We investigated the impact of a CNN-based deep-learning (DL) image de-blurring algorithm on coronary artery calcium (CAC) detection performance in conventional CT imaging. Our approach comprises first de-noising the image with a state-of-the-art CNN-based image de-noising algorithm. With improved SNR, it is then possible to sharpen the image with a CNN-based image de-blurring algorithm. We train such networks using natural images, i.e., a large set of diverse photographs. The de-noising strength in the final image can be adjusted by blending back the estimated noise from the first step to the desired degree. To assess the impact of the de-blurring algorithm, we scanned an anthropomorphic phantom containing 100 small calcifications on a CT system using a CAC scoring protocol. Data were acquired at clinical and high dose, and subsequently reconstructed with and without the DL de-blurring algorithm, using 25% of the maximum de-noising strength. For each small CAC, detectability was defined as the ability to calculate an Agatston score (at least 3 adjacent voxels exceeding 130 HU). For the high dose scans, CAC detectability increased from 39% for the standard reconstruction to 49% with de-blurring. The same 39% CAC detectability at high dose without de-blurring was obtained with routine dose with de-blurring. In this work, we also show some visual impressions of applying our DL de-blurring method to clinical cardiac data.
Absolute myocardial perfusion imaging (MPI) can be beneficial in the diagnosis and prognosis of patients with coronary artery disease. However, validation and standardization of perfusion estimates across centers is needed to ensure safe and adequate integration into clinical routine. MPI phantoms can contribute to this clinical need as these models can provide ground truth evaluation of absolute MPI in a simplified, though controlled setup. This work presents verification of phantom design choices, including the justification for using sorbents in mimicking contrast kinetics (i.e., tracer uptake and retention). Moreover, we compare preliminary phantom results obtained with SPECT-MPI with a patient example. Finally, we applied a general two-tissue compartment model to describe the obtained phantom time activity curve data. These evaluation steps support shaping of a suitable verification and validation strategy for the multimodal myocardial perfusion phantom design and realization.
Institutional diagnostic workflows regarding coronary artery disease (CAD) may differ greatly. Myocardial perfusion imaging (MPI) is a commonly used diagnostic method in CAD, whereby multiple modalities are deployed to assess relative or absolute myocardial blood flow (MBF) (e.g. with SPECT, PET, MR, CT, or combinations). In line with proper clinical decision-making, it is essential to assess institutional MPI test validity by confronting MBF assessment to a ground truth. Our research focuses on developing such validation instrument/method for MPI by means of simulating controlled myocardial perfusion in a phantom flow setup. A first step was made in the process of method development and validation by specifying basic requirements for the phantom flow setup. First tests in CT-MPI were aimed to gain experience in clinical testing, to verify to which extent the set requirements are met, and to evaluate the steps needed to further improve accuracy and reproducibility of measurements. The myocardium was simulated as a static cylinder and placed in a controllable pulsatile flow circuit whereby using flow sensors as reference. First flow experiments were performed for different stroke volumes (20-35 mL/stroke). After contrast injection, dynamic MPI-CT scans (SOMATOM Force, Siemens) were obtained to investigate the relation between first-pass measured and computed flow. We observed a moderate correlation; hence, the required accuracy and reproducibility levels were not met. However, we have gained new insights in factors regarding the measurement setup and MBF computation process that might affect instrument validation, which we will incorporate in future flow setup design and testing.
The purpose of this study was to determine the relationships between the CT value and temperature for the range of
ablation therapy. Bovine liver was slowly heated and image acquisition was performed with a clinical CT. Real time
temperature was measured and stored using calibrated thermal sensors. Images were analyzed at CT workstation. It was
feasible to validate the spatial and temporal temperature growth during heating by means of declining CT values in the
performed images. The thermal sensitivity for liver tissue was -0.54±0.10 HU/oC. It is concluded that CT can be
calibrated to predict temperature distribution during heating.
Purpose: ECG-gated CTA allows visualization of the aneurysm and stentgraft during the different phases of the cardiac
cycle, although with a lower SNR per cardiac phase than without ECG gating using the same dose. In our institution,
abdominal aortic aneurysm (AAA) is evaluated using non-ECG-gated CTA. Some common CT scanners cannot reconstruct
a non-gated volume from ECG-gated acquired data. In order to obtain the same diagnostic image quality, we propose offline
temporal averaging of the ECG-gated data. This process, though straightforward, is fundamentally different from
taking a non-gated scan, and its result will certainly differ as well. The purpose of this study is to quantitatively investigate
how good off-line averaging approximates a non-gated scan.
Method: Non-gated and ECG-gated CT scans have been performed on a phantom (Catphan 500). Afterwards the phases
of the ECG-gated CTA data were averaged to create a third dataset. The three sets are compared with respect to noise properties
(NPS) and frequency response (MTF). To study motion artifacts identical scans were acquired on a programmable
dynamic phantom.
Results and Conclusions: The experiments show that the spatial frequency content is not affected by the averaging
process. The minor differences observed for the noise properties and motion artifacts are in favor of the averaged data.
Therefore the averaged ECG-gated phases can be used for diagnosis. This enables the use of ECG-gating for research on
stentgrafts in AAA, without impairing clinical patient care.
Objective: To quantify the influence of velocity, calcification density and acquisition time on coronary calcium
determination using multi-detector CT, dual-source CT and EBT.
Materials and Methods: Artificial arteries with four calcifications of increasing density were attached to a robotic arm to
which a linear movement was applied between 0 and 120 mm/s (step 10 mm/s). The phantom was scanned five times on
64-slice MDCT, DSCT and EBT using a standard acquisition protocol and the average Agatston score was determined.
Results: Increasing motion artifacts were observed at increasing velocities on all scanners, with increasing severity from
EBT to DSCT to 64-slice MDCT. The Agatston score showed a linear dependency on velocity from which a correction
factor was derived. This correction factor showed a linear dependency on calcification density (0.92≤R2≤0.95). The slope
and offset of this correction factor also showed a linear dependency on acquisition time (0.84≤R2≤0.86).
Conclusion: The Agatston score is highly dependent on the average density of individual calcifications. The dependency
of the Agatston score on velocity shows a linear behaviour on calcification density. A quantitative method could be
derived which corrects the measured calcium score for the influence of velocity, calcification density and acquisition
time.
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