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Purpose: To compare noise, contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR) and image quality using deep-learning image reconstruction (DLIR) vs. adaptive statistical iterative reconstruction (ASIR-V) in 0.625 and 2.5 mm slice thickness gray scale 74 keV virtual monoenergetic (VM) abdominal dual-energy CT (DECT). Methods: This retrospective study was approved by the institutional review board and regional ethics committee. We analysed 30 portal-venous phase abdominal fast kV-switching DECT (80/140kVp) scans. Data were reconstructed to ASIR-V 60% and DLIR-High at 74 keV in 0.625 and 2.5 mm slice thickness. Quantitative HU and noise assessment were measured within liver, aorta, adipose tissue and muscle. Two board-certified radiologists evaluated image noise, sharpness, texture and overall quality based on a five-point Likert scale. Results: DLIR significantly reduced image noise and increased CNR as well as SNR compared to ASIR-V, when slice thickness was maintained (p < 0.001). Slightly higher noise of 5.5–16.2% was measured (p < 0.01) in liver, aorta and muscle tissue at 0.625 mm DLIR compared to 2.5 mm ASIR-V, while noise in adipose tissue was 4.3% lower with 0.625 mm DLIR compared to 2.5 mm ASIR-V (p = 0.08). Qualitative assessments demonstrated significantly improved image quality for DLIR particularly in 0.625 mm images. Conclusions: DLIR significantly reduced image noise, increased CNR and SNR and improved image quality in 0.625 mm slice images, when compared to ASIR-V. DLIR may facilitate thinner image slice reconstructions for routine contrast-enhanced abdominal DECT. Graphical abstract: [Figure not available: see fulltext.].

Tracking plays an important role in super-resolution (SR) ultrasound imaging, as it improves the quality and sharpness of the final SR images. Moreover, tracking enables quantification of clinically important parameters, such as blood flow velocity. However, the tracking performance degrades in the presence of complex particle patterns and localization uncertainty due to noise and motion. This work presents and discusses multiple approaches for tracking evaluation and compares a nearestneighbor (NN) with a Kalman tracker through simulations and an in vivo experiment. It is shown that in the presence of a localization uncertainty with a standard deviation (SD) of λ/5, the bias and SD of the velocity estimates reach -1.04 ± 0.9 mm/s and -0.12 ± 0.72 mm/s in the NN and Kalman tracker, respectively (relative to the peak velocity of 10 mm/s). The precision of individual track positions is estimated for an in vivo experiment as 37.95 ± 21.37 µm and 23.9 ± 11.82 µm for the NN and Kalman trackers, respectively. The results indicate that the Kalman tracker achieves a better velocity estimation and reduces localization uncertainty.

Super resolution imaging (SRI) can attain resolutions in the 10-20 μm range to visualize capillary circulation. It requires completely stationary interrogated tissue during the 3- to 10-minute acquisition, which is not obtainable for live animals apart from in the brain. All other organs move due to respiration, heart pulsation, and muscle activity. SRI can, thus, only be obtained with proper compensation for these motions. A method for full 2D tissue motion estimation is suggested, and it is shown how this motion compensation can restore SRI of small vessels in a rat kidney. A BK 5000 scanner was used with a X18L5 transducer (BK Medical). B-mode and contrast pulse sequence images were acquired with an MI of 0.2 at a 54 Hz frame rate for 10 min. The left kidney of a male Sprague-Dawley rat was scanned during laparotomy. A 1:10 diluted SonoVue contrast agent (Bracco) was injected through a jugular vein catheter at 100 μl/min. Envelope B-mode data were used for 2D tissue speckle tracking with sub-pixel precision in a central region of the kidney for axial and lateral motion estimation relative to a reference frame. The three motion components from the rat's forced ventilation, heart beat, and residual muscular motion were isolated by using the harmonics of the different motion frequencies (breathing 71 bpm, heart 280-350 bpm, residual: other frequencies). The precision of the motion was found by aligning signals across cycles and thereby estimating the mean standard deviation. The motion signal was used for compensating the position of the individual bubble locations back to that of the reference frame to remove motion. Estimated peak motions were: Heart: Axial: 1.2±0:079μm, Lateral: 7±0.99μm, Breathing Ax: 8±0.22μm, Lat: 79±1.64μm, and Residual: Ax: 42 μm, Lat: 110 μm. The estimation is, thus, sufficiently accurate to correct shifts down to the 10 μm capillary level. A motion-corrected image compared to a non-corrected image show small vascular structures in the c

To obtain accurate blood flow velocity estimates it is important to remove the clutter signal originating from tissue. Conventionally, the clutter signal has been separated from the blood signal based on the difference of their spectral frequencies. However, this approach is not enough for obtaining vector flow measurements, since the spectra overlaps at high beam-to-flow angles. In this work a distinct approach is proposed, where the energy of the velocity spectrum is used to differentiate among the two signals. The energy based method is applied by limiting the amplitude of the velocity spectrum function to a predetermined threshold. The effect of the clutter filtering is evaluated on a plane wave (PW) scan sequence in combination with transverse oscillation (TO) and directional beamforming (DB) for velocity estimation. The performance of the filter is assessed by comparison of the velocity estimates of the proposed filter against a conventional moving average clutter filter. The effect of tissue motion is investigated using a Field II simulation of a straight vessel with moving wall, while the direct effect of the filter on the velocity estimates is evaluated on a CFD model of a carotid bifurcation with a fixed vessel wall. The results show that the proposed filter outperformed the moving average during moving vessel wall conditions, where standard deviations from the velocity magnitudes and angles were kept consistently below 6% and 6° compared to 63% and 48° on the moving average filter. The results on the CFD showed that on non-moving conditions the velocity estimates had minor statistical differences with errors on the magnitude of -7.95±10.1% and angles of 0.15±6.65° for the proposed filter compared to -5.83±9.08% and -0.12±4.48°.

