Standardization of a CT Protocol for Imaging Patients with Suspected COVID-19-A RACOON Project

CT protocols that diagnose COVID-19 vary in regard to the associated radiation exposure and the desired image quality (IQ). This study aims to evaluate CT protocols of hospitals participating in the RACOON (Radiological Cooperative Network) project, consolidating CT protocols to provide recommendations and strategies for future pandemics. In this retrospective study, CT acquisitions of COVID-19 patients scanned between March 2020 and October 2020 (RACOON phase 1) were included, and all non-contrast protocols were evaluated. For this purpose, CT protocol parameters, IQ ratings, radiation exposure (CTDIvol), and central patient diameters were sampled. Eventually, the data from 14 sites and 534 CT acquisitions were analyzed. IQ was rated good for 81% of the evaluated examinations. Motion, beam-hardening artefacts, or image noise were reasons for a suboptimal IQ. The tube potential ranged between 80 and 140 kVp, with the majority between 100 and 120 kVp. CTDIvol was 3.7 ± 3.4 mGy. Most healthcare facilities included did not have a specific non-contrast CT protocol. Furthermore, CT protocols for chest imaging varied in their settings and radiation exposure. In future, it will be necessary to make recommendations regarding the required IQ and protocol parameters for the majority of CT scanners to enable comparable IQ as well as radiation exposure for different sites but identical diagnostic questions.

Strong Association of Depression and Anxiety With the Presence of Back Pain While Impact of Spinal Imaging Findings is Limited: Analysis of an MRI Cohort Study

Development of back pain is multifactorial, and it is not well understood which factors are the main drivers of the disease. We therefore applied a machine-learning approach to an existing large cohort study data set and sought to identify and rank the most important contributors to the presence of back pain amongst the documented parameters of the cohort. Data from 399 participants in the KORA-MRI (Cooperative health research in the region Augsburg-magnetic resonance imaging) (Cooperative Health Research in the Region Augsburg) study was analyzed. The data set included MRI images of the whole body, including the spine, metabolic, sociodemographic, anthropometric, and cardiovascular data. The presence of back pain was one of the documented items in this data set. Applying a machine-learning approach to this preexisting data set, we sought to identify the variables that were most strongly associated with back pain. Mediation analysis was performed to evaluate the underlying mechanisms of the identified associations. We found that depression and anxiety were the 2 most selected predictors for back pain in our model. Additionally, body mass index, spinal canal width and disc generation, medium and heavy physical work as well as cardiovascular factors were among the top 10 most selected predictors. Using mediation analysis, we found that the effects of anxiety and depression on the presence of back pain were mainly direct effects that were not mediated by spinal imaging. In summary, we found that psychological factors were the most important predictors of back pain in our cohort. This supports the notion that back pain should be treated in a personalized multidimensional framework. PERSPECTIVE: This article presents a wholistic approach to the problem of back pain. We found that depression and anxiety were the top predictors of back pain in our cohort. This strengthens the case for a multidimensional treatment approach to back pain, possibly with a special emphasis on psychological factors.

Comparison of the performance of conventional and spectral-based tagged stool cleansing algorithms at CT colonography

OBJECTIVES

To compare the performance of conventional versus spectral-based electronic stool cleansing for iodine-tagged CT colonography (CTC) using a dual-layer spectral detector scanner.

METHODS

We retrospectively evaluated iodine contrast stool-tagged CTC scans of 30 consecutive patients (mean age: 69 ± 8 years) undergoing colorectal cancer screening obtained on a dual-layer spectral detector CT scanner. One reader identified locations of electronic cleansing artifacts (n = 229) on conventional and spectral cleansed images. Three additional independent readers evaluated these locations using a conventional cleansing algorithm (Intellispace Portal) and two experimental spectral cleansing algorithms (i.e., fully transparent and translucent tagged stool). For each cleansed image set, readers recorded the severity of over- and under-cleansing artifacts on a 5-point Likert scale (0 = none to 4 = severe) and readability compared to uncleansed images. Wilcoxon's signed-rank tests were used to assess artifact severity, type, and readability (worse, unchanged, or better).

RESULTS

Compared with conventional cleansing (66% score ≥ 2), the severity of overall cleansing artifacts was lower in transparent (60% score ≥ 2, p = 0.011) and translucent (50% score ≥ 2, p < 0.001) spectral cleansing. Under-cleansing artifact severity was lower in transparent (49% score ≥ 2, p < 0.001) and translucent (39% score ≥ 2, p < 0.001) spectral cleansing compared with conventional cleansing (60% score ≥ 2). Over-cleansing artifact severity was worse in transparent (17% score ≥ 2, p < 0.001) and translucent (14% score ≥ 2, p = 0.023) spectral cleansing compared with conventional cleansing (9% score ≥ 2). Overall readability was significantly improved in transparent (p < 0.001) and translucent (p < 0.001) spectral cleansing compared with conventional cleansing.

