Advances in image reconstruction are necessary to decrease radiation exposure from coronary CT angiography (CCTA) further, but iterative reconstruction has been shown to degrade image quality at high ...levels. Deep-learning image reconstruction (DLIR) offers unique opportunities to overcome these limitations. The present study compared the impact of DLIR and adaptive statistical iterative reconstruction-Veo (ASiR-V) on quantitative and qualitative image parameters and the diagnostic accuracy of CCTA using invasive coronary angiography (ICA) as the standard of reference.
This retrospective study includes 43 patients who underwent clinically indicated CCTA and ICA. Datasets were reconstructed with ASiR-V 70% (using standard SD and high-definition HD kernels) and with DLIR at different levels (i.e., medium M and high H). Image noise, image quality, and coronary luminal narrowing were evaluated by three blinded readers. Diagnostic accuracy was compared against ICA.
Noise did not significantly differ between ASiR-V SD and DLIR-M (37 vs. 37 HU, p = 1.000), but was significantly lower in DLIR-H (30 HU, p < 0.001) and higher in ASiR-V HD (53 HU, p < 0.001). Image quality was higher for DLIR-M and DLIR-H (3.4–3.8 and 4.2–4.6) compared to ASiR-V SD and HD (2.1–2.7 and 1.8–2.2; p < 0.001), with DLIR-H yielding the highest image quality. Consistently across readers, no significant differences in sensitivity (88% vs. 92%; p = 0.453), specificity (73% vs. 73%; p = 0.583) and diagnostic accuracy (80% vs. 82%; p = 0.366) were found between ASiR-V HD and DLIR-H.
DLIR significantly reduces noise in CCTA compared to ASiR-V, while yielding superior image quality at equal diagnostic accuracy.
The present study evaluated the impact of deep-learning image reconstruction (DLIR) on noise, image quality, and diagnostic accuracy. In 43 patients who underwent clinically indicated coronary CT angiography and invasive coronary angiography, image quality was improved by up to 62% at similar noise levels. In addition, DLIR-H yielded the highest noise reduction (up to 43%) and best image quality (improvement of up to 138%). More importantly, sensitivity (92% vs. 88%), specificity (73% vs. 73%) and diagnostic accuracy (82% vs. 80%) of DLIR were at least non-inferior to ASiR-V.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Context.
Water is a key molecule in the physics and chemistry of star and planet formation, but it is difficult to observe from Earth. The
Herschel
Space Observatory provided unprecedented ...sensitivity as well as spatial and spectral resolution to study water. The Water In Star-forming regions with
Herschel
(WISH) key program was designed to observe water in a wide range of environments and provide a legacy data set to address its physics and chemistry.
Aims.
The aim of WISH is to determine which physical components are traced by the gas-phase water lines observed with
Herschel
and to quantify the excitation conditions and water abundances in each of these components. This then provides insight into how and where the bulk of the water is formed in space and how it is transported from clouds to disks, and ultimately comets and planets.
Methods.
Data and results from WISH are summarized together with those from related open time programs. WISH targeted ~80 sources along the two axes of luminosity and evolutionary stage: from low- to high-mass protostars (luminosities from <1 to > 10
5
L
⊙
) and from pre-stellar cores to protoplanetary disks. Lines of H
2
O and its isotopologs, HDO, OH, CO, and O I, were observed with the HIFI and PACS instruments, complemented by other chemically-related molecules that are probes of ultraviolet, X-ray, or grain chemistry. The analysis consists of coupling the physical structure of the sources with simple chemical networks and using non-LTE radiative transfer calculations to directly compare models and observations.
Results.
