Deformation-encoding Deep Learning Transformer for High-Frame-Rate Cardiac Cine MRI.

Purpose To develop a deep learning model for increasing cardiac cine frame rate while maintaining spatial resolution and scan time. Materials and Methods A transformer-based model was trained and tested on a retrospective sample of cine images from 5840 patients (mean age, 55 years ± 19 [SD]; 3527 male patients) referred for clinical cardiac MRI from 2003 to 2021 at nine centers; images were acquired using 1.5- and 3-T scanners from three vendors. Data from three centers were used for training and testing (4:1 ratio). The remaining data were used for external testing. Cines with downsampled frame rates were restored using linear, bicubic, and model-based interpolation. The root mean square error between interpolated and original cine images was modeled using ordinary least squares regression. In a prospective study of 49 participants referred for clinical cardiac MRI (mean age, 56 years ± 13; 25 male participants) and 12 healthy participants (mean age, 51 years ± 16; eight male participants), the model was applied to cines acquired at 25 frames per second (fps), thereby doubling the frame rate, and these interpolated cines were compared with actual 50-fps cines. The preference of two readers based on perceived temporal smoothness and image quality was evaluated using a noninferiority margin of 10%. Results The model generated artifact-free interpolated images. Ordinary least squares regression analysis accounting for vendor and field strength showed lower error (P < .001) with model-based interpolation compared with linear and bicubic interpolation in internal and external test sets. The highest proportion of reader choices was "no preference" (84 of 122) between actual and interpolated 50-fps cines. The 90% CI for the difference between reader proportions favoring collected (15 of 122) and interpolated (23 of 122) high-frame-rate cines was -0.01 to 0.14, indicating noninferiority. Conclusion A transformer-based deep learning model increased cardiac cine frame rates while preserving both spatial resolution and scan time, resulting in images with quality comparable to that of images obtained at actual high frame rates. Keywords: Functional MRI, Heart, Cardiac, Deep Learning, High Frame Rate Supplemental material is available for this article. © RSNA, 2024.

Cardiovascular Magnetic Resonance Radiomics to Identify Components of the Extracellular Matrix in Dilated Cardiomyopathy.

BACKGROUND: Current cardiovascular magnetic resonance sequences cannot discriminate between different myocardial extracellular space (ECSs), including collagen, noncollagen, and inflammation. We sought to investigate whether cardiovascular magnetic resonance radiomics analysis can distinguish between noncollagen and inflammation from collagen in dilated cardiomyopathy. METHODS: We identified data from 132 patients with dilated cardiomyopathy scheduled for an invasive septal biopsy who underwent cardiovascular magnetic resonance at 3 T. Cardiovascular magnetic resonance imaging protocol included native and postcontrast T1 mapping and late gadolinium enhancement (LGE). Radiomic features were computed from the midseptal myocardium, near the biopsy region, on native T1, extracellular volume (ECV) map, and LGE images. Principal component analysis was used to reduce the number of radiomic features to 5 principal radiomics. Moreover, a correlation analysis was conducted to identify radiomic features exhibiting a strong correlation (r>0.9) with the 5 principal radiomics. Biopsy samples were used to quantify ECS, myocardial fibrosis, and inflammation. RESULTS: Four histopathological phenotypes were identified: low collagen (n=20), noncollagenous ECS expansion (n=49), mild to moderate collagenous ECS expansion (n=42), and severe collagenous ECS expansion (n=21). Noncollagenous expansion was associated with the highest risk of myocardial inflammation (65%). Although native T1 and ECV provided high diagnostic performance in differentiating severe fibrosis (C statistic, 0.90 and 0.90, respectively), their performance in differentiating between noncollagen and mild to moderate collagenous expansion decreased (C statistic: 0.59 and 0.55, respectively). Integration of ECV principal radiomics provided better discrimination and reclassification between noncollagen and mild to moderate collagen (C statistic, 0.79; net reclassification index, 0.83 [95% CI, 0.45-1.22]; P<0.001). There was a similar trend in the addition of native T1 principal radiomics (C statistic, 0.75; net reclassification index, 0.93 [95% CI, 0.56-1.29]; P<0.001) and LGE principal radiomics (C statistic, 0.74; net reclassification index, 0.59 [95% CI, 0.19-0.98]; P=0.004). Five radiomic features per sequence were identified with correlation analysis. They showed a similar improvement in performance for differentiating between noncollagen and mild to moderate collagen (native T1, ECV, LGE C statistic, 0.75, 0.77, and 0.71, respectively). These improvements remained significant when confined to a single radiomic feature (native T1, ECV, LGE C statistic, 0.71, 0.70, and 0.64, respectively). CONCLUSIONS: Radiomic features extracted from native T1, ECV, and LGE provide incremental information that improves our capability to discriminate noncollagenous expansion from mild to moderate collagen and could be useful for detecting subtle chronic inflammation in patients with dilated cardiomyopathy.

