Recent Update of Advanced Imaging for Diagnosis of Cardiac Sarcoidosis: Based on the Findings of Cardiac Magnetic Resonance Imaging and Positron Emission Tomography

Chang, Suyon,Lee, Won Woo,Chun, Eun Ju Korean Society of Magnetic Resonance in Medicine 2019 Investigative Magnetic Resonance Imaging Vol.23 No.2

Sarcoidosis is a multisystem disease characterized by noncaseating granulomas. Cardiac involvement is known to have poor prognosis because it can manifest as a serious condition such as the conduction abnormality, heart failure, ventricular arrhythmia, or sudden cardiac death. Although early diagnosis and early treatment is critical to improve patient prognosis, the diagnosis of CS is challenging in most cases. Diagnosis usually relies on endomyocardial biopsy (EMB), but its diagnostic yield is low due to the incidence of patchy myocardial involvement. Guidelines for the diagnosis of CS recommend a combination of clinical, electrocardiographic, and imaging findings from various modalities, if EMB cannot confirm the diagnosis. Especially, the role of advanced imaging such as cardiac magnetic resonance (CMR) imaging and positron emission tomography (PET), has shown to be important not only for the diagnosis, but also for monitoring treatment response and prognostication. CMR can evaluate cardiac function and fibrotic scar with good specificity. Late gadolinium enhancement (LGE) in CMR shows a distinctive enhancement pattern for each disease, which may be useful for differential diagnosis of CS from other similar diseases. Effectively, T1 or T2 mapping techniques can be also used for early recognition of CS. In the meantime, PET can detect and quantify metabolic activity and can be used to monitor treatment response. Recently, the use of a hybrid CMR-PET has introduced to allow identify patients with active CS with excellent co-localization and better diagnostic accuracy than CMR or PET alone. However, CS may show various findings with a wide spectrum, therefore, radiologists should consider the possible differential diagnosis of CS including myocarditis, dilated cardiomyopathy (DCM), hypertrophic cardiomyopathy, amyloidosis, and arrhythmogenic right ventricular cardiomyopathy. Radiologists should recognize the differences in various diseases that show the characteristics of mimicking CS, and try to get an accurate diagnosis of CS.

Automated Measurement of Native T1 and Extracellular Volume Fraction in Cardiac Magnetic Resonance Imaging Using a Commercially Available Deep Learning Algorithm

Chang Suyon,Han Kyunghwa,Lee Suji,Yang Young Joong,Kim Pan Ki,Choi Byoung Wook,서영주 대한영상의학회 2022 Korean Journal of Radiology Vol.23 No.12

Objective: T1 mapping provides valuable information regarding cardiomyopathies. Manual drawing is time consuming and prone to subjective errors. Therefore, this study aimed to test a DL algorithm for the automated measurement of native T1 and extracellular volume (ECV) fractions in cardiac magnetic resonance (CMR) imaging with a temporally separated dataset. Materials and Methods: CMR images obtained for 95 participants (mean age ± standard deviation, 54.5 ± 15.2 years), including 36 left ventricular hypertrophy (12 hypertrophic cardiomyopathy, 12 Fabry disease, and 12 amyloidosis), 32 dilated cardiomyopathy, and 27 healthy volunteers, were included. A commercial deep learning (DL) algorithm based on 2D U-net (Myomics-T1 software, version 1.0.0) was used for the automated analysis of T1 maps. Four radiologists, as study readers, performed manual analysis. The reference standard was the consensus result of the manual analysis by two additional expert readers. The segmentation performance of the DL algorithm and the correlation and agreement between the automated measurement and the reference standard were assessed. Interobserver agreement among the four radiologists was analyzed. Results: DL successfully segmented the myocardium in 99.3% of slices in the native T1 map and 89.8% of slices in the post-T1 map with Dice similarity coefficients of 0.86 ± 0.05 and 0.74 ± 0.17, respectively. Native T1 and ECV showed strong correlation and agreement between DL and the reference: for T1, r = 0.967 (95% confidence interval [CI], 0.951–0.978) and bias of 9.5 msec (95% limits of agreement [LOA], -23.6–42.6 msec); for ECV, r = 0.987 (95% CI, 0.980–0.991) and bias of 0.7% (95% LOA, -2.8%–4.2%) on per-subject basis. Agreements between DL and each of the four radiologists were excellent (intraclass correlation coefficient [ICC] of 0.98–0.99 for both native T1 and ECV), comparable to the pairwise agreement between the radiologists (ICC of 0.97–1.00 and 0.99–1.00 for native T1 and ECV, respectively). Conclusion: The DL algorithm allowed automated T1 and ECV measurements comparable to those of radiologists.