Most of the far-infrared water emission observed with
Herschel
in star-forming regions originates from warm outflowing and shocked gas at a high density and temperature (> 10
5
cm
−3
, 300–1000 K,
v
~ 25 km s
−1
), heated by kinetic energy dissipation. This gas is not probed by single-dish low-
J
CO lines, but only by CO lines with
J
up
> 14. The emission is compact, with at least two different types of velocity components seen. Water is a significant, but not dominant, coolant of warm gas in the earliest protostellar stages. The warm gas water abundance is universally low: orders of magnitude below the H
2
O/H
2
abundance of 4 × 10
−4
expected if all volatile oxygen is locked in water. In cold pre-stellar cores and outer protostellar envelopes, the water abundance structure is uniquely probed on scales much smaller than the beam through velocity-resolved line profiles. The inferred gaseous water abundance decreases with depth into the cloud with an enhanced layer at the edge due to photodesorption of water ice. All of these conclusions hold irrespective of protostellar luminosity. For low-mass protostars, a constant gaseous HDO/H
2
O ratio of ~0.025 with position into the cold envelope is found. This value is representative of the outermost photodesorbed ice layers and cold gas-phase chemistry, and much higher than that of bulk ice. In contrast, the gas-phase NH
3
abundance stays constant as a function of position in low-mass pre- and protostellar cores. Water abundances in the inner hot cores are high, but with variations from 5 × 10
−6
to a few × 10
−4
for low- and high-mass sources. Water vapor emission from both young and mature disks is weak.
Conclusions.
The main chemical pathways of water at each of the star-formation stages have been identified and quantified. Low warm water abundances can be explained with shock models that include UV radiation to dissociate water and modify the shock structure. UV fields up to 10
2
−10
3
times the general interstellar radiation field are inferred in the outflow cavity walls on scales of the
Herschel
beam from various hydrides. Both high temperature chemistry and ice sputtering contribute to the gaseous water abundance at low velocities, with only gas-phase (re-)formation producing water at high velocities. Combined analyses of water gas and ice show that up to 50% of the oxygen budget may be missing. In cold clouds, an elegant solution is that this apparently missing oxygen is locked up in larger
μ
m-sized grains that do not contribute to infrared ice absorption. The fact that even warm outflows and hot cores do not show H
2
O at full oxygen abundance points to an unidentified refractory component, which is also found in diffuse clouds. The weak water vapor emission from disks indicates that water ice is locked up in larger pebbles early on in the embedded Class I stage and that these pebbles have settled and drifted inward by the Class II stage. Water is transported from clouds to disks mostly as ice, with no evidence for strong accretion shocks. Even at abundances that are somewhat lower than expected, many oceans of water are likely present in planet-forming regions. Based on the lessons for galactic protostars, the low-
J
H
2
O line emission (
E
up
< 300 K) observed in extragalactic sources is inferred to be predominantly collisionally excited and to originate mostly from compact regions of current star formation activity. Recommendations for future mid- to far-infrared missions are made.
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Echocardiography is used for assessment of patients after transcatheter aortic valve implantation (TAVI). Global work index (GWI) integrates LV deformation throughout the cardiac cycle and LV ...afterload and may be advantageous for long-term follow-up.
We analysed 144 patients with severe aortic stenosis who underwent TAVI and echocardiography within two weeks afterwards. GE EchoPAC v2.6 was applied for determining LV ejection fraction, global longitudinal strain (GLS), stroke work (SW), cardiac power output (CPO), and GWI. The endpoint was cardiovascular mortality.
During median follow-up of 625 IQR: 511–770 days, 20 (14%) patients died. Clinical baseline characteristics were comparable between non-survivors and survivors. GWI (p = 0.003) and LVEF (p = 0.039) were lower in non-survivors, while GLS, SW, and CPO were not different. In Kaplan-Meier analysis patients with GWI ≤1234 mmHg% exhibited a lower survival probability (P = 0.006). In univariable Cox regression, a significant mortality association was identified for GWI (P = 0.004), weaker for LVEF (P = 0.014), but not for the other parameters. In multivariable Cox regression, GWI independently improved an LV systolic function model including LVEF and GLS. Similarly, GWI but not LVEF independently improved outcome association of different clinical models.
GWI was lower in non-survivors than survivors, differentiated non-survivors from survivors, was associated with mortality independent of clinical or LV parameters, and improved the fitness of clinical or LV prediction models. In contrast, GLS, SW, and CPO did not show any of these properties. GWI provides added value for follow-up after TAVI possibly by integrating LV deformation throughout the cardiac cycle.