Cardiovascular magnetic resonance characterization of myocardial tissue injury in a miniature swine model of cancer therapy-related cardiovascular toxicity.

BACKGROUND: Left ventricular ejection fraction (LVEF) is the most commonly clinically used imaging parameter for assessing cancer therapy-related cardiac dysfunction (CTRCD). However, LVEF declines may occur late, after substantial injury. This study sought to investigate cardiovascular magnetic resonance (CMR) imaging markers of subclinical cardiac injury in a miniature swine model. METHODS: Female Yucatan miniature swine (n = 14) received doxorubicin (2 mg/kg) every 3 weeks for 4 cycles. CMR, including cine, tissue characterization via T1 and T2 mapping, and late gadolinium enhancement (LGE) were performed on the same day as doxorubicin administration and 3 weeks after the final chemotherapy cycle. In addition, magnetic resonance spectroscopy (MRS) was performed during the 3 weeks after the final chemotherapy in 7 pigs. A single CMR and MRS exam were also performed in 3 Yucatan miniature swine that were age- and weight-matched to the final imaging exam of the doxorubicin-treated swine to serve as controls. CTRCD was defined as histological early morphologic changes, including cytoplasmic vacuolization and myofibrillar loss of myocytes, based on post-mortem analysis of humanely euthanized pigs after the final CMR exam. RESULTS: Of 13 swine completing 5 serial CMR scans, 10 (77%) had histological evidence of CTRCD. Three animals had neither histological evidence nor changes in LVEF from baseline. No absolute LVEF <40% or LGE was observed. Native T1, extracellular volume (ECV), and T2 at 12 weeks were significantly higher in swine with CTRCD than those without CTRCD (1178 ms vs. 1134 ms, p = 0.002, 27.4% vs. 24.5%, p = 0.03, and 38.1 ms vs. 36.4 ms, p = 0.02, respectively). There were no significant changes in strain parameters. The temporal trajectories in native T1, ECV, and T2 in swine with CTRCD showed similar and statistically significant increases. At the same time, there were no differences in their temporal changes between those with and without CTRCD. MRS myocardial triglyceride content substantially differed among controls, swine with and without CTRCD (0.89%, 0.30%, 0.54%, respectively, analysis of variance, p = 0.01), and associated with the severity of histological findings and incidence of vacuolated cardiomyocytes. CONCLUSION: Serial CMR imaging alone has a limited ability to detect histologic CTRCD beyond LVEF. Integrating MRS myocardial triglyceride content may be useful for detection of early potential CTRCD.

Optimal Protocol for Contrast-enhanced Free-running 5D Whole-heart Coronary MR Angiography at 3T.

Free-running 5D whole-heart coronary MR angiography (MRA) is gaining in popularity because it reduces scanning complexity by removing the need for specific slice orientations, respiratory gating, or cardiac triggering. At 3T, a gradient echo (GRE) sequence is preferred in combination with contrast injection. However, neither the injection scheme of the gadolinium (Gd) contrast medium, the choice of the RF excitation angle, nor the dedicated image reconstruction parameters have been established for 3T GRE free-running 5D whole-heart coronary MRA. In this study, a Gd injection scheme, RF excitation angles of lipid-insensitive binominal off-resonance RF excitation (LIBRE) pulse for valid fat suppression and continuous data acquisition, and compressed-sensing reconstruction regularization parameters were optimized for contrast-enhanced free-running 5D whole-heart coronary MRA using a GRE sequence at 3T. Using this optimized protocol, contrast-enhanced free-running 5D whole-heart coronary MRA using a GRE sequence is feasible with good image quality at 3T.

Radiomics of Late Gadolinium Enhancement Reveals Prognostic Value of Myocardial Scar Heterogeneity in Hypertrophic Cardiomyopathy.

