Metastases-Like Liver Lesions in Two Different Types of Porphyria - Porphyria Cutanea Tarda (PCT) and Acute Hepatic Porphyria (AHP) - and the Role of CEUS.

Porphyria are a group of metabolic disorders caused by altered activity of enzymes in the heme biosynthesis pathway. Acute hepatic porphyria (AHP) are due to hepatic overproduction of the porphyrin precursors, delta aminolevulinic acid, and porphobilinogen, and the symptoms are probably caused primarily by injury to the nervous system. However, abdominal pain and nausea can be observed. Porphyria cutanea tarda (PCT) is the most common of the porphyria with a prevalence of 5–10 per 100 000 people (D. Montgomery Bissell, K. E. et al. N Engl J Med 2017; 377: 862–872). In PCT, deficient enzymatic activity of uroporphyrinogen decarboxylase (UROD) in the liver leads to cutaneous phototoxicity after sun exposure. The most important susceptibility factors include hepatitis C virus infection, alcohol consumption, smoking, estrogen use, and genetic factors like UROD mutation and HFE mutations (S. Jalil et al. Clin Gastroenterol Hepatol 2010; 8: 297–302). In ultrasonography (US) of PCT patients, the liver can be normal, exhibit fibrotic or cirrhotic changes, or rarely demonstrates hyperechogenic focal alterations (M. Nishiyama et al. Abdom Radiol 2017; 42: 1813–1818). We report two cases of patients with rare findings not previously diagnosed with any porphyria

Radial self-navigated native magnetic resonance angiography in comparison to navigator-gated contrast-enhanced MRA of the entire thoracic aorta in an aortic patient collective.

BACKGROUND The native balanced steady state with free precession (bSSFP) magnetic resonance angiography (MRA) technique has been shown to provide high diagnostic image quality for thoracic aortic disease. This study compares a 3D radial respiratory self-navigated native MRA (native-SN-MRA) based on a bSSFP sequence with conventional Cartesian, 3D, contrast-enhanced MRA (CE-MRA) with navigator-gated respiration control for image quality of the entire thoracic aorta. METHODS Thirty-one aortic native-SN-MRA were compared retrospectively (63.9 ± 10.3 years) to 61 CE-MRA (63.1 ± 11.7 years) serving as a reference standard. Image quality was evaluated at the aortic root/ascending aorta, aortic arch and descending aorta. Scan time was recorded. In 10 patients with both MRA sequences, aortic pathologies were evaluated and normal and pathologic aortic diameters were measured. The influence of artifacts on image quality was analyzed. RESULTS Compared to the overall image quality of CE-MRA, the overall image quality of native-SN-MRA was superior for all segments analyzed (aortic root/ascending, p < 0.001; arch, p < 0.001, and descending, p = 0.005). Regarding artifacts, the image quality of native-SN-MRA remained superior at the aortic root/ascending aorta and aortic arch before and after correction for confounders of surgical material (i.e., susceptibility-related artifacts) (p = 0.008 both) suggesting a benefit in terms of motion artifacts. Native-SN-MRA showed a trend towards superior intraindividual image quality, but without statistical significance. Intraindividually, the sensitivity and specificity for the detection of aortic disease were 100% for native-SN-MRA. Aortic diameters did not show a significant difference (p = 0.899). The scan time of the native-SN-MRA was significantly reduced, with a mean of 05:56 ± 01:32 min vs. 08:51 ± 02:57 min in the CE-MRA (p < 0.001). CONCLUSIONS Superior image quality of the entire thoracic aorta, also regarding artifacts, can be achieved with native-SN-MRA, especially in motion prone segments, in addition to a shorter acquisition time

Can bioprosthetic valve thrombosis be promoted by aortic root morphology? An in vitro study

OBJECTIVES: Bioprosthetic valve thrombosis has been considered uncommon, but recent studies have shown that it is more frequent than previously thought. Insufficient washout of the aortic sinus is believed to be a risk factor for bioprosthetic valve thrombosis. The objective of this in vitro experiment was to investigate the impact of aortic root morphology on blood flow in the aortic sinus and to relate these results to in vivo data obtained in patients with a transcatheter aortic valve implant. METHODS: Two compliant aortic root phantoms with different morphologies (symmetrical and patient-specific) were fabricated with silicone. A bioprosthetic aortic valve was inserted in both phantoms. Haemodynamic measurements were performed in a pulsatile flow-loop replicating physiological flow and pressure conditions. The flow in the aortic root was visualized by injecting contrast agent (CA). The distribution of the CA was captured by a high-speed camera, and image post-processing was performed to quantify CA distribution in the aortic sinus. The results were compared with angiographic images after a transcatheter aortic valve implant. RESULTS: Blood flow in the aortic root and the washout of the sinus portion are significantly affected by aortic root morphology. CA arrives at the aortic sinus of the 2 phantoms at 0.09 s and 0.16 s after the valve opens in the symmetrical and the patient-specific phantoms, respectively. Delayed CA arrival was also observed in the patients with a transcatheter aortic valve implant. CONCLUSIONS: Aortic root morphology affects the blood flow in the aortic sinus and may be a factor in bioprosthetic valve thrombosis. Therefore, patient-specific aortic root morphology should be considered when selecting and positioning a prosthesis

Prognosis of abdominal aortic aneurysms: A machine learning-enabled approach merging clinical, morphometric, biomechanical and texture information

An effective surveillance strategy for the progression of abdominal aortic aneurysms (AAAs) may be achieved by assessing its expected growth rate in a personalized manner. Given the variety of factors with an impact on AAA growth, an integrative approach to the problem could potentially benefit from incorporating clinical and morphometric data, as well as mechanical stress characterizations. In addition, here we investigated the use of texture information on computed tomography angiography images within the AAA sac. A cohort of n=38 patients underwent a baseline examination, plus a follow-up visit to measure AAA growth rates, in terms of its maximum diameter (Dmax) divided by the elapsed time period. Subsequently, each case was labelled as ‘slow’, ‘medium’ or ‘quick’ growth, compared to the expected rate reported in demographic studies, as a function of gender and baseline Dmax. We computed a total of 102 features (5 clinical, 17 morphometric, 4 biomechanical, and 76 on texture) and used a number of machine learning (ML) algorithms; with the aim of minimizing misclassification costs. The performance of the system was evaluated with a leave-one-out cross-validation scheme. The results achieved by the best performing approach, an ensemble of decision trees (‘LPBoost’) using the entire 102-dimensional feature space, indicated that the combination of different information sources, along with ML algorithms, may have a positive impact on the AAA prognosis assessment