Native myocardial T1 mapping: influence of spatial resolution on quantitative results and reproducibility.

Background: Myocardial mapping techniques can be used to quantitatively assess alterations in myocardial tissue properties. This study aims to evaluate the influence of spatial resolution on quantitative results and reproducibility of native myocardial T1 mapping in cardiac magnetic resonance imaging (MRI). Methods: In this cross-sectional study with prospective data collection between October 2019 and February 2020, 50 healthy adults underwent two identical cardiac MRI examinations in the radiology department on the same day. T1 mapping was performed using a MOLLI 5(3)3 sequence with higher (1.4 mm × 1.4 mm) and lower (1.9 mm × 1.9 mm) in-plane spatial resolution. Global quantitative results of T1 mapping were compared between high-resolution and low-resolution acquisitions using paired t-test. Intra-class correlation coefficient (ICC) and Bland-Altman statistics (absolute and percentage differences as means ± SD) were used for assessing test-retest reproducibility. Results: There was no significant difference between global quantitative results acquired with high vs. low-resolution T1 mapping. The reproducibility of global T1 values was good for high-resolution (ICC: 0.88) and excellent for low-resolution T1 mapping (ICC: 0.95, P=0.003). In subgroup analyses, inferior test-retest reproducibility was observed for high spatial resolution in women compared to low spatial resolution (ICC: 0.71 vs. 0.91, P=0.001) and heart rates >77 bpm (ICC: 0.53 vs. 0.88, P=0.004). Apical segments had higher T1 values and variability compared to other segments. Regional T1 values for basal (ICC: 0.81 vs. 0.89, P=0.023) and apical slices (ICC: 0.86 vs. 0.92, P=0.024) showed significantly higher reproducibility in low-resolution compared to high-resolution acquisitions but without differences for midventricular slice (ICC: 0.91 vs. 0.92, P=0.402). Conclusions: Based on our data, we recommend a spatial resolution on the order of 1.9 mm × 1.9 mm for native myocardial T1 mapping using a MOLLI 5(3)3 sequence at 1.5 T particularly in individuals with higher heart rates and women.

Improved image quality in CT pulmonary angiography using deep learning-based image reconstruction.

We investigated the effect of deep learning-based image reconstruction (DLIR) compared to iterative reconstruction on image quality in CT pulmonary angiography (CTPA) for suspected pulmonary embolism (PE). For 220 patients with suspected PE, CTPA studies were reconstructed using filtered back projection (FBP), adaptive statistical iterative reconstruction (ASiR-V 30%, 60% and 90%) and DLIR (low, medium and high strength). Contrast-to-noise ratio (CNR) served as the primary parameter of objective image quality. Subgroup analyses were performed for normal weight, overweight and obese individuals. For patients with confirmed PE (n = 40), we further measured PE-specific CNR. Subjective image quality was assessed independently by two experienced radiologists. CNR was lowest for FBP and enhanced with increasing levels of ASiR-V and, even more with increasing strength of DLIR. High strength DLIR resulted in an additional improvement in CNR by 29-67% compared to ASiR-V 90% (p < 0.05). PE-specific CNR increased by 75% compared to ASiR-V 90% (p < 0.05). Subjective image quality was significantly higher for medium and high strength DLIR compared to all other image reconstructions (p < 0.05). In CT pulmonary angiography, DLIR significantly outperforms iterative reconstruction for increasing objective and subjective image quality. This may allow for further reductions in radiation exposure in suspected PE.

Dual-Energy Computed Tomography in Cardiac Imaging.

Dual-energy computed tomography (DECT) acquires images using two energy spectra and offers a variation of reconstruction techniques for improved cardiac imaging. Virtual monoenergetic images decrease artifacts improving coronary plaque and stent visualization. Further, contrast attenuation is increased allowing significant reduction of contrast dose. Virtual non-contrast reconstructions enable coronary artery calcium scoring from contrast-enhanced scans. DECT provides advanced plaque imaging with detailed analysis of plaque components, indicating plaque stability. Extracellular volume assessment using DECT offers noninvasive detection of myocardial fibrosis. This review aims to outline the current cardiac applications of DECT, summarize recent literature, and discuss their findings.

Improved image quality in transcatheter aortic valve implantation planning CT using deep learning-based image reconstruction.

Background: This study aims to evaluate the impact of a novel deep learning-based image reconstruction (DLIR) algorithm on the image quality in computed tomographic angiography (CTA) for pre-interventional planning of transcatheter aortic valve implantation (TAVI). Methods: We analyzed 50 consecutive patients (median age 80 years, 25 men) who underwent TAVI planning CT on a 256-dectector-row CT. Images were reconstructed with adaptive statistical iterative reconstruction V (ASIR-V) and DLIR. Intravascular image noise, edge sharpness, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were quantified for ascending aorta, descending aorta, abdominal aorta and iliac arteries. Two readers (one radiologist and one interventional cardiologist) scored task-specific subjective image quality on a five-point scale. Results: DLIR significantly reduced median image noise by 29-57% at all anatomical locations (all P<0.001). Accordingly, median SNR improved by 44-133% (all P<0.001) and median CNR improved by 44-125% (all P<0.001). DLIR significantly improved subjective image quality for all four pre-specified TAVI-specific tasks (measuring the annulus, assessing valve morphology and calcifications, the coronary ostia, and the suitability of the aorto-iliac access route) for both the radiologist and the interventional cardiologist (P≤0.001). Measurements of the aortic annulus circumference, area and diameter did not differ between ASIR-V and DLIR reconstructions (all P>0.05). Conclusions: DLIR significantly improves objective and subjective image quality in TAVI planning CT compared to a state-of-the-art iterative reconstruction without affecting measurements of the aortic annulus. This may provide an opportunity for further reductions in contrast medium volume in this population.

