Background: Infiltrating adipose tissue (inFAT) is a newly recognized proarrhythmic substrate for postinfarct ventricular tachycardias (VT) identifiable on contrast-enhanced computed tomography. This study presents novel digital-heart technology that incorporates inFAT from contrast-enhanced computed tomography to noninvasively predict VT ablation targets and assesses the capability of the technology by comparing its predictions with VT ablation procedure data from patients with ischemic cardiomyopathy.
Methods: Digital-heart models reflecting patient-specific inFAT distributions were reconstructed from contrast-enhanced computed tomography. The digital-heart identification of fat-based ablation targeting (DIFAT) technology evaluated the rapid-pacing-induced VTs in each personalized inFAT-based substrate. DIFAT targets that render the inFAT substrate noninducible to VT, including VTs that arise postablation, were determined. DIFAT predictions were compared with corresponding clinical ablations to assess the capabilities of the technology.
Results: DIFAT was developed and applied retrospectively to 29 ischemic cardiomyopathy patients with contrast-enhanced computed tomography. DIFAT ablation volumes were significantly less than the estimated clinical ablation volumes (1.87±0.35 versus 7.05±0.88 cm, <0.0005). DIFAT targets overlapped with clinical ablations in 79% of patients, mostly in the apex (72%) and inferior/inferolateral (74%). In 3 patients, DIFAT targets colocalized with redo ablations delivered years after the index procedure.
Conclusions: DIFAT is a novel digital-heart technology for individualized VT ablation guidance designed to eliminate VT inducibility following initial ablation. DIFAT predictions colocalized well with clinical ablation locations but provided significantly smaller lesions. DIFAT also predicted VTs targeted in redo procedures years later. As DIFAT uses widely accessible computed tomography, its integration into clinical workflows may augment therapeutic precision and reduce redo procedures.
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http://dx.doi.org/10.1161/CIRCEP.120.008912 | DOI Listing |
Medicina (Kaunas)
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Department of Pneumonology, Medical University of Gdańsk, 80-210 Gdańsk, Poland.
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Department of Emergency, "Santa Maria della Misericordia" University Hospital of Udine, Azienda Sanitaria Universitaria Friuli Centrale, 33100 Udine, Italy.
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View Article and Find Full Text PDFBioengineering (Basel)
December 2024
Department of Radiology, Jena University Hospital, Friedrich Schiller University, 07747 Jena, Germany.
Deep learning image reconstruction (DLIR) has shown potential to enhance computed tomography (CT) image quality, but its impact on tumor visibility and adoption among radiologists with varying experience levels remains unclear. This study compared the performance of two deep learning-based image reconstruction methods, DLIR and Pixelshine, an adaptive statistical iterative reconstruction-volume (ASIR-V) method, and filtered back projection (FBP) across 33 contrast-enhanced CT staging examinations, evaluated by 20-24 radiologists. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were measured for tumor and surrounding organ tissues across DLIR (Low, Medium, High), Pixelshine (Soft, Ultrasoft), ASIR-V (30-100%), and FBP.
View Article and Find Full Text PDFDiagnostics (Basel)
December 2024
Department of Orthopaedic Surgery, University of California Los Angeles, Los Angeles, CA 90095, USA.
Postoperative imaging of musculoskeletal tumors poses a significant diagnostic challenge for radiologists. The complexity arises from the need to differentiate between expected postoperative changes, potential complications, and local recurrence. The choice of imaging modality depends on the type of primary tumor.
View Article and Find Full Text PDFUnlabelled: Accurate estimation of the Lung Shunt Fraction (LSF) is a standard of care in yttrium-90 ( Y) radioembolization treatment planning to prevent excessive lung irradiation due to arterio-venous shunting in the liver. LSF is assessed using Tc macroaggregated albumin ( Tc-MAA) imaging, but this approach adds risk, complexity, and expense to the treatment planning. This study investigates the potential of Contrast-Enhanced Computed Tomography (CECT) as a non-invasive alternative for LSF estimation.
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