T1 Map-Based Radiomics for Prediction of Left Ventricular Reverse Remodeling in Patients With Nonischemic Dilated Cardiomyopathy

Chang Suyon,Han Kyunghwa,Kwon Yonghan,Kim Lina,Hwang Seunghyun,Kim Hwiyoung,Choi Byoung Wook 대한영상의학회 2023 Korean Journal of Radiology Vol.24 No.5

Objective: This study aimed to develop and validate models using radiomics features on a native T1 map from cardiac magnetic resonance (CMR) to predict left ventricular reverse remodeling (LVRR) in patients with nonischemic dilated cardiomyopathy (NIDCM). Materials and Methods: Data from 274 patients with NIDCM who underwent CMR imaging with T1 mapping at Severance Hospital between April 2012 and December 2018 were retrospectively reviewed. Radiomic features were extracted from the native T1 maps. LVRR was determined using echocardiography performed ≥ 180 days after the CMR. The radiomics score was generated using the least absolute shrinkage and selection operator logistic regression models. Clinical, clinical + late gadolinium enhancement (LGE), clinical + radiomics, and clinical + LGE + radiomics models were built using a logistic regression method to predict LVRR. For internal validation of the result, bootstrap validation with 1000 resampling iterations was performed, and the optimism-corrected area under the receiver operating characteristic curve (AUC) with 95% confidence interval (CI) was computed. Model performance was compared using AUC with the DeLong test and bootstrap. Results: Among 274 patients, 123 (44.9%) were classified as LVRR-positive and 151 (55.1%) as LVRR-negative. The optimismcorrected AUC of the radiomics model in internal validation with bootstrapping was 0.753 (95% CI, 0.698–0.813). The clinical + radiomics model revealed a higher optimism-corrected AUC than that of the clinical + LGE model (0.794 vs. 0.716; difference, 0.078 [99% CI, 0.003–0.151]). The clinical + LGE + radiomics model significantly improved the prediction of LVRR compared with the clinical + LGE model (optimism-corrected AUC of 0.811 vs. 0.716; difference, 0.095 [99% CI, 0.022–0.139]). Conclusion: The radiomic characteristics extracted from a non-enhanced T1 map may improve the prediction of LVRR and offer added value over traditional LGE in patients with NIDCM. Additional external validation research is required.

Cardiac Magnetic Resonance in the Aging Heart

Suyon Chang 한국심초음파학회 2022 Journal of Cardiovascular Imaging (J Cardiovasc Im Vol.30 No.3

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Predictive factors for treatment response using dual-energy computed tomography in patients with advanced lung adenocarcinoma

Hong, Sae Rom,Hur, Jin,Moon, Yong Wha,Han, Kyunghwa,Chang, Suyon,Kim, Jin Young,Im, Dong Jin,Suh, Young Joo,Hong, Yoo Jin,Lee, Hye-Jeong,Kim, Young Jin,Choi, Byoung Wook Elsevier 2018 European journal of radiology Vol.101 No.-

<P>Conclusions: Dual-energy CT using a quantitative analytic method based on iodine concentration measurements can be used to predict the effects of chemotherapy in patients with advanced adenocarcinoma.</P>

Myocardial T1 and T2 Mapping: Techniques and Clinical Applications

Kim, Pan Ki,Hong, Yoo Jin,Im, Dong Jin,Suh, Young Joo,Park, Chul Hwan,Kim, Jin Young,Chang, Suyon,Lee, Hye-Jeong,Hur, Jin,Kim, Young Jin,Choi, Byoung Wook The Korean Society of Radiology 2017 KOREAN JOURNAL OF RADIOLOGY Vol.18 No.1

<P>Cardiac magnetic resonance (CMR) imaging is widely used in various medical fields related to cardiovascular diseases. Rapid technological innovations in magnetic resonance imaging in recent times have resulted in the development of new techniques for CMR imaging. T1 and T2 image mapping sequences enable the direct quantification of T1, T2, and extracellular volume fraction (ECV) values of the myocardium, leading to the progressive integration of these sequences into routine CMR settings. Currently, T1, T2, and ECV values are being recognized as not only robust biomarkers for diagnosis of cardiomyopathies, but also predictive factors for treatment monitoring and prognosis. In this study, we have reviewed various T1 and T2 mapping sequence techniques and their clinical applications.</P>