Unlabelled: Gated SPECT recorded with 16 intervals determines left ventricular (LV) ejection fraction more accurately than does gated SPECT recorded with 8 intervals but produces higher image noise. This study aimed to assess the results from sestamibi and (201)Tl 16-interval gated SPECT when both signal-to-noise ratio and spatial resolution were enhanced with an original method of reconstruction.
Methods: Forty patients with coronary artery disease underwent (201)Tl and sestamibi 16-interval gated SPECT and, to be used as a reference, cardiac MRI. Assessments of global and regional LV function provided by ordered-subsets expectation maximization (OSEM) with depth-dependant resolution recovery and temporal Fourier filtering were compared with those from conventional filtered backprojection (FBP) previously optimized by screening various filter frequencies and various temporal smoothing levels.
Results: For both tracers, LV ejection fraction was determined best when the association of OSEM with depth-dependant resolution recovery was used alone, with temporal Fourier filtering, or with a slight 2-frame temporal smoothing: Mean absolute values of relative errors ranged from 3.2% to 3.6% (4.0%-7.9% for FBP), and coefficient correlation ranged from 0.91 to 0.93 (0.70-0.91 for FBP). Among these 3 reconstruction methods, the association of OSEM with depth-dependant resolution recovery with temporal Fourier filtering provided the highest signal-to-noise ratio, with mean increases of 54% for sestamibi and 80% for (201)Tl when compared with FBP, and the best analysis of segmental contractility, with exact agreement rates with MRI being 73% for (201)Tl and 79% for sestamibi.
Conclusion: OSEM associated with temporal Fourier filtering and depth-dependant resolution recovery filtering enhances the LV function assessment provided by sestamibi and (201)Tl 16-interval gated SPECT and dramatically reduces image noise, a property that enhances and facilitates image interpretation.
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Mol Pharm
January 2025
Department of Geriatrics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.
Acute myocardial infarction (MI) remains a leading cause of mortality worldwide, with inflammatory and reparative phases playing critical roles in disease progression. Currently, there is a pressing need for imaging techniques to monitor immune cell infiltration and inflammation activity during these phases. We developed a novel probe, Tc-HYNIC-mAb, utilizing a monoclonal antibody that targets the voltage-gated potassium channel 1.
View Article and Find Full Text PDFNucl Med Commun
January 2025
Division of Cardiology, Onishi Hospital, Fujioka, Japan.
Objective: Patients with chronic kidney disease (CKD) have an increased risk of adverse cardio-cerebrovascular events. The purpose of this study is to evaluate the prognostic predictors over 5 years in patients with CKD including haemodialysis.
Methods: In this multicenter, prospective cohort study performed with the Gunma-CKD SPECT Study protocol, 311 patients with CKD [estimated glomerular filtration rate (eGFR) < 60 min/ml/1.
J Interv Card Electrophysiol
January 2025
Department of Cardiovascular Medicine, National Cerebral and Cardiovascular Center, 6-1 Kishibe-Shimmachi, Suita, Osaka, 564-8565, Japan.
Background: Non-response to cardiac resynchronization therapy (CRT) is an important issue in the treatment of heart failure with reduced ejection fraction (HFrEF) and non-left bundle branch block (LBBB). Electrocardiogram-gated myocardial perfusion single-photon emission computed tomography imaging (G-MPI SPECT) is typically used to assess left ventricular (LV) dyssynchrony. This study aimed to determine whether G-MPI parameters are associated with non-responsiveness to CRT.
View Article and Find Full Text PDFEur Heart J Imaging Methods Pract
July 2024
Department of Nuclear Medicine, CHU de Caen Normandie, Normandie Univ, UNICAEN UR 4650 PSIR, Avenue Cote de Nacre, 14000 Caen, France.
J Educ Health Promot
October 2024
Adani Institute of Infrastructure Engineering, Ahmedabad, Gujarat, India.
Parkinson's disease (PD) is a neurodegenerative brain disorder that causes symptoms such as tremors, sleeplessness, behavioral problems, sensory abnormalities, and impaired mobility, according to the World Health Organization (WHO). Artificial intelligence, machine learning (ML), and deep learning (DL) have been used in recent studies (2015-2023) to improve PD diagnosis by categorizing patients and healthy controls based on similar clinical presentations. This study investigates several datasets, modalities, and data preprocessing techniques from the collected data.
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