Atherosclerosis on CT coronary angiography and the risk of long-term cardiovascular events after liver transplantation.

Computed tomography coronary angiography (CTCA) is increasingly utilized for preoperative risk stratification before liver transplantation (LT). We sought to assess the predictors of advanced atherosclerosis on CTCA using the recently developed Coronary Artery Disease-Reporting and Data System (CAD-RADS) score and its impact on the prediction of long-term major adverse cardiovascular events (MACE) following LT. We conducted a retrospective cohort study of consecutive patients who underwent CTCA for LT work-up between 2011 and 2018. Advanced atherosclerosis was defined as coronary artery calcium scores > 400 or CAD-RADS score ≥ 3 (≥50% coronary artery stenosis). MACE was defined as myocardial infarction, heart failure, stroke, or resuscitated cardiac arrest. Overall, 229 patients underwent CTCA (mean age 66 ± 5 y, 82% male). Of these, 157 (68.5%) proceeded with LT. The leading etiology of cirrhosis was hepatitis (47%), and 53% of patients had diabetes before transplant. On adjusted analysis, male sex (OR 4.6, 95% CI 1.5-13.8, p = 0.006), diabetes (OR 2.2, 95% CI 1.2-4.2, p = 0.01) and dyslipidemia (OR 3.1, 95% CI 1.3-6.9, p = 0.005) were predictors of advanced atherosclerosis on CTCA. Thirty-two patients (20%) experienced MACE. At a median follow-up of 4 years, CAD-RADS ≥ 3, but not coronary artery calcium scores, was associated with a heightened risk of MACE (HR 5.8, 95% CI 1.6-20.6, p = 0.006). Based on CTCA results, 71 patients (31%) commenced statin therapy which was associated with a lower risk of all-cause mortality (HR 0.48, 95% CI 0.24-0.97, p = 0.04). The standardized CAD-RADS classification on CTCA predicted the occurrence of cardiovascular outcomes following LT, with a potential to increase the utilization of preventive cardiovascular therapies.

Automated inversion time selection for late gadolinium-enhanced cardiac magnetic resonance imaging.

OBJECTIVES: To develop and share a deep learning method that can accurately identify optimal inversion time (TI) from multi-vendor, multi-institutional and multi-field strength inversion scout (TI scout) sequences for late gadolinium enhancement cardiac MRI. MATERIALS AND METHODS: Retrospective multicentre study conducted on 1136 1.5-T and 3-T cardiac MRI examinations from four centres and three scanner vendors. Deep learning models, comprising a convolutional neural network (CNN) that provides input to a long short-term memory (LSTM) network, were trained on TI scout pixel data from centres 1 to 3 to identify optimal TI, using ground truth annotations by two readers. Accuracy within 50 ms, mean absolute error (MAE), Lin's concordance coefficient (LCCC) and reduced major axis regression (RMAR) were used to select the best model from validation results, and applied to holdout test data. Robustness of the best-performing model was also tested on imaging data from centre 4. RESULTS: The best model (SE-ResNet18-LSTM) produced accuracy of 96.1%, MAE 22.9 ms and LCCC 0.47 compared to ground truth on the holdout test set and accuracy of 97.3%, MAE 15.2 ms and LCCC 0.64 when tested on unseen external (centre 4) data. Differences in vendor performance were observed, with greatest accuracy for the most commonly represented vendor in the training data. CONCLUSION: A deep learning model was developed that can identify optimal inversion time from TI scout images on multi-vendor data with high accuracy, including on previously unseen external data. We make this model available to the scientific community for further assessment or development. CLINICAL RELEVANCE STATEMENT: A robust automated inversion time selection tool for late gadolinium-enhanced imaging allows for reproducible and efficient cross-vendor inversion time selection. KEY POINTS: • A model comprising convolutional and recurrent neural networks was developed to extract optimal TI from TI scout images. • Model accuracy within 50 ms of ground truth on multi-vendor holdout and external data of 96.1% and 97.3% respectively was achieved. • This model could improve workflow efficiency and standardise optimal TI selection for consistent LGE imaging.

Contemporary and Emerging MRI Strategies for Assessing Kidney Allograft Complications: Arterial Stenosis and Parenchymal Injury, From the AJR Special Series on Imaging of Fibrosis.