Global and Regional Test-Retest Reproducibility of Native T1 and T2 Mapping in Cardiac Magnetic Resonance Imaging.

BACKGROUND: Mapping of T1 and T2 relaxation times in cardiac MRI is an invaluable tool for the diagnosis and risk stratification of a wide spectrum of cardiac diseases. PURPOSE: To investigate the global and regional reproducibility of native T1 and T2 mapping and to analyze the influence of demographic factors, physiological parameters, slice position, and myocardial regions on reproducibility. STUDY TYPE: Prospective single-center cohort-study. POPULATION: Fifty healthy volunteers (29 female, 21 male) with a mean age of 39.4 ± 13.7 years. FIELD STRENGTH/SEQUENCE: Each volunteer was investigated twice at 1.5 T using a modified look-locker inversion-recovery (MOLLI) sequence (T1 mapping) and a T2-prepared steady-state free precession (SSFP) sequence (T2 mapping). ASSESSMENT: Global T1 and T2 values were quantified for the entire left ventricle in three short-axis slices. Regional T1 and T2 values were measured for each myocardial segment and for myocardial segments grouped by slice position and anatomical region. STATISTICAL TESTS: Test-retest reproducibility was assessed using intraclass correlation coefficient (ICC) and Bland-Altman statistics. A P value < 0.05 was considered statistically significant. RESULTS: Reproducibility was good for global T1 values (ICC 0.88) and excellent for global T2 values (ICC 0.91). Reproducibility of T1 values was excellent (ICC 0.91) for midventricular slice and good for apical (ICC 0.86) and basal slice (ICC 0.81). Reproducibility of T1 mapping values was highest in the septum (ICC 0.90) compared to the anterior (0.81), lateral (0.86), and inferior (0.86) wall. For T2 mapping, reproducibility was good for all slice positions (ICC 0.86 for midventricular, 0.83 for basal, and 0.80 for apical slice). Reproducibility of T2 mapping was significantly lower for the inferior wall (ICC 0.58) than for septum (0.89), anterior (0.85), and lateral (0.87) wall. DATA CONCLUSION: Native T1 and T2 mapping has good to excellent reproducibility with significant regional differences. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 2.

Fully automated quantification of left ventricular volumes and function in cardiac MRI: clinical evaluation of a deep learning-based algorithm.

To investigate the performance of a deep learning-based algorithm for fully automated quantification of left ventricular (LV) volumes and function in cardiac MRI. We retrospectively analysed MR examinations of 50 patients (74% men, median age 57 years). The most common indications were known or suspected ischemic heart disease, cardiomyopathies or myocarditis. Fully automated analysis of LV volumes and function was performed using a deep learning-based algorithm. The analysis was subsequently corrected by a senior cardiovascular radiologist. Manual volumetric analysis was performed by two radiology trainees. Volumetric results were compared using Bland-Altman statistics and intra-class correlation coefficient. The frequency of clinically relevant differences was analysed using re-classification rates. The fully automated volumetric analysis was completed in a median of 8 s. With expert review and corrections, the analysis required a median of 110 s. Median time required for manual analysis was 3.5 min for a cardiovascular imaging fellow and 9 min for a radiology resident (p < 0.0001 for all comparisons). The correlation between fully automated results and expert-corrected results was very strong with intra-class correlation coefficients of 0.998 for end-diastolic volume, 0.997 for end-systolic volume, 0.899 for stroke volume, 0.972 for ejection fraction and 0.991 for myocardial mass (all p < 0.001). Clinically meaningful differences between fully automated and expert corrected results occurred in 18% of cases, comparable to the rate between the two manual readers (20%). Deep learning-based fully automated analysis of LV volumes and function is feasible, time-efficient and highly accurate. Clinically relevant corrections are required in a minority of cases.

Enhancing the orienting properties of poly(gamma-benzyl-L-glutamate) by means of additives.

Residual dipolar couplings (RDCs) have recently become increasingly important in organic structure determination due to their unique information content. One main limitation for the use of RDCs in organic compounds is the orientation that needs to be induced to be able to measure RDCs. So far, there are very few possibilities to modulate the orientational properties of organic solutes and even less when chiral media are considered. Based on our recent findings that the critical concentration of the liquid-crystalline phase of homopolypeptides depends on their molecular weight, we sought for further ways to modulate the orienting properties. We were especially interested in seeing whether we could not only influence the induced degree of orientation, but whether we could also change the solute's preferred orientation and even enhance enantiodifferentiation. We thus tried different aprotic and protic additives and were successful in all of the above-mentioned aspects by using CCl(4) as the additive. Furthermore, we consider DMSO to be a very useful additive. The LC phase of low MW poly(gamma-benzyl-L-glutamate) (PBLG) is usually unstable when DMSO is added. The high MW PBLG used in this study, however, remained stable up to a DMSO/CDCl(3) ratio of 1:2. By using this combination of solvents, the alignment of the two enantiomers of a compound, which is insoluble in CDCl(3), namely, the HCl salt of a tryptophane ester, was possible leading to high-quality spectra. The two enantiomers of the tryptophane ester showed different couplings, thus indicating that enantiodifferentiation is taking place. Thus we were able to modulate the orienting properties (degree of orientation, preferred orientation and enantiodifferentiation) of PBLG by using additives and to increase the accessible solvent and solute range significantly.