minutes. Target volumes were delineated using rChoNAA ratio over 2 (rChoNAA>2) on sMRI similar to a prior prospective trial in upfront glioblastoma. rChoNAA maps and rChoNAA>2 contours were made available to the treating radiation oncologist through export to standard contouring software, and in all treated cases rChoNAA>2 volumes were included as part of the gross tumor volume. Volumes of rChoNAA>2 were compared with conventional multiparametric sequences on a routine clinical 3T MRI neuroimaging protocol including T1PC, T2/FLAIR, diffusion-restricted regions on apparent diffusion coefficient (ADC) maps, and perfusion relative cerebral blood volume (rCBV). <h3>Results</h3> rChoNAA>2 volumes (mean 29 cc, range 7-79 cc) were significantly different from all other imaging modalities using paired t-tests (p<0.01). T1PC volumes had a mean of 11 cc (range 1-31 cc). Compared to T1PC on a per patient basis, rChoNAA>2 volumes were 262% larger (range 23% smaller – 873% larger). FLAIR hyperintense volumes (mean 112 cc, range 19-233 cc) were much larger than all other modalities, reflecting likely benign and post-RT changes. Volumes of increased perfusion relative to surrounding brain were often small (mean 6.7 cc, range 0.2-19.1 cc) and volumes of restricted diffusion were often not identified and generally tiny (mean 0.8 cc, range 0-3.7 cc). <h3>Conclusion</h3> Multiparametric MRI techniques such as T1PC, FLAIR, ADC, and rCBV are confounded by post-RT effects in rGBM and are of questionable utility in re-RT planning. Similar to upfront treatment of GBM, sMRI suggests high-risk targets in rGBM undetected by conventional MRI. Pattern of failure analyses and radiologic-pathologic correlation studies are underway to confirm the utility of sMRI in rGBM.

Tumor Connectomics: A Novel MRI-Based Framework to Distinguish Pathologically-Proven Tumor Progression and Radiation Necrosis in Brain Metastases Treated with Radiosurgery

<h3>Purpose/Objective(s)</h3> Distinguishing true progression (TP) from radionecrosis (RN) in brain metastases after stereotactic radiosurgery (SRS) remains difficult. While traditional radiomics focuses on characterizing individual voxels, we applied tumor connectomics, a novel MRI-based complex graph framework that describes the intricate network of relationships within the tumor and surrounding tissue, to identifying TP vs. RN after SRS. <h3>Materials/Methods</h3> Brain metastases treated with SRS that underwent biopsy or resection for pathologic assessment of TP vs. RN were included from a single institution. The regions of interest were manually segmented based on the single largest diameter of the T1 post-contrast (T1C) enhancing lesion plus the corresponding area of T2 FLAIR hyperintensity, on the MRI images preceding surgical assessment typically within 5 days. The data was randomly allocated into training (80%) and test (20%) sets. The training data was standardized to zero mean and unit standard deviation. The standardization parameters were then applied to the test set to prevent any data leakage. We trained a linear support vector machine classifier using the connectomics graph theoretic metrics (consisting of node strength, degree centrality, betweenness centrality, average path length, and eigenvector centrality) in combination with biologically effective dose (BED10) to classify TP from RN. Class imbalance was resolved using the Synthetic Minority Over-sampling Technique (SMOTE). Area Under the Curve (AUC) analysis was performed. Statistical significance was set as p<0.05. <h3>Results</h3> We analyzed 135 SRS-treated lesions in 110 patients. The most common histologies were NSCLC (35.6%), breast (20.7%), and melanoma (20.0%). The median BED10 for SRS was 43.2 Gy (range: 28-60). There were 43 cases (31.9%) of pathologically proven RN and 92 cases (68.1%) of TP after SRS. The highest-performing connectomics features were degree centrality and average path length. Degree centrality was increased while average path length was decreased in RN cases compared to TP cases. This suggests a higher degree of connectivity in RN, with greater similarity in intralesional features between regions represented by the T1C and FLAIR signal. The model built using these two connectomics metrics had a sensitivity, specificity, and AUC-ROC of 0.85, 0.57, and 0.81 (95% CI: 0.61-0.93), respectively. Incorporating BED10 into the model improved performance to a sensitivity of 0.8 and specificity of 0.85, with AUC-ROC of 0.89 (95% CI: 0.7-0.98). <h3>Conclusion</h3> Our novel tumor connectomics machine-learning framework was able to distinguish pathologically-proven TP from RN with excellent discrimination, which may ultimately serve as a useful advanced imaging metric to guide clinical decision-making for radiographically progressive brain metastases.

The relationship between radiomics and pathomics in Glioblastoma patients: Preliminary results from a cross-scale association study.