MRI plays an important role in the evaluation of kidney allografts for vascular complications as well as parenchymal insults. Transplant renal artery stenosis, the most common vascular complication of kidney transplant, can be evaluated by MRA using gadolinium and nongadolinium contrast agents as well as by unenhanced MRA techniques. Parenchymal injury occurs through a variety of pathways, including graft rejection, acute tubular injury, BK polyomavirus infection, drug-induced interstitial nephritis, and pyelonephritis. Investigational MRI techniques have sought to differentiate among these causes of dysfunction as well as to assess the degree of interstitial fibrosis or tubular atrophy (IFTA)-the common end pathway for all of these processes-which is currently evaluated by invasively obtained core biopsies. Some of these MRI sequences have shown promise in not only assessing the cause of parenchymal injury but also assessing IFTA noninvasively. This review describes current clinically used MRI techniques and previews promising investigational MRI techniques for assessing complications of kidney grafts.

Computed Tomography-Derived Extracellular Volume Fraction and Splenic Size for Liver Fibrosis Staging.

OBJECTIVE: Extracellular volume fraction (fECV) and liver and spleen size have been correlated with liver fibrosis stages and cirrhosis. The purpose of the current study was to determine the predictive value of fECV alone and in conjunction with measurement of liver and spleen size for severity of liver fibrosis. METHODS: This was a retrospective study of 95 subjects (65 with liver biopsy and 30 controls). Spearman rank correlation coefficient was used to assess correlation between radiological markers and fibrosis stage. Receiver operating characteristic analysis was performed to assess the discriminative ability of radiological markers for significant (F2+) and advanced (F3+) fibrosis and cirrhosis (F4), by reporting the area under the curve (AUC). RESULTS: The cohort had a mean age of 51.4 ± 14.4 years, and 52 were female (55%). There were 36, 5, 6, 9, and 39 in fibrosis stages F0, F1, F2, F3, and F4, respectively. Spleen volume alone showed the highest correlation (r = 0.552, P < 0.001) and AUCs of 0.823, 0.807, and 0.785 for identification of significant and advanced fibrosis and cirrhosis, respectively. Adding fECV to spleen length improved AUCs (0.764, 0.745, and 0.717 to 0.812, 0.781, and 0.738, respectively) compared with splenic length alone. However, adding fECV to spleen volume did not improve the AUCs for significant or advanced fibrosis or cirrhosis. CONCLUSIONS: Spleen size (measured in length or volume) showed better correlation with liver fibrosis stages compared with fECV. The combination of fECV and spleen length had higher accuracy compared with fECV alone or spleen length alone.

Opportunistic screening for osteoporosis using routine clinical care computed tomography brain studies.

OBJECTIVE: Osteoporosis and falls are both prevalent in the elderly, and CT brain (CTB) is frequently performed post head-strike. We aim to validate the relationship between frontal bone density (Hounsfield unit) from routine CTB and bone mineral density from dual-energy X-ray absorptiometry (DEXA) scan for opportunistic osteoporosis screening. MATERIALS AND METHODS: Patients who had a non-contrast CTB followed by a DEXA scan in the subsequent year were included in this multi-center retrospective study. The relationship between frontal bone density on CT and femoral neck T-score on DEXA was examined using ANOVA, Pearson's correlation, and receiver operating curve (ROC) analysis. Sensitivity, specificity, negative and positive predictive values, and area under the curve (AUC) were calculated. RESULTS: Three hundred twenty-six patients (205 females and 121 males) were analyzed. ANOVA analysis showed that frontal bone density was lower in patients with DEXA-defined osteoporosis (p < 0.001), while Pearson's correlation analysis demonstrated a fair correlation with femoral neck T-score (r = 0.3, p < 0.001). On subgroup analysis, these were true in females but not in males. On ROC analysis, frontal bone density weakly predicted osteoporosis (AUC 0.6, 95% CI 0.5-0.7) with no optimal threshold identified. HU < 610 was highly specific (87.5%) but poorly sensitive (18.9%). HU > 1200 in females had a strong negative predictive value for osteoporosis (92.6%, 95% CI 87.1-98.1%). CONCLUSION: Frontal bone density from routine CTB is significantly different between females with and without osteoporosis, but not between males. However, frontal bone density was a weak predictor for DEXA-defined osteoporosis. Further research is required to determine the role of CTB in opportunistic osteoporosis screening.