BACKGROUND: Late gadolinium enhancement (LGE) scar burden by cardiac magnetic resonance is a major risk factor for sudden cardiac death (SCD) in hypertrophic cardiomyopathy (HCM). However, there is currently limited data on the incremental prognostic value of integrating myocardial LGE radiomics (ie, shape and texture features) into SCD risk stratification models. OBJECTIVES: The purpose of this study was to investigate the incremental prognostic value of myocardial LGE radiomics beyond current European Society of Cardiology (ESC) and American College of Cardiology (ACC)/American Heart Association (AHA) models for SCD risk prediction in HCM. METHODS: A total of 1,229 HCM patients (62% men; age 52 ± 16 years) from 3 medical centers were included. Left ventricular myocardial radiomic features were calculated from LGE images. Principal component analysis was used to reduce the radiomic features and calculate 3 principal radiomics (PrinRads). Cox and logistic regression analyses were then used to evaluate the significance of the extracted PrinRads of LGE images, alone or in combination with ESC or ACC/AHA models, to predict SCD risk. The ACC/AHA risk markers include LGE burden using a dichotomized 15% threshold of LV scar. RESULTS: SCD events occurred in 30 (2.4%) patients over a follow-up period of 49 ± 28 months. Risk prediction using PrinRads resulted in higher c-statistics than the ESC (0.69 vs 0.57; P = 0.02) and the ACC/AHA (0.69 vs 0.67; P = 0.75) models. Risk predictions were improved by combining the 3 PrinRads with ESC (0.73 vs 0.57; P < 0.01) or ACC/AHA (0.76 vs 0.67; P < 0.01) risk scores. The net reclassification index was improved by combining the PrinRads with ESC (0.25 [95% CI: 0.08-0.43]; P = 0.005) or ACC/AHA (0.05 [95% CI: -0.07 to 0.16]; P = 0.42) models. One PrinRad was a significant predictor of SCD risk (HR: 0.57 [95% CI: 0.39-0.84]; P = 0.01). LGE heterogeneity was a major component of PrinRads and a significant predictor of SCD risk (HR: 0.07 [95% CI: 0.01-0.75]; P = 0.03). CONCLUSIONS: Myocardial LGE radiomics are strongly associated with SCD risk in HCM and provide incremental risk stratification beyond current ESC or AHA/ACC risk models. Our proof-of-concept study warrants further validation.

Highly accelerated free-breathing real-time myocardial tagging for exercise cardiovascular magnetic resonance.

BACKGROUND: Exercise cardiovascular magnetic resonance (Ex-CMR) myocardial tagging would enable quantification of myocardial deformation after exercise. However, current electrocardiogram (ECG)-segmented sequences are limited for Ex-CMR. METHODS: We developed a highly accelerated balanced steady-state free-precession real-time tagging technique for 3 T. A 12-fold acceleration was achieved using incoherent sixfold random Cartesian sampling, twofold truncated outer phase encoding, and a deep learning resolution enhancement model. The technique was tested in two prospective studies. In a rest study of 27 patients referred for clinical CMR and 19 healthy subjects, a set of ECG-segmented for comparison and two sets of real-time tagging images for repeatability assessment were collected in 2-chamber and short-axis views with spatiotemporal resolution 2.0 × 2.0 mm2 and 29 ms. In an Ex-CMR study of 26 patients with known or suspected cardiac disease and 23 healthy subjects, real-time images were collected before and after exercise. Deformation was quantified using measures of short-axis global circumferential strain (GCS). Two experienced CMR readers evaluated the image quality of all real-time data pooled from both studies using a 4-point Likert scale for tagline quality (1-excellent; 2-good; 3-moderate; 4-poor) and artifact level (1-none; 2-minimal; 3-moderate; 4-significant). Statistical evaluation included Pearson correlation coefficient (r), intraclass correlation coefficient (ICC), and coefficient of variation (CoV). RESULTS: In the rest study, deformation was successfully quantified in 90% of cases. There was a good correlation (r = 0.71) between ECG-segmented and real-time measures of GCS, and repeatability was good to excellent (ICC = 0.86 [0.71, 0.94]) with a CoV of 4.7%. In the Ex-CMR study, deformation was successfully quantified in 96% of subjects pre-exercise and 84% of subjects post-exercise. Short-axis and 2-chamber tagline quality were 1.6 ± 0.7 and 1.9 ± 0.8 at rest and 1.9 ± 0.7 and 2.5 ± 0.8 after exercise, respectively. Short-axis and 2-chamber artifact level was 1.2 ± 0.5 and 1.4 ± 0.7 at rest and 1.3 ± 0.6 and 1.5 ± 0.8 post-exercise, respectively. CONCLUSION: We developed a highly accelerated real-time tagging technique and demonstrated its potential for Ex-CMR quantification of myocardial deformation. Further studies are needed to assess the clinical utility of our technique.

