Our aims were to investigate, first, the relationship between gastric tone (measured with a barostat) and gastric emptying (measured by radioscintigraphy with and without barostat) and, second, to determine the effect of a symptomatic intragastric pressure increment on gastric emptying. In 16 healthy subjects we quantified simultaneously gastric tone, emptying, and perception at two different intragastric pressure levels: 2 mmHg (low pressure) or 8 mmHg above intra-abdominal pressure (high pressure). At the low intragastric pressure level, ingestion of the meal induced an additional expansion in intragastric volume of 285 +/- 50 ml (P < 0.001), which reflected a gastric accommodative relaxation. At the high pressure level, intragastric volume expanded further, but neither low nor high pressure levels had significant effects on solid emptying. Interestingly, low and high pressure levels produced a similar, modest but significant, acceleration of liquid emptying (17 +/- 5 and 17 +/- 4%, respectively). However, although the low pressure was largely unperceived (score 1.0 +/- 0.5; NS), the high pressure level produced significant symptomatic perception (score 2.5 +/- 0.9; P < 0.05 vs. low pressure). We conclude that 1) gastric accommodation to a meal prevents volume-dependent wall tension increments and 2) the stomach adapts to increments in postcibal intragastric pressure by a limited acceleration of liquid emptying, but wall stress triggers a symptomatic alert mechanism.
The POSE procedure was followed by significant sustained weight loss and improved glucose homeostasis and satiation peptide responses. Weight loss following POSE may be mediated through changes in gastrointestinal neuro-endocrine physiology.
Active shape models bear a great promise for model-based medical image analysis. Their practical use, though, is undermined due to the need to train such models on large image databases. Automatic building of point distribution models (PDMs) has been successfully addressed and a number of autolandmarking techniques are currently available. However, the need for strategies to automatically build intensity models around each landmark has been largely overlooked in the literature. This work demonstrates the potential of creating intensity models automatically by simulating image generation. We show that it is possible to reuse a 3D PDM built from computed tomography (CT) to segment gated single photon emission computed tomography (gSPECT) studies. Training is performed on a realistic virtual population where image acquisition and formation have been modeled using the SIMIND Monte Carlo simulator and ASPIRE image reconstruction software, respectively. The dataset comprised 208 digital phantoms (4D-NCAT) and 20 clinical studies. The evaluation is accomplished by comparing point-to-surface and volume errors against a proper gold standard. Results show that gSPECT studies can be successfully segmented by models trained under this scheme with subvoxel accuracy. The accuracy in estimated LV function parameters, such as end diastolic volume, end systolic volume, and ejection fraction, ranged from 90.0% to 94.5% for the virtual population and from 87.0% to 89.5% for the clinical population.
Following the publication of the original article [1], the authors reported that the figures were cited and presented as part of the Background section instead of where they were originally cited in the text.The original article [1] has been updated.
Background
COVID-19 infection, especially in cases with pneumonia, is associated with a high rate of pulmonary embolism (PE). In patients with contraindications for CT pulmonary angiography (CTPA) or non-diagnostic CTPA, perfusion single-photon emission computed tomography/computed tomography (Q-SPECT/CT) is a diagnostic alternative. The goal of this study is to develop a radiomic diagnostic system to detect PE based only on the analysis of Q-SPECT/CT scans.
Methods
This radiomic diagnostic system is based on a local analysis of Q-SPECT/CT volumes that includes both CT and Q-SPECT values for each volume point. We present a combined approach that uses radiomic features extracted from each scan as input into a fully connected classification neural network that optimizes a weighted cross-entropy loss trained to discriminate between three different types of image patterns (pixel sample level): healthy lungs (control group), PE and pneumonia. Four types of models using different configuration of parameters were tested.
Results
The proposed radiomic diagnostic system was trained on 20 patients (4,927 sets of samples of three types of image patterns) and validated in a group of 39 patients (4,410 sets of samples of three types of image patterns). In the training group, COVID-19 infection corresponded to 45% of the cases and 51.28% in the test group. In the test group, the best model for determining different types of image patterns with PE presented a sensitivity, specificity, positive predictive value and negative predictive value of 75.1%, 98.2%, 88.9% and 95.4%, respectively. The best model for detecting pneumonia presented a sensitivity, specificity, positive predictive value and negative predictive value of 94.1%, 93.6%, 85.2% and 97.6%, respectively. The area under the curve (AUC) was 0.92 for PE and 0.91 for pneumonia. When the results obtained at the pixel sample level are aggregated into regions of interest, the sensitivity of the PE increases to 85%, and all metrics improve for pneumonia.
Conclusion
This radiomic diagnostic system was able to identify the different lung imaging patterns and is a first step toward a comprehensive intelligent radiomic system to optimize the diagnosis of PE by Q-SPECT/CT.
Highlights
Artificial intelligence applied to Q-SPECT/CT is a diagnostic option in patients with contraindications to CTPA or a non-diagnostic test in times of COVID-19.
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