Merging virtual and physical experiences: extended realities in cardiovascular medicine.

Technological advancement and the COVID-19 pandemic have brought virtual learning and working into our daily lives. Extended realities (XR), an umbrella term for all the immersive technologies that merge virtual and physical experiences, will undoubtedly be an indispensable part of future clinical practice. The intuitive and three-dimensional nature of XR has great potential to benefit healthcare providers and empower patients and physicians. In the past decade, the implementation of XR into cardiovascular medicine has flourished such that it is now integrated into medical training, patient education, pre-procedural planning, intra-procedural visualization, and post-procedural care. This review article discussed how XR could provide innovative care and complement traditional practice, as well as addressing its limitations and considering its future perspectives.

Human biodistribution and radiation dosimetry of the demyelination tracer [18F]3F4AP.

PURPOSE: [18F]3F4AP is a novel PET radiotracer that targets voltage-gated potassium (K+) channels and has shown promise for imaging demyelinated lesions in animal models of neurological diseases. This study aimed to evaluate the biodistribution, safety, and radiation dosimetry of [18F]3F4AP in healthy human volunteers. METHODS: Four healthy volunteers (2 females) underwent a 4-h dynamic PET scan from the cranial vertex to mid-thigh using multiple bed positions after administration of 368 ± 17.9 MBq (9.94 ± 0.48 mCi) of [18F]3F4AP. Volumes of interest for relevant organs were manually drawn guided by the CT, and PET images and time-activity curves (TACs) were extracted. Radiation dosimetry was estimated from the integrated TACs using OLINDA software. Safety assessments included measuring vital signs immediately before and after the scan, monitoring for adverse events, and obtaining a comprehensive metabolic panel and electrocardiogram within 30 days before and after the scan. RESULTS: [18F]3F4AP distributed throughout the body with the highest levels of activity in the kidneys, urinary bladder, stomach, liver, spleen, and brain and with low accumulation in muscle and fat. The tracer cleared quickly from circulation and from most organs. The clearance of the tracer was noticeably faster than previously reported in nonhuman primates (NHPs). The average effective dose (ED) across all subjects was 12.1 ± 2.2 µSv/MBq, which is lower than the estimated ED from the NHP studies (21.6 ± 0.6 µSv/MBq) as well as the ED of other fluorine-18 radiotracers such as [18F]FDG (~ 20 µSv/MBq). No differences in ED between males and females were observed. No substantial changes in safety assessments or adverse events were recorded. CONCLUSION: The biodistribution and radiation dosimetry of [18F]3F4AP in humans are reported for the first time. The average total ED across four subjects was lower than most 18F-labeled PET tracers. The tracer and study procedures were well tolerated, and no adverse events occurred.

Joint EANM/SIOPE/RAPNO practice guidelines/SNMMI procedure standards for imaging of paediatric gliomas using PET with radiolabelled amino acids and [18F]FDG: version 1.0.

Positron emission tomography (PET) has been widely used in paediatric oncology. 2-Deoxy-2-[18F]fluoro-D-glucose ([18F]FDG) is the most commonly used radiopharmaceutical for PET imaging. For oncological brain imaging, different amino acid PET radiopharmaceuticals have been introduced in the last years. The purpose of this document is to provide imaging specialists and clinicians guidelines for indication, acquisition, and interpretation of [18F]FDG and radiolabelled amino acid PET in paediatric patients affected by brain gliomas. There is no high level of evidence for all recommendations suggested in this paper. These recommendations represent instead the consensus opinion of experienced leaders in the field. Further studies are needed to reach evidence-based recommendations for the applications of [18F]FDG and radiolabelled amino acid PET in paediatric neuro-oncology. These recommendations are not intended to be a substitute for national and international legal or regulatory provisions and should be considered in the context of good practice in nuclear medicine. The present guidelines/standards were developed collaboratively by the EANM and SNMMI with the European Society for Paediatric Oncology (SIOPE) Brain Tumour Group and the Response Assessment in Paediatric Neuro-Oncology (RAPNO) working group. They summarize also the views of the Neuroimaging and Oncology and Theranostics Committees of the EANM and reflect recommendations for which the EANM and other societies cannot be held responsible.

