The effect of deep learning reconstruction on abdominal CT densitometry and image quality: a systematic review and meta-analysis.

OBJECTIVE: To determine the difference in CT values and image quality of abdominal CT images reconstructed by filtered back-projection (FBP), hybrid iterative reconstruction (IR), and deep learning reconstruction (DLR). METHODS: PubMed and Embase were systematically searched for articles regarding CT densitometry in the abdomen and the image reconstruction techniques FBP, hybrid IR, and DLR. Mean differences in CT values between reconstruction techniques were analyzed. A comparison between signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of FBP, hybrid IR, and DLR was made. A comparison of diagnostic confidence between hybrid IR and DLR was made. RESULTS: Sixteen articles were included, six being suitable for meta-analysis. In the liver, the mean difference between hybrid IR and DLR was - 0.633 HU (p = 0.483, SD ± 0.902 HU). In the spleen, the mean difference between hybrid IR and DLR was - 0.099 HU (p = 0.925, SD ± 1.061 HU). In the pancreas, the mean difference between hybrid IR and DLR was - 1.372 HU (p = 0.353, SD ± 1.476 HU). In 14 articles, CNR was described. In all cases, DLR showed a significantly higher CNR. In 9 articles, SNR was described. In all cases but one, DLR showed a significantly higher SNR. In all cases, DLR showed a significantly higher diagnostic confidence. CONCLUSIONS: There were no significant differences in CT values reconstructed by FBP, hybrid IR, and DLR in abdominal organs. This shows that these reconstruction techniques are consistent in reconstructing CT values. DLR images showed a significantly higher SNR and CNR, compared to FBP and hybrid IR. KEY POINTS: CT values of abdominal CT images are similar between deep learning reconstruction (DLR), filtered back-projection (FBP), and hybrid iterative reconstruction (IR). DLR results in improved image quality in terms of SNR and CNR compared to FBP and hybrid IR images. DLR can thus be safely implemented in the clinical setting resulting in improved image quality without affecting CT values.

Endocrine and non-endocrine causes of fatigue in adults with Neurofibromatosis type 1.

Context: Neurofibromatosis type 1 (NF1) is a complex system disorder, caused by alterations in RAS pathways. NF1 adults often suffer from chronic and severe fatigue, for which they are frequently referred to Internal Medicine/Endocrinology. Seeking medical help often leads to (invasive) diagnostic procedures. To prevent the personal and financial burden of this disabling fatigue, it is crucial to know the causes. Objective: To explore somatic causes and provide practical recommendations for the approach to fatigue in adults with NF1. Design: Cross-sectional. All adults with NF1 (N = 133) who visited our Endocrinology department underwent a systematic health screening, including a medical questionnaire, structured interview, complete physical examination, biochemical measurements and additional tests if indicated. Main outcome measure: Prevalence of endocrine and non-endocrine health problems between NF1 adults with and without fatigue. Results: In our cohort, 75% of NF1 adults experienced fatigue. The most frequent endocrine disorders were vitamin D deficiency (28%), obesity (18%) and hypothyroidism (8%). The most frequent non-endocrine internal disorder was high blood pressure (42%). None of the disorders differed significantly between adults with and without fatigue. Conclusions: Endocrine and non-endocrine disorders were equally present in our cohort of NF1 adults with and without fatigue. This suggests that the high prevalence of fatigue in NF1 adults is not explained by these somatic disorders. An alternative explanation for fatigue might be deficits in cognitive functioning and other neuropsychological processes in NF1. Based on our results and review of the literature, we provide a clinical algorithm for the approach to fatigue in NF1 adults, including somatic and psychological assessment.