Objective: In myocardial perfusion single-photon emission computed tomography (SPECT), abdominal activity often interferes with the evaluation of perfusion in the inferior wall, especially after pharmacological stress. In this randomized study, we examined the effect of carbonated water intake versus still water intake on the quality of images obtained during myocardial perfusion images (MPI) studies. Methods: A total of 467 MIBI studies were randomized into a carbonated water group and a water group. The presence of intestinal activity adjacent to the inferior wall was evaluated by two observers. Furthermore, a semiquantitative analysis was performed in the adenosine subgroup,using a count ratio of the inferior myocardial wall and adjacent abdominal activity. Results: The need for repeated SPECT in the adenosine studies was 5.3 % in the carbonated water group versus 19.4 % in the still water group (p = 0.019). The inferior wall-to-abdomen count ratio was significantly higher in the carbonated water group compared to the still water group (2.11 ± 1.00 vs. 1.72 ± 0.73, p\0.001). The effect of carbonated water during rest and after exercise was not significant. Conclusions: This randomized study showed that carbonated water significantly reduced the interference of extra-cardiac activity in adenosine SPECT MPI. Keywords: Extra-cardiac radioactivity, Myocardial SPECT, Image quality enhancement, Carbonated water
Rationale: Patients with cancer of the upper gastrointestinal tract or lung are more likely to present with malnutrition at diagnosis than, for instance, patients with melanoma. Low muscle mass is an indicator of malnutrition and can be determined by computed tomography (CT) analysis of the skeletal muscle index (SMI) at the 3rd lumbar vertebra (L3) level. However, CT images at L3 are not always available. At each vertebra level, we determined if type of cancer, i.e., head and neck cancer (HNC), oesophageal cancer (OC) or lung cancer (LC) vs. melanoma (ME) was associated with lower SMI. Methods: CT images from adult patients with HNC, OC, LC or ME were included and analyzed. Scans were performed in the patient’s initial staging after diagnosis. MIM software version 7.0.1 was used to contour the muscle areas for all vertebra levels. Skeletal muscle area was corrected for stature to calculate SMI (cm2/m2). We tested for the association of HNC, OC, or LC diagnosis vs ME with SMI by univariate and multivariate linear regression analyses. In the multivariate analyses, age (years), sex, and body mass index (BMI; kg/m2) were included. Betas (B;95%CI) were calculated and statistical significance was set at p
Background & aims: Accurate diagnosis of sarcopenia requires evaluation of muscle quality, which refers to the amount of fat infiltration in muscle tissue. In this study, we aim to investigate whether we can independently predict mortality risk in transcatheter aortic valve implantation (TAVI) patients, using automatic deep learning algorithms to assess muscle quality on procedural computed tomography (CT) scans. Methods: This study included 1199 patients with severe aortic stenosis who underwent transcatheter aortic valve implantation (TAVI) between January 2010 and January 2020. A procedural CT scan was performed as part of the preprocedural-TAVI evaluation, and the scans were analyzed using deep-learning-based software to automatically determine skeletal muscle density (SMD) and intermuscular adipose tissue (IMAT). The association of SMD and IMAT with all-cause mortality was analyzed using a Cox regression model, adjusted for other known mortality predictors, including muscle mass. Results: The mean age of the participants was 80 ± 7 years, 53% were female. The median observation time was 1084 days, and the overall mortality rate was 39%. We found that the lowest tertile of muscle quality, as determined by SMD, was associated with an increased risk of mortality (HR 1.40 [95%CI: 1.15–1.70], p < 0.01). Similarly, low muscle quality as defined by high IMAT in the lowest tertile was also associated with increased mortality risk (HR 1.24 [95%CI: 1.01–1.52], p = 0.04). Conclusions: Our findings suggest that deep learning-assessed low muscle quality, as indicated by fat infiltration in muscle tissue, is a practical, useful and independent predictor of mortality after TAVI.