CONCLUSIONS

Spectral cleansing provided more robust electronic stool cleansing of iodine-tagged stool at CTC than conventional cleansing.

KEY POINTS

• Spectral-based electronic cleansing of tagged stool at CT colonography provides higher quality images with less perception of artifacts than does conventional cleansing. • Spectral-based electronic cleansing could potentially advance minimally cathartic approach for CT colonography. Further clinical trials are warranted.

Deep learning in CT colonography: differentiating premalignant from benign colorectal polyps

Objectives

To investigate the differentiation of premalignant from benign colorectal polyps detected by CT colonography using deep learning.

Methods

In this retrospective analysis of an average risk colorectal cancer screening sample, polyps of all size categories and morphologies were manually segmented on supine and prone CT colonography images and classified as premalignant (adenoma) or benign (hyperplastic polyp or regular mucosa) according to histopathology. Two deep learning models SEG and noSEG were trained on 3D CT colonography image subvolumes to predict polyp class, and model SEG was additionally trained with polyp segmentation masks. Diagnostic performance was validated in an independent external multicentre test sample. Predictions were analysed with the visualisation technique Grad-CAM++.

Results

The training set consisted of 107 colorectal polyps in 63 patients (mean age: 63 ± 8 years, 40 men) comprising 169 polyp segmentations. The external test set included 77 polyps in 59 patients comprising 118 polyp segmentations. Model SEG achieved a ROC-AUC of 0.83 and 80% sensitivity at 69% specificity for differentiating premalignant from benign polyps. Model noSEG yielded a ROC-AUC of 0.75, 80% sensitivity at 44% specificity, and an average Grad-CAM++ heatmap score of ≥ 0.25 in 90% of polyp tissue.

Conclusions

In this proof-of-concept study, deep learning enabled the differentiation of premalignant from benign colorectal polyps detected with CT colonography and the visualisation of image regions important for predictions. The approach did not require polyp segmentation and thus has the potential to facilitate the identification of high-risk polyps as an automated second reader.

Key Points

• Non-invasive deep learning image analysis may differentiate premalignant from benign colorectal polyps found in CT colonography scans.

• Deep learning autonomously learned to focus on polyp tissue for predictions without the need for prior polyp segmentation by experts.

• Deep learning potentially improves the diagnostic accuracy of CT colonography in colorectal cancer screening by allowing for a more precise selection of patients who would benefit from endoscopic polypectomy, especially for patients with polyps of 6–9 mm size.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00330-021-08532-2.

White matter hyperintensity volume in pre-diabetes, diabetes and normoglycemia

INTRODUCTION

As white matter hyperintensities (WMHs) of the brain are associated with an increased risk of stroke, cognitive decline, and depression, elucidating the associated risk factors is important. In addition to age and hypertension, pre-diabetes and diabetes may play important roles in the development of WMHs. Previous studies have, however, shown conflicting results. We aimed to investigate the effect of diabetes status and quantitative markers of glucose metabolism on WMH volume in a population-based cohort without prior cardiovascular disease.

RESEARCH DESIGN AND METHODS

400 participants underwent 3 T MRI. WMHs were manually segmented on 3D fluid-attenuated inversion recovery images. An oral glucose tolerance test (OGTT) was administered to all participants not previously diagnosed with diabetes to assess 2-hour serum glucose concentrations. Fasting glucose concentrations and glycated hemoglobin (HbA1c) levels were measured. Zero-inflated negative binomial regression analyses of WMH volume and measures of glycemic status were performed while controlling for cardiovascular risk factors and multiple testing.

RESULTS

The final study population comprised 388 participants (57% male; age 56.3±9.2 years; n=98 with pre-diabetes, n=51 with diabetes). Higher WMH volume was associated with pre-diabetes (p=0.001) and diabetes (p=0.026) compared with normoglycemic control participants after adjustment for cardiovascular risk factors. 2-hour serum glucose (p<0.001), but not fasting glucose (p=0.389) or HbA1c (p=0.050), showed a significant positive association with WMH volume after adjustment for cardiovascular risk factors.

CONCLUSION

Our results indicate that high 2-hour serum glucose concentration in OGTT, but not fasting glucose levels, may be an independent risk factor for the development of WMHs, with the potential to inform intensified prevention strategies in individuals at risk of WMH-associated morbidity.