Consensus-Based Technical Recommendations for Clinical Translation of Renal Phase Contrast MRI.

BACKGROUND: Phase-contrast (PC) MRI is a feasible and valid noninvasive technique to measure renal artery blood flow, showing potential to support diagnosis and monitoring of renal diseases. However, the variability in measured renal blood flow values across studies is large, most likely due to differences in PC-MRI acquisition and processing. Standardized acquisition and processing protocols are therefore needed to minimize this variability and maximize the potential of renal PC-MRI as a clinically useful tool. PURPOSE: To build technical recommendations for the acquisition, processing, and analysis of renal 2D PC-MRI data in human subjects to promote standardization of renal blood flow measurements and facilitate the comparability of results across scanners and in multicenter clinical studies. STUDY TYPE: Systematic consensus process using a modified Delphi method. POPULATION: Not applicable. SEQUENCE FIELD/STRENGTH: Renal fast gradient echo-based 2D PC-MRI. ASSESSMENT: An international panel of 27 experts from Europe, the USA, Australia, and Japan with 6 (interquartile range 4-10) years of experience in 2D PC-MRI formulated consensus statements on renal 2D PC-MRI in two rounds of surveys. Starting from a recently published systematic review article, literature-based and data-driven statements regarding patient preparation, hardware, acquisition protocol, analysis steps, and data reporting were formulated. STATISTICAL TESTS: Consensus was defined as ≥75% unanimity in response, and a clear preference was defined as 60-74% agreement among the experts. RESULTS: Among 60 statements, 57 (95%) achieved consensus after the second-round survey, while the remaining three showed a clear preference. Consensus statements resulted in specific recommendations for subject preparation, 2D renal PC-MRI data acquisition, processing, and reporting. DATA CONCLUSION: These recommendations might promote a widespread adoption of renal PC-MRI, and may help foster the set-up of multicenter studies aimed at defining reference values and building larger and more definitive evidence, and will facilitate clinical translation of PC-MRI. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY STAGE: 1.

CardiSort: a convolutional neural network for cross vendor automated sorting of cardiac MR images.

OBJECTIVES: To develop an image-based automatic deep learning method to classify cardiac MR images by sequence type and imaging plane for improved clinical post-processing efficiency. METHODS: Multivendor cardiac MRI studies were retrospectively collected from 4 centres and 3 vendors. A two-head convolutional neural network ('CardiSort') was trained to classify 35 sequences by imaging sequence (n = 17) and plane (n = 10). Single vendor training (SVT) on single-centre images (n = 234 patients) and multivendor training (MVT) with multicentre images (n = 434 patients, 3 centres) were performed. Model accuracy and F1 scores on a hold-out test set were calculated, with ground truth labels by an expert radiologist. External validation of MVT (MVTexternal) was performed on data from 3 previously unseen magnet systems from 2 vendors (n = 80 patients). RESULTS: Model sequence/plane/overall accuracy and F1-scores were 85.2%/93.2%/81.8% and 0.82 for SVT and 96.1%/97.9%/94.3% and 0.94 MVT on the hold-out test set. MVTexternal yielded sequence/plane/combined accuracy and F1-scores of 92.7%/93.0%/86.6% and 0.86. There was high accuracy for common sequences and conventional cardiac planes. Poor accuracy was observed for underrepresented classes and sequences where there was greater variability in acquisition parameters across centres, such as perfusion imaging. CONCLUSIONS: A deep learning network was developed on multivendor data to classify MRI studies into component sequences and planes, with external validation. With refinement, it has potential to improve workflow by enabling automated sequence selection, an important first step in completely automated post-processing pipelines. KEY POINTS: • Deep learning can be applied for consistent and efficient classification of cardiac MR image types. • A multicentre, multivendor study using a deep learning algorithm (CardiSort) showed high classification accuracy on a hold-out test set with good generalisation to images from previously unseen magnet systems. • CardiSort has potential to improve clinical workflows, as a vital first step in developing fully automated post-processing pipelines.