We assessed the CT attenuation density of the pulmonary tissue adjacent to the heart in patients with acute non-ST segment elevation myocardial infarction (J.T. Kuhl, T.S. Kristensen, A.F. Thomsen et al., 2016) [1]. This data was related to the level of ground-glass opacification evaluated by a radiologist, and data on the interobserver variability of semi-automated assessment of pulmonary attenuation density was provided.

BACKGROUND: Signs of pulmonary congestion obtained from cardiac computed tomography angiographic (coronary CTA) images have not previously been related to clinical congestion or outcome and the clinical value is, therefore, unknown. Our objective was to test the hypothesis that signs of pulmonary congestion predict clinical heart failure and adverse outcome in patients with myocardial infarction.METHODS: Coronary CTA was performed before invasive treatment in 400 prospectively included patients with non ST segment elevation myocardial infarction in an observational study. Using a previously described chest computed tomography evaluation algorithm, patients were classified as having "no congestion", "mild to moderate congestion" or "severe congestion".RESULTS: Using multivariate analyses, presence of pulmonary congestion on coronary CTA images was associated with age, female gender, left ventricular ejection fraction (LVEF) and left atrial size. The diagnostic accuracy for predicting clinical heart failure, defined as Killip class >1, was: sensitivity: 83%, specificity: 69%, positive predictive value: 25%, and negative predictive value: 97%. The median follow-up time was 50 months and the study end-point of death or hospitalization due to heart failure was reached in 68 (16%) patients. In a Cox proportional hazards model with adjustments for known risk factors and Killip class, the presence of "mild to moderate congestion" and "severe congestion" was independently associated with adverse outcome (Hazard ratio: 2.6 (95% CI:1.3-5.0) and 3.2 (1.3-7.5)).CONCLUSION: Signs of pulmonary congestion on coronary CTA images are closely correlated to cardiac dysfunction, predict clinical heart failure, and provide prognostic value independent of LVEF and Killip class.

The aim of this study was prospectively to monitor the volume flow in patients with arteriovenous fistula (AVF) with the angle independent ultrasound technique Vector Flow Imaging (VFI). Volume flow values were compared with Ultrasound dilution technique (UDT). Hemodialysis patients need a well-functioning vascular access with as few complications as possible and preferred vascular access is an AVF. Dysfunction due to stenosis is a common complication, and regular monitoring of volume flow is recommended to preserve AVF patency. UDT is considered the gold standard for volume flow surveillance, but VFI has proven to be more precise, when performing single repeated instantaneous measurements. Three patients with AVF were monitored with UDT and VFI monthly for five months. A commercial ultrasound scanner with a 9 MHz linear array transducer with integrated VFI was used to obtain data. UDT values were obtained with Transonic HD03 Flow-QC Hemodialysis Monitor. Three independent measurements at each scan session were obtained with UDT and VFI each month. Average deviation of volume flow between UDT and VFI was 25.7 % (Cl: 16.7% to 34.7%) (p= 0.73). The standard deviation for all patients, calculated from the mean variance of each individual scan sessions, was 199.8 ml/min for UDT and 47.6 ml/min for VFI (p = 0.002). VFI volume flow values were not significantly different from the corresponding estimates obtained using UDT, and VFI measurements were more precise than UDT. The study indicates that VFI can be used for surveillance of volume flow.

This work presents the development and first results of in vivo transthoracic cardiac imaging using an implementation of Vector Flow Imaging (VFI) via the Transverse Oscillation (TO) method on a phased-array transducer. Optimal selection of the lateral wavelength of the transversely-oscillating receive field is described, and results from Field II simulations are presented. Measurements are made using the SARUS experimental ultrasound scanner driving an intercostal phased-array probe. The acquisition sequence was composed of interleaved frames of 68-line B-mode and 17-direction, 32-shot vector velocity flow images. A flow pump was programmed for constant flow for in vitro acquisitions at varying depths in a tissue-mimicking fluid. Additionally, mitral, aortic, and tricuspid valves of two healthy volunteers were scanned from intercostal acoustic windows. The acquired RF data were beam-formed via the TO method, and fourth-order estimators were employed for the velocity estimation. The resulting images were compared with those from conventional spectral Doppler and color flow mapping sequences. VFI is shown to be a clinically-feasible tool, which enables new flexibility for choosing acoustic windows, visualizing turbulent flow patterns, and measuring velocities.