Computed Tomography Perfusion Deficit Volumes Predict Functional Outcome in Patients With Basilar Artery Occlusion

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Significant Impact of Coffee Consumption on MR-Based Measures of Cardiac Function in a Population-Based Cohort Study without Manifest Cardiovascular Disease

Subclinical effects of coffee consumption (CC) with regard to metabolic, cardiac, and neurological complications were evaluated using a whole-body magnetic resonance imaging (MRI) protocol. A blended approach was used to estimate habitual CC in a population-based study cohort without a history of cardiovascular disease. Associations of CC with MRI markers of gray matter volume, white matter hyperintensities, cerebral microhemorrhages, total and visceral adipose tissue (VAT), hepatic proton density fat fraction, early/late diastolic filling rate, end-diastolic/-systolic and stroke volume, ejection fraction, peak ejection rate, and myocardial mass were evaluated by linear regression. In our analysis with 132 women and 168 men, CC was positively associated with MR-based cardiac function parameters including late diastolic filling rate, stroke volume (p < 0.01 each), and ejection fraction (p < 0.05) when adjusting for age, sex, smoking, hypertension, diabetes, Low-density lipoprotein (LDL), triglycerides, cholesterol, and alcohol consumption. CC was inversely associated with VAT independent of demographic variables and cardiovascular risk factors (p < 0.05), but this association did not remain significant after additional adjustment for alcohol consumption. CC was not significantly associated with potential neurodegeneration. We found a significant positive and independent association between CC and MRI-based systolic and diastolic cardiac function. CC was also inversely associated with VAT but not independent of alcohol consumption.

Diagnostic value of contrast-enhanced ultrasound versus computed tomography for hepatocellular carcinoma: a retrospective, single-center evaluation of 234 patients

OBJECTIVE

Hepatocellular carcinoma (HCC) is the most common cause of primary liver cancer. A major part of diagnostic HCC work-up is based on imaging findings from sonography, computed tomography (CT), or magnetic resonance imaging (MRI) scans. Contrast-enhanced ultrasound (CEUS) allows for the dynamic assessment of the microperfusion pattern of suspicious liver lesions. This study aimed to evaluate the diagnostic value of CEUS compared with CT scans for assessing HCC.

METHODS

We performed a retrospective, single-center study between 2004 and 2018 on 234 patients with suspicious liver lesions who underwent CEUS and CT examinations. All patients underwent native B-mode, color Doppler and CEUS after providing informed consent. Every CEUS examination was performed and interpreted by a single experienced radiologist (European Federation of Societies for Ultrasound in Medicine and Biology level 3).

RESULTS

CEUS was performed on all included patients without occurrence of any adverse effects. CEUS showed a sensitivity of 94%, a specificity of 70%, a positive predictive value of 93% and a negative predictive value of 72% for analyzing HCC compared with CT as the diagnostic gold standard.

CONCLUSIONS

CEUS has an excellent safety profile and shows a high diagnostic accuracy in assessing HCC compared with corresponding results from CT scans.

Decomposing Acute Symptom Severity in Large Vessel Occlusion Stroke: Association With Multiparametric CT Imaging and Clinical Parameters

Background and Purpose: Acute ischemic stroke of the anterior circulation due to large vessel occlusion (LVO) is a multifactorial process, which causes neurologic symptoms of different degree. Our aim was to examine the impact of neuromorphologic and vascular correlates as well as clinical factors on acute symptom severity in LVO stroke. Methods: We selected LVO stroke patients with known onset time from a consecutive cohort which underwent multiparametric CT including non-contrast CT, CT angiography and CT perfusion (CTP) before thrombectomy. Software-based quantification was used to calculate CTP total ischemic and ischemic core volume. Symptom severity was assessed using the National Institutes of Health Stroke Scale (NIHSS) upon admission. Multivariable regression analysis was performed to determine independent associations of admission NIHSS with imaging and clinical parameters. Receiver operating characteristics (ROC) analyses were used to examine performance of imaging parameters to classify symptom severity. Results: We included 142 patients. Linear and ordinal regression analyses for NIHSS and NIHSS severity groups identified significant associations for total ischemic volume [β = 0.31, p = 0.01; Odds ratio (OR) = 1.11, 95%-confidence-interval (CI): 1.02-1.19], clot burden score (β = -0.28, p = 0.01; OR = 0.76, 95%-CI: 0.64-0.90) and age (β = 0.17, p = 0.04). No association was found for ischemic core volume, stroke side, collaterals and time from onset. Stroke topography according to the Alberta Stroke Program CT Score template did not display significant influence after correction for multiple comparisons. AUC for classification of the NIHSS threshold ≥6 by total ischemic volume was 0.81 (p < 0.001). Conclusions: We determined total ischemic volume, clot burden and age as relevant drivers for baseline NIHSS in acute LVO stroke. This suggests that not only mere volume but also degree of occlusion influences symptom severity. Use of imaging parameters as surrogate for baseline NIHSS reached limited performance underlining the need for combined clinical and imaging assessment in acute stroke management.