Microvascular Dysfunction in Patients with Idiopathic Dilated Cardiomyopathy: Quantitative Assessment with Phase Contrast Cine MR Imaging of the Coronary Sinus.

PURPOSE: The purposes of this study were to compare global coronary flow reserve (CFR) between patients with idiopathic dilated cardiomyopathy (DCM) and risk-matched controls using cardiac MRI (CMR), and to evaluate the relationship between global CFR and CMR left ventricular (LV) parameters. METHODS: Twenty-six patients with DCM and 26 risk-matched controls who underwent comprehensive CMR examination, including stress-rest coronary sinus flow measurement by phase contrast (PC) cine CMR were retrospectively studied. LV peak global longitudinal, radial, and circumferential strains (GLS, GRS, and GCS) were determined by feature tracking. RESULTS: Patients with DCM had significantly lower global CFR compared with the risk-matched controls (2.87 ± 0.86 vs. 4.03 ± 1.47, P = 0.001). Among the parameters, univariate linear regression analyses revealed significant correlation of global CFR with LV end-diastolic volume index (r = -0.396, P = 0.045), LV mass index (r = -0.461, P = 0.018), GLS (r = -0.558, P = 0.003), and GRS (r = 0.392, P = 0.047). Multiple linear regression analysis revealed GLS as the only independent predictor of global CFR (standardized ß = -0.558, P = 0.003). CONCLUSION: Global CFR was significantly impaired in patients with idiopathic DCM and independently associated with LV GLS, suggesting that microvascular dysfunction may contribute to deterioration of LV function in patients with idiopathic DCM.

Accelerated Cardiac MRI Cine with Use of Resolution Enhancement Generative Adversarial Inline Neural Network.

Background Cardiac cine can benefit from deep learning-based image reconstruction to reduce scan time and/or increase spatial and temporal resolution. Purpose To develop and evaluate a deep learning model that can be combined with parallel imaging or compressed sensing (CS). Materials and Methods The deep learning model was built on the enhanced super-resolution generative adversarial inline neural network, trained with use of retrospectively identified cine images and evaluated in participants prospectively enrolled from September 2021 to September 2022. The model was applied to breath-hold electrocardiography (ECG)-gated segmented and free-breathing real-time cine images collected with reduced spatial resolution with use of generalized autocalibrating partially parallel acquisitions (GRAPPA) or CS. The deep learning model subsequently restored spatial resolution. For comparison, GRAPPA-accelerated cine images were collected. Diagnostic quality and artifacts were evaluated by two readers with use of Likert scales and compared with use of Wilcoxon signed-rank tests. Agreement for left ventricle (LV) function, volume, and strain was assessed with Bland-Altman analysis. Results The deep learning model was trained on 1616 patients (mean age ± SD, 56 years ± 16; 920 men) and evaluated in 181 individuals, 126 patients (mean age, 57 years ± 16; 77 men) and 55 healthy subjects (mean age, 27 years ± 10; 15 men). In breath-hold ECG-gated segmented cine and free-breathing real-time cine, the deep learning model and GRAPPA showed similar diagnostic quality scores (2.9 vs 2.9, P = .41, deep learning vs GRAPPA) and artifact score (4.4 vs 4.3, P = .55, deep learning vs GRAPPA). Deep learning acquired more sections per breath-hold than GRAPPA (3.1 vs one section, P < .001). In free-breathing real-time cine, the deep learning showed a similar diagnostic quality score (2.9 vs 2.9, P = .21, deep learning vs GRAPPA) and lower artifact score (3.9 vs 4.3, P < .001, deep learning vs GRAPPA). For both sequences, the deep learning model showed excellent agreement for LV parameters, with near-zero mean differences and narrow limits of agreement compared with GRAPPA. Conclusion Deep learning-accelerated cardiac cine showed similarly accurate quantification of cardiac function, volume, and strain to a standardized parallel imaging method. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Vannier and Wang in this issue.

Complementary prognostic value of stress perfusion imaging and global coronary flow reserve derived from cardiovascular magnetic resonance: a long-term cohort study.