•GWI is an independent predictor of long-term survival in patients with severe aortic stenosis undergoing TAVI.•This effect may be related to integration of LV deformation throughout the cardiac cycle.•GWI provides added value for follow-up after TAVI, while an additional effect by inclusion of LV afterload cannot be excluded.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Precise definition of the mitral valve plane (VP) during segmentation of the left ventricle for SPECT myocardial perfusion imaging (MPI) quantification often requires manual adjustment, which affects ...the quantification of perfusion. We developed a machine learning approach using support vector machines (SVM) for automatic VP placement.
A total of 392 consecutive patients undergoing
Tc-tetrofosmin stress (5 min; mean ± SD, 350 ± 54 MBq) and rest (5 min; 1,024 ± 153 MBq) fast SPECT MPI attenuation corrected (AC) by CT and same-day coronary CT angiography were studied; included in the 392 patients were 48 patients who underwent invasive coronary angiography and had no known coronary artery disease. The left ventricle was segmented with standard clinical software (quantitative perfusion SPECT) by 2 experts, adjusting the VP if needed. Two-class SVM models were computed from the expert placements with 10-fold cross validation to separate the patients used for training and those used for validation. SVM probability estimates were used to compute the best VP position. Automatic VP localizations on AC and non-AC images were compared with expert placement on coronary CT angiography. Stress and rest total perfusion deficits and detection of per-vessel obstructive stenosis by invasive coronary angiography were also compared.
Bland-Altman 95% confidence intervals (CIs) for VP localization by SVM and experts for AC stress images (bias, 1; 95% CI, -5 to 7 mm) and AC rest images (bias, 1; 95% CI, -7 to 10 mm) were narrower than interexpert 95% CIs for AC stress images (bias, 0; 95% CI, -8 to 8 mm) and AC rest images (bias, 0; 95% CI, -10 to 10 mm) (
< 0.01). Bland-Altman 95% CIs for VP localization by SVM and experts for non-AC stress images (bias, 1; 95% CI, -4 to 6 mm) and non-AC rest images (bias, 2; 95% CI, -7 to 10 mm) were similar to interexpert 95% CIs for non-AC stress images (bias, 0; 95% CI, -6 to 5 mm) and non-AC rest images (bias, -1; 95% CI, -9 to 7 mm) (
was not significant NS). For regional detection of obstructive stenosis, ischemic total perfusion deficit areas under the receiver operating characteristic curve for the 2 experts (AUC, 0.79 95% CI, 0.7-0.87; AUC, 0.81 95% CI, 0.73-0.89) and the SVM (0.82 0.74-0.9) for AC data were the same (
= NS) and were higher than those for the unadjusted VP (0.63 0.53-0.73) (
< 0.01). Similarly, for non-AC data, areas under the receiver operating characteristic curve for the experts (AUC, 0.77 95% CI, 0.69-0.89; AUC, 0.8 95% CI, 0.72-0.88) and the SVM (0.79 0.71-0.87) were the same (
= NS) and were higher than those for the unadjusted VP (0.65 0.56-0.75) (
< 0.01).
Machine learning with SVM allows automatic and accurate VP localization, decreasing user dependence in SPECT MPI quantification.
Despite substantial medical advances over the past decades, sudden cardiac death (SCD) remains a leading cause of cardiovascular deaths in patients with ischemic heart disease. The presence of ...structural heart disease with left ventricular ejection fraction <35% is the current criteria for implantable cardioverter-defibrillator therapy as a primary prevention to SCD. However, more than 80% of patients who suffer SCD have a left ventricular ejection fraction >35%, whereas few patients who received an implantable cardioverter-defibrillator required appropriate defibrillation. Cardiac magnetic resonance enables the visualization of the arrhythmogenic myocardial substrate including the presence and pattern of scar and fibrosis. The most promising of these features, besides left ventricular function, strain analysis, and morphology, include tissue characterization using late-gadolinium enhancement, T1 mapping, and extracellular volume fraction calculation. We review the current evidence of SCD relating to ischemic heart disease, provide insights into imaging of the arrhythmogenic substrate that produces lethal ventricular arrhythmia, and discuss how imaging may guide therapies toward SCD prevention.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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