Manual segmentation of muscle and adipose compartments from computed tomography (CT) axial images is a potential bottleneck in early rapid detection and quantification of sarcopenia. A prototype deep learning neural network was trained on a multi-center collection of 3413 abdominal cancer surgery subjects to automatically segment truncal muscle, subcutaneous adipose tissue and visceral adipose tissue at the L3 lumbar vertebral level. Segmentations were externally tested on 233 polytrauma subjects. Although after severe trauma abdominal CT scans are quickly and robustly delivered, with often motion or scatter artefacts, incomplete vertebral bodies or arms that influence image quality, the concordance was generally very good for the body composition indices of Skeletal Muscle Radiation Attenuation (SMRA) (Concordance Correlation Coefficient (CCC) = 0.92), Visceral Adipose Tissue index (VATI) (CCC = 0.99) and Subcutaneous Adipose Tissue Index (SATI) (CCC = 0.99). In conclusion, this article showed an automated and accurate segmentation system to segment the cross-sectional muscle and adipose area L3 lumbar spine level on abdominal CT. Future perspectives will include fine-tuning the algorithm and minimizing the outliers.

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Purpose Due to the high morbidity and mortality of infective endocarditis (IE), medical imaging techniques are combined to ensure a correct diagnosis. [18F]FDG PET/CT has demonstrated the ability to improve diagnostic accuracy compared with the conventional modified Duke criteria in patients with suspected IE, especially those with prosthetic valve infective endocarditis (PVIE). The aim of this study is to provide an adjunctive diagnostic tool to improve the diagnostic accuracy in cardiovascular infections, specifically PVIE. Methods A segmentation tool to extract quantitative measures of [18F]FDG PET/CT image studies of prosthetic heart valve regions was developed and validated in 20 cases of suspected PVIE, of which 9 were confirmed. For that, Valvular Heterogeneity Index (VHI) and Ring-to-Center Ratio (RCR) were defined. Results Results show an overall increase in the metabolic uptake of the prosthetic valve ring in the studies with confirmed PVIE diagnosis (SUVmax from 1.70 to 3.20; SUVmean from 0.86 to 1.50). The VHI and RCR showed areas under the curve of 0.727 and 0.808 in the receiver operating characteristics curve analyses, respectively, for PVIE diagnosis. Mann–Whitney U tests showed statistically significant differences between groups for RCR (p = 0.02). Visual analyses and clinical reports were concordant with the extracted quantitative metrics. Conclusion The proposed new method and presented software solution (CASSIA) provide the capability to assess quantitatively myocardial metabolism along the prosthetic valve region in routine [18F]FDG PET/CT scans for evaluating heart valve infectious processes. VHI and RCR are proposed as new potential adjunctive measures for PVIE diagnosis.

Chronic thromboembolic pulmonary hypertension (CTEPH) is confirmed by visual analysis of single-photon emission computer tomography (SPECT) ventilation and perfusion (V/Q) images. Defects in the perfusion image discordant with the ventilation image indicate obstructed segments and the positive diagnosis of CTEPH. A quantitative metric and classification algorithm are proposed based on volumetric data from SPECT V/Q images. The difference in ventilation and perfusion volumes (VV-P) is defined as a quantitative metric to identify discordant defects in the SPECT images. The algorithm was validated with 22 patients grouped according to their diagnosis: (1) CTEPH and (2) respiratory pathology. Volumetric data from SPECT perfusion images was also compared before and after treatment for CTEPH. CTEPH was detected with a sensitivity of 0.67 and specificity of 0.80. The performance of volumetric data from SPECT perfusion images for the evaluation of treatment response was studied for two cases and improvement of pulmonary perfusion was observed in one case. This study uses volumetric data from SPECT V/Q images for the diagnosis of CTEPH and its differentiation from respiratory pathologies. The results indicate that the defined metric is a viable option for a quantitative analysis of SPECT V/Q images.

Neurodegenerative parkinsonisms affect mainly cognitive and motor functions and are syndromes of overlapping symptoms and clinical manifestations such as tremor, rigidness, and bradykinesia. These include idiopathic Parkinson’s disease (PD) and the atypical parkinsonisms, namely progressive supranuclear palsy (PSP), corticobasal degeneration (CBD), multiple system atrophy (MSA) and dementia with Lewy body (DLB). Differences in the striatal metabolism among these syndromes are evaluated using [18F]FDG PET, caused by alterations to the dopaminergic activity and neuronal loss. A study cohort of three patients with PD, 29 with atypical parkinsonism (10 PSP, 6 CBD, 2 MSA, 7 DLB, and 4 non-classifiable), and a control group of 25 patients with normal striatal metabolism is available. Standardized uptake value ratios (SUVR) are extracted from the striatum, and the caudate and the putamen separately. SUVRs are compared among the study groups. In addition, hemispherical and caudate-putamen differences are evaluated in atypical parkinsonisms. Striatal hypermetabolism is detected in patients with PD, while atypical parkinsonisms show hypometabolism, compared to the control group. Hemispherical differences are observed in CBD, MSA and DLB, with the latter also showing statistically significant caudate–putamen asymmetry (p = 0.018). These results indicate disease-specific metabolic uptake patterns in the striatum that can support the differential diagnosis.