BACKGROUND: Phase-contrast cine cardiovascular magnetic resonance (CMR) quantifies global coronary flow reserve (CFR) by measuring blood flow in the coronary sinus (CS), allowing assessment of the entire coronary circulation. However, the complementary prognostic value of stress perfusion CMR and global CFR in long-term follow-up has yet to be investigated. This study aimed to investigate the complementary prognostic value of stress myocardial perfusion imaging and global CFR derived from CMR in patients with suspected or known coronary artery disease (CAD) during long-term follow-up. METHODS: Participants comprised 933 patients with suspected or known CAD who underwent comprehensive CMR. Major adverse cardiac events (MACE) comprised cardiac death, non-fatal myocardial infarction, unstable angina, hospitalization for heart failure, stroke, ventricular arrhythmia, and late revascularization. RESULTS: During follow-up (median, 5.3 years), there were 223 MACE. Kaplan-Meier curve analysis revealed a significant difference in event-free survival among tertile groups for global CFR (log-rank, p < 0.001) and between patients with and without ischemia (p < 0.001). The combination of stress perfusion CMR and global CFR enhanced risk stratification (p < 0.001 for overall), and prognoses were comparable between the subgroup with ischemia and no impaired CFR and the subgroup with no ischemia and impaired CFR (p = 0.731). Multivariate Cox proportional hazard regression analysis showed that impaired CFR remained a significant predictor for MACE (hazard ratio, 1.6; p = 0.002) when adjusted for coronary risk factors and CMR predictors, including ischemia. The addition of impaired CFR to coronary risk factors and ischemia significantly increased the global chi-square value from 88 to 109 (p < 0.001). Continuous net reclassification improvement and integrated discrimination with the addition of global CFR to coronary risk factors plus ischemia improved to 0.352 (p < 0.001) and 0.017 (p < 0.001), respectively. CONCLUSIONS: During long-term follow-up, stress perfusion CMR and global CFR derived from CS flow measurement provided complementary prognostic value for prediction of cardiovascular events. Microvascular dysfunction or diffuse atherosclerosis as shown by impaired global CFR may play a role as important as that of ischemia due to epicardial coronary stenosis in the risk stratification of CAD patients.

The cardiac computed tomography-derived extracellular volume fraction predicts patient outcomes and left ventricular mass reductions after transcatheter aortic valve implantation for aortic stenosis.

BACKGROUND: Transcatheter aortic valve implantation (TAVI) improved outcome of patients with severe aortic valve stenosis (AS). Myocardial fibrosis is associated with AS-related pathological left ventricular (LV) remodeling and predicts cardiovascular mortality after TAVI. The present study aimed to investigate the impact of preoperative extracellular volume (ECV) assessed by computed tomography (CT) on left ventricular mass (LVM) regression and clinical outcomes in severe AS patients after TAVI. METHODS: We examined 71 consecutive severe AS patients who underwent CT with ECV determination before TAVI. ECV was calculated as the ratio of the change in Hounsfield units in the myocardium and LV blood before and after contrast administration, multiplied by (1-hematocrit). Delayed scan was performed at 5 min after contrast injection. Echocardiography was performed before and 6 months after TAVI. The primary endpoint was heart failure (HF) hospitalization after TAVI. Patients were divided into two subgroups according to the median value of global ECV with 32 % (Low-ECV group: n = 35, and High-ECV group: n = 36). RESULTS: No significant differences were observed in background characteristics between the 2 groups. However, the preoperative LV ejection fraction and LVM index were similar between the 2 groups, the Low-ECV group had greater LVM index reduction than the High-CV group after 6 months (p < 0.001). Kaplan-Meier curves demonstrated that the High-ECV group had significantly higher rate of HF hospitalization than the Low-ECV group (p = 0.016). In addition, multivariate analyses identified high global ECV as an independent predictor of HF hospitalization (HR 10.8, 95 % confidence interval 1.36 to 84.8, p = 0.024). CONCLUSION: The low preoperative ECV assessed by CT is associated with the greater LVM regression, and predict better outcome in AS patients after TAVI.

Obesity-Related Differences in Pathomechanism and Outcomes in Patients With HFpEF: A CMR Study.