Machine Learning-based Differentiation of Benign and Premalignant Colorectal Polyps Detected with CT Colonography in an Asymptomatic Screening Population: A Proof-of-Concept Study

Background CT colonography does not enable definite differentiation between benign and premalignant colorectal polyps. Purpose To perform machine learning-based differentiation of benign and premalignant colorectal polyps detected with CT colonography in an average-risk asymptomatic colorectal cancer screening sample with external validation using radiomics. Materials and Methods In this secondary analysis of a prospective trial, colorectal polyps of all size categories and morphologies were manually segmented on CT colonographic images and were classified as benign (hyperplastic polyp or regular mucosa) or premalignant (adenoma) according to the histopathologic reference standard. Quantitative image features characterizing shape (n = 14), gray level histogram statistics (n = 18), and image texture (n = 68) were extracted from segmentations after applying 22 image filters, resulting in 1906 feature-filter combinations. Based on these features, a random forest classification algorithm was trained to predict the individual polyp character. Diagnostic performance was validated in an external test set. Results The random forest model was fitted using a training set consisting of 107 colorectal polyps in 63 patients (mean age, 63 years ± 8 [standard deviation]; 40 men) comprising 169 segmentations on CT colonographic images. The external test set included 77 polyps in 59 patients comprising 118 segmentations. Random forest analysis yielded an area under the receiver operating characteristic curve of 0.91 (95% CI: 0.85, 0.96), a sensitivity of 82% (65 of 79) (95% CI: 74%, 91%), and a specificity of 85% (33 of 39) (95% CI: 72%, 95%) in the external test set. In two subgroup analyses of the external test set, the area under the receiver operating characteristic curve was 0.87 in the size category of 6-9 mm and 0.90 in the size category of 10 mm or larger. The most important image feature for decision making (relative importance of 3.7%) was quantifying first-order gray level histogram statistics. Conclusion In this proof-of-concept study, machine learning-based image analysis enabled noninvasive differentiation of benign and premalignant colorectal polyps with CT colonography. © RSNA, 2021 Online supplemental material is available for this article.

Liver Function Changes After Technetium-99m-Macroaggregated Albumin Administration and Their Predictive Value Regarding Hepatotoxicity in Patients Undergoing Yttrium-90-Radioembolization

BACKGROUND/AIM

Intraarterial Technetium-99m-Macroaggregated Albumin (^99mTc-MAA) administration is an established method to predict particle distribution prior to radioembolization. This study aimed to analyse the impact of intraarterial administration of ^99mTc-MAA on changes in liver-specific laboratory parameters and to assess whether such changes are associated with post-radioembolization hepatotoxicity.

PATIENTS AND METHODS

A total of 202 patients treated with radioembolization received prior mapping angiography with ^99mTc-MAA administration. All patients underwent clinical and laboratory examinations, including liver-specific parameters at certain times before and after mapping angiography/^99mTc-MAA administration, as well as before radioembolization and during follow-up.

RESULTS

Bilirubin increased temporarily after ^99mTc-MAA administration (p<0.001), but was not clinically relevant, and returned close to the initial value before radioembolization. These changes showed no association with subsequent postradioembolic hepatotoxicity or shortened overall survival.

CONCLUSION

^99mTc-MAA administration results in a significant, however, not clinically relevant transient increase in bilirubin levels, which does not provide a predictive value for subsequent radioembolization outcome or postradioembolic hepatotoxicity.

Machine-learning based exploration of determinants of gray matter volume in the KORA-MRI study

To identify the most important factors that impact brain volume, while accounting for potential collinearity, we used a data-driven machine-learning approach. Gray Matter Volume (GMV) was derived from magnetic resonance imaging (3T, FLAIR) and adjusted for intracranial volume (ICV). 93 potential determinants of GMV from the categories sociodemographics, anthropometric measurements, cardio-metabolic variables, lifestyle factors, medication, sleep, and nutrition were obtained from 293 participants from a population-based cohort from Southern Germany. Elastic net regression was used to identify the most important determinants of ICV-adjusted GMV. The four variables age (selected in each of the 1000 splits), glomerular filtration rate (794 splits), diabetes (323 splits) and diabetes duration (122 splits) were identified to be most relevant predictors of GMV adjusted for intracranial volume. The elastic net model showed better performance compared to a constant linear regression (mean squared error = 1.10 vs. 1.59, p < 0.001). These findings are relevant for preventive and therapeutic considerations and for neuroimaging studies, as they suggest to take information on metabolic status and renal function into account as potential confounders.

