Temporal image compression in cardiac computed tomography: impact of temporal super resolution and noise reduction for assessing left ventricular function
Abstract
Computed tomography (CT) is valuable for assessing left ventricular (LV) function. However, it leads to increased data storage demands and energy consumption. Temporal super resolution (TSR) has the potential to reduce temporal data size while preserving accuracy. This study aimed to determine the feasibility of using TSR for temporal image compression in LV functional analysis. The study included 20 patients who underwent retrospective electrocardiogram (ECG)-gated cardiac CT, from which 20 cardiac phases per patient were acquired. TSR was applied to temporally compressed image data sets, with and without noise reduction (NR), using two NR levels: weak (30%) and strong (70%). Five data sets-including the original uncompressed data and four compressed versions-were analyzed for LV function using fully automated software. Bland-Altman plots and Pearson correlation coefficients were used to assess measurement agreement and reliability. The correlations between the uncompressed and compressed data sets for LV end-systolic volumes (ESVs), end-diastolic volumes (EDVs), and ejection fractions (EFs) were strong (all r = 1.00, 95% CI = 1.00-1.00, all Ps < 0.0001). Bland-Altman analysis showed reduced bias in LV measurements when TSR was applied without NR, while bias increased when NR was applied at both levels. The limits of agreement (LOA) were narrower for EDV but remained wider for ESV and EF. TSR without NR reduced bias but failed to narrow LOA, with EF improving or unchanged…
How strong is the evidence?
Evidence Hub reads this as a cohort study, done in people. Its evidence level is 3 of 5 (Moderate · trial / cohort / secondary analysis). Trials without a large randomized design, cohorts and secondary analyses give moderate evidence. Reported size: 20 participants or records.
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