Background: Clinical significance of an integrated evaluation of epicardial adipose tissue (EAT) and the right ventricle (RV) in heart failure with preserved ejection fraction (HFpEF) is unknown. Objectives: The authors investigated the potential of EAT and RV quantification for obesity-related pathophysiology and risk stratification in obese HFpEF patients using cardiovascular magnetic resonance (CMR). Methods: A total of 150 patients (obese, body mass index ≥30 kg/m2; n = 73, nonobese, body mass index <30 kg/m2; n = 77) with a clinical diagnosis of HFpEF undergoing CMR were retrospectively identified. EAT volume surrounding both ventricles were quantified with manual delineation on cine images. Total RV volume (TRVV) was calculated as the sum of RV cavity and mass at end-diastole. The endpoint was the composite of all-cause mortality and first HF hospitalization. Results: During a median follow-up of 46 months, 39 nonobese patients (51%) and 32 obese patients (44%) experienced the endpoint. EAT was a prognostic biomarker regardless of obesity and was independently correlated with TRVV. In obese HFpEF, EAT correlated with RV longitudinal strain (r = 0.32, P = 0.006), and increased amount of EAT and TRVV was associated with greater left ventricular end-diastolic eccentric index (r = 0.36, P = 0.002). The integration of RV quantification into EAT provided improved risk stratification with a C-statistic increase from 0.70 to 0.79 in obese HFpEF. Obese patients with EAT<130 ml and TRVV<180 ml had low risk (annual event rate 3.2%), while those with increased EAT ≥130 ml and TRVV ≥180 ml had significantly higher risk (annual event rate 11.8%; P < 0.001). Conclusions: CMR quantification of EAT and RV structure provides additive risk stratification for adverse outcomes in obese HFpEF.

Recurrent Myocardial Infarction in a Patient With MINOCA and a Negative Ergometrine Provocation Test.

This case highlights the diagnostic challenge associated with coronary vasospasm, especially when accompanied by no provoked spasms on the ergometrine test in the acute setting of myocardial infarction with nonobstructive coronary arteries. Inexplicable myocardial infarction with nonobstructive coronary arteries after extensive evaluation should alert the clinician to the possibility of coronary vasospasm and the necessity of acetylcholine provocative testing in spite of a negative ergometrine provocation test. (Level of Difficulty: Intermediate.).

Myocardial tissue imaging with cardiovascular magnetic resonance.

Alteration in myocardial tissue, such as myocardial fibrosis, edema, inflammation, or accumulation with amyloid, lipids, or iron, has an important role in the cardiac remodeling that leads to diastolic and/or systolic dysfunction and the development of chronic heart failure, increasing the risk of adverse cardiovascular events. Thus, the early detection of changes at myocardial tissue level has great diagnostic and prognostic potential. The gold standard technique to assess these myocardial alterations is endomyocardial biopsy. However, this has been limited to a few patients due to the invasive nature, sampling errors, and its inability to assess the entire myocardium. Cardiovascular magnetic resonance (CMR) has emerged as the gold standard imaging not only for assessing cardiac volume, function quantification, and viability but also for noninvasive myocardial tissue characterization over the past decade. Its ability to characterize myocardial tissue composition is unique among noninvasive imaging modalities in cardiovascular disease. Currently, multi-parametric myocardial characterization with T1, T2, and extracellular volume has the potential to identify and track diffuse pathology in various diseases. In this review article, we present the role of established and emerging CMR techniques in myocardial tissue characterization, with an emphasis on T1 and T2 mapping, in clinical practice.

Subtle-but-smouldering myocardial injury after immune checkpoint inhibitor treatment accompanied by amyloid deposits.

Although cardiac troponin is a highly specific biomarker for myocardial cell injury, it is important to recognize the pitfalls of this test in the diagnosis and management of immune checkpoint inhibitor (ICI) myocarditis. We describe the challenging case of an 81-year-old woman with persistently high troponin after undergoing immunotherapy with ipilimumab and nivolumab, and histological evidence of amyloid deposition in the myocardium. The patient received immunosuppressive treatments based on the magnitude of troponin changes because myocarditis was clinically suspected. However, histological examination revealed the deposition of transthyretin amyloid fibrils with only minimal T-lymphocyte infiltration and no myocyte necrosis, suggesting transthyretin cardiac amyloidosis rather than ICI myocarditis. This case highlights the importance of assessing other causes of persistently high troponin, and the necessity of incorporating comprehensive histological and immunohistochemical examinations of the endomyocardial biopsy, especially when cardiovascular magnetic resonance imaging is inconclusive.