Background Photon-counting detector (PCD) CT provides comprehensive spectral data with every acquisition, but studies evaluating myocardial extracellular volume (ECV) quantification with use of PCD CT compared with an MRI reference remain lacking. Purpose To compare ECV quantification for myocardial tissue characterization between a first-generation PCD CT system and cardiac MRI. Materials and Methods In this single-center prospective study, adults without contraindication to iodine-based contrast media underwent same-day cardiac PCD CT and MRI with native and postcontrast T1 mapping and late gadolinium enhancement for various clinical indications for cardiac MRI (the reference standard) between July 2021 and January 2022. Global and midventricular ECV were assessed with use of three methods: single-energy PCD CT, dual-energy PCD CT, and MRI T1 mapping. Quantitative comparisons among all techniques were performed. Correlation and reliability between different methods of ECV quantification were assessed with use of the Pearson correlation coefficient () and the intraclass correlation coefficient. Results The final sample included 29 study participants (mean age ± SD, 54 years ± 17; 15 men). There was a strong correlation of ECV between dual- and single-energy PCD CT ( = 0.91, < .001). Radiation dose was 40% lower with dual-energy versus single-energy PCD CT (volume CT dose index, 10.1 mGy vs 16.8 mGy, respectively; < .001). In comparison with MRI, dual-energy PCD CT showed strong correlation ( = 0.82 and 0.91, both < .001) and good to excellent reliability (intraclass correlation coefficients, 0.81 and 0.90) for midventricular and global ECV quantification, but it overestimated ECV by approximately 2%. Single-energy PCD CT showed similar relationship with MRI but underestimated ECV by 3%. Conclusion Myocardial tissue characterization with photon-counting detector CT-based quantitative extracellular volume analysis showed a strong correlation to MRI. © RSNA, 2023
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http://dx.doi.org/10.1148/radiol.222030 | DOI Listing |
Eur Radiol
December 2024
Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Objectives: To develop and validate deep learning (DL)-models that denoise late iodine enhancement (LIE) images and enable accurate extracellular volume (ECV) quantification.
Methods: This study retrospectively included patients with chest discomfort who underwent CT myocardial perfusion + CT angiography + LIE from two hospitals. Two DL models, residual dense network (RDN) and conditional generative adversarial network (cGAN), were developed and validated.
Eur Radiol
December 2024
Department of Radiology, Qilu Hospital, Shandong University, Jinan, China.
Objectives: To analyze the performance of multiparametric magnetic resonance imaging (MRI) in quantification of pancreatic ductal adenocarcinoma (PDAC) fibrosis grading.
Method: This prospective study enrolled 79 patients with PDAC confirmed by pathology. Multiparametric MRI including native T1 mapping, intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI), diffusion kurtosis imaging diffusion-weighted imaging (DKI-DWI), and enhanced T1 mapping were performed before surgery.
Front Cardiovasc Med
December 2024
Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN, United States.
Purpose: Evaluate the feasibility of quantification of Relaxation Along a Fictitious Field in the 2nd rotating frame (RAFF2) relaxation times in the human myocardium at 3 T.
Methods: mapping was performed using a breath-held ECG-gated acquisition of five images: one without preparation, three preceded by RAFF2 trains of varying duration, and one preceded by a saturation prepulse. Pixel-wise maps were obtained after three-parameter exponential fitting.
Eur J Nucl Med Mol Imaging
November 2024
Department of Nuclear Medicine, The Third Xiangya Hospital, Central South University, Changsha, China.
Purpose: To evaluate right ventricular (RV) uptake measured by quantitative [Tc]Tc-DPD SPECT/CT to investigate its role in predicting and evaluating prognosis and therapeutic outcomes in patients with transthyretin amyloid cardiomyopathy (ATTR-CA).
Methods: Patients with ATTR-CA were consecutively enrolled for quantitative [Tc]Tc-DPD SPECT/CT. Ventricular amyloid burden was quantified by SUV and TBR.
J Cardiovasc Dev Dis
October 2024
Department of Diagnostic Radiology, Yokohama City University Graduate School of Medicine, Yokohama 236-0004, Kanagawa, Japan.
Background: The utility of synthetic ECV, which does not require hematocrit values, has been reported; however, high-quality CT images are essential for accurate quantification. Second-generation Deep Learning Reconstruction (DLR) enables low-noise and high-resolution cardiac CT images. The aim of this study is to compare the differences among four reconstruction methods (hybrid iterative reconstruction (HIR), model-based iterative reconstruction (MBIR), DLR, and second-generation DLR) in the quantification of synthetic ECV.
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