Glioblastoma multiforme (GBM) typically exhibits substantial intratumoral heterogeneity at both microscopic and radiological resolution scales. Diffusion Weighted Imaging (DWI) and dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) are two functional MRI techniques that are commonly employed in clinic for the assessment of GBM tumor characteristics. This work presents initial results aiming at determining if radiomics features extracted from preoperative ADC maps and post-contrast T1 (T1C) images are associated with pathomic features arising from H&E digitized pathology images. 48 patients from the public available CPTAC-GBM database, for which both radiology and pathology images were available, were involved in the study. 91 radiomics features were extracted from ADC maps and post-contrast T1 images using PyRadiomics. 65 pathomic features were extracted from cell detection measurements from H&E images. Moreover, 91 features were extracted from cell density maps of H&E images at four different resolutions. Radiopathomic associations were evaluated by means of Spearman’s correlation (ρ) and factor analysis. p values were adjusted for multiple correlations by using a false discovery rate adjustment. Significant cross-scale associations were identified between pathomics and ADC, both considering features (n = 186, 0.45 < ρ < 0.74 in absolute value) and factors (n = 5, 0.48 < ρ < 0.54 in absolute value). Significant but fewer ρ values were found concerning the association between pathomics and radiomics features (n = 53, 0.5 < ρ < 0.65 in absolute value) and factors (n = 2, ρ = 0.63 and ρ = 0.53 in absolute value). The results of this study suggest that cross-scale associations may exist between digital pathology and ADC and T1C imaging. This can be useful not only to improve the knowledge concerning GBM intratumoral heterogeneity, but also to strengthen the role of radiomics approach and its validation in clinical practice as “virtual biopsy”, introducing new insights for omics integration toward a personalized medicine approach.

Multiparametric CMR tissue characterization differentiates pathological from physiological phenotypes

Abstract Background An abnormal increase of cardiomyocyte mass of the left ventricle is observed in physiological and pathological phenotypes of hypertrophy. Aims To apply CMR tissue characterization using native T1/T2 and post-contrast T1 mapping and identify tissue phenotypes corresponding to physiological and pathological hypertrophy, in athletes and heart failure (HF), respectively. Methods/Results 187 individuals were prospectively enrolled, in 4 groups: Athletes (n=56, 32±13 years), HF with and without preserved ejection fraction (HFpEF: n=49, 62±12 years; HFrEF: n=49, 54±16 years, H2FpEF-score: 4.8 [3–9]), and healthy controls (n=33, 41±13.7 years). All participants underwent cardiopulmonary exercise testing and a multiparametric CMR study to assess morphology/function, T2, native T1, extracellular volume fraction (ECV), and intracellular lifetime of water (a marker of cardiomyocyte diameter). As expected, LVEF varied significantly among groups (Athletes: 64.7±6.1%, HFpEF: 59.3±10.7%, HFrEF: 29.4±8.5%, and controls: 65.4±4.3%, p&amp;lt;0.001) and was markedly reduced in HFrEF. Both LV mass index (Athletes: 64.1±15.8 g/m2, HFpEF: 62.3±24 g/m2, HFrEF: 79.5±36.7 g/m2, and controls: 42±9.2 g/m2, p&amp;lt;0.001) and cardiomyocyte mass index (calculated as (1 − ECV) x LV mass/BSA) (Athletes: 47.9±13.1 g/m2, HFpEF: 42.2±17.2 g/m2, HFrEF: 55.69±24.70 g/m2, and controls: 30.7±6.9 g/m2, p&amp;lt;0.001, Fig. 1A) were elevated in athletes and HF, compared to controls. Athletes and HFpEF patients showed concentric LV remodeling, while the eccentric LV remodeling was observed in HFrEF (Fig. 1B). In the HF groups NT-proBNP was elevated (Athletes: 34.6±16.8 ng/dL, HFpEF: 473.2±700.1 ng/dL, HFrEF: 1,365.3±1,772 ng/dL, and controls: 34.3±29 ng/dL, p&amp;lt;0.005), and adjusted maximum oxygen consumption was markedly reduced (Athletes: 49.4±9.3 mL/kg/min, HFpEF: 18.3±5.5 mL/kg/min, HFrEF: 17.1±4.2 mL/kg/min, and controls: 30.3±10.2 mL/kg/min, p&amp;lt;0.005). ECV was larger in both HF groups (athletes: 0.27±0.04, HFpEF: 0.31±0.05, HFrEF: 0.32±0.04, and controls: 0.26±0.02, p&amp;lt;0.001). The intracellular lifetime of water was longer among athletes compared to controls and shorter in HFrEF compared to HFpEF (Athletes: 0.17±0.07, HFpEF: 0.15±0.05, HFrEF: 0.13±0.05, and controls: 0.14±0.05, p&amp;lt;0.001). Native T1 was reduced in athletes compared to controls and elevated in the HF groups (Athletes: 1,173.4±63.2 ms, HFpEF: 1,262.8±62.4 ms, HFrEF: 1,275.1±59.9 ms, and controls: 1,212.78±76.01 ms, p&amp;lt;0.001). Lastly, the an increased T2 was indicative of edema in HF patients (Fig. 2). Conclusions In a prospective observational study with CMR T1/T2 mapping, physiological hypertrophy is characterized by increased cardiomyocyte diameter, normal ECV, and a decrease in native T1, due to the larger cardiomyocyte volume. In contrast, with pathological hypertrophy in HF, is associated with an increased and an above-normal native T1. Cardiomyocyte diameter appears reduced in HFrEF compared to HFpEF, reflecting the transition to an eccentric LV shape. Funding Acknowledgement Type of funding sources: Public Institution(s). Main funding source(s): The São Paulo Research Foundation