Splenogonadal fusion evaluation using Contrast Enhanced Ultrasound and Elastography. A case report

We present the case of splenogonadal fusion in a 53-year-old male patient assessed by ultrasound and MRI, confirmed by pathologic examination. In addition to B-mode and colour-coded Doppler ultrasound, shear wave elastography and CEUS were performed and are presented in detail. Splenogonadal fusion is a rare congenital anomaly presumably caused by an abnormal attachment of splenic tissue to the gonad during gestation. Diagnosis is challenging for clinicians and in unclear cases splenogonadal fusion might cause unnecessary orchiectomies with benign pathologic results. Ultrasound is the first-line imaging modality in the diagnosis of testicular pathologies. This case report summarizes all available modern ultrasound imagingtechnologies and highlights the possibilities for the diagnosis of splenogonadal fusion.

Preoperative ultrasonography as a mean of predicting the conversion of mini cholecystectomy into classic cholecystectomy

Cholecystectomy is considered to be the treatment of choice in symptomatic biliary lithiasis. Lately, due to medical progress, classic cholecystectomy has been gradually replaced by laparoscopic cholecystectomy and by mini cholecystectomy. Therefore, it is very important to determine certain preoperative factors which might predict the conversion of mini cholecystectomy (MC) into classic cholecystectomy (CC).

MATERIAL AND METHOD

The possibility of selecting high-risk conversion patients has important clinical implications, both for the surgeon and for the patient. Differentiating preoperative risk allows the surgeon to inform the patient about a high conversion risk to CC, and about the ensuing consequences: longer hospitalization period, longer postoperative recovery, greater costs. All the patients were examined by ultrasonography. The tests recorded six parameters: the diameter of the biliary duct (mm), the number of calculi, the diameter of the largest calculus (mm), the contracted aspect of the gallbladder, the distance between the tegument and the gallbladder fundus (cm), the distance between the tegument and the cystic duct (cm). All the variables were introduced into an initial model, which was checked using the colinearity method and significant observations, and subsequently reduced by eliminating insignificant predictive factors, revealed by Wald tests.

RESULTS

The significant predictive conversion factors to CC, quantified on the basis of regression analysis, are: age > 70, calculus with a diameter > 20 mm, biliary duct with a diameter > 6 mm, contracted gallbladder, distance between the tegument and gallbladder fundus > 7.2 cm, distance between the tegument and cystic duct > 17.1 cm.

CONCLUSION

Being a procedure that can be carried out on an outpatient basis and with rather low costs, ultrasonography plays a very important role in the preoperative prediction of converting MC to CC.

Neurons of origin of the neurotensinergic plexus enmeshing the ventral tegmental area in rat: retrograde labeling and in situ hybridization combined

The morphological and physiological substrates that underlie the mutual regulatory interactions of neurotensin and dopamine in the rat mesotelencephalic projections and related structures remain to be fully described. A salient candidate for neurotensinergic effects on the mesotelencephalic dopamine projection is the dense plexus of neurotensin immunoreactive axons that enmeshes the ventral tegmental area and substantia nigra, but the locations of the neurons that give rise to this plexus have not been identified and its systemic context remains obscure. To address this, Fluoro-Gold and the cholera toxin beta subunit, retrogradely transported axonal tracers, were injected into the ventral tegmental area of rats and the brains were processed to demonstrate neurons that contained both retrograde tracer immunoreactivity and a probe against neurotensin/neuromedin N messenger RNA. Substantial numbers of double-labeled neurons were observed in the rostral part of the lateral septum, and in a region centered on the shared boundaries of the bed nucleus of stria terminalis, ventromedial ventral pallidum, diagonal band of Broca, lateral preoptic area and rostral lateral hypothalamus. A few double-labeled neurons were also observed in the dorsal raphe nucleus and adjacent periaqueductal gray. Despite the administration of haloperidol and D-amphetamine to elicit and enhance neurotensin/neuromedin N messenger RNA expression in striatum, including the nucleus accumbens and olfactory tubercle, no double-labeled neurons were observed there. These results identify a novel brain substrate for control of midbrain dopamine levels, which affect reward mechanisms and motivation.