Height and body surface area versus wall stress for stratification of mid-term outcomes in ascending aortic aneurysm.

Objectives: Current diameter-based guidelines for ascending thoracic aortic aneurysms (aTAA) do not consistently predict risk of dissection/rupture. ATAA wall stresses may enhance risk stratification independent of diameter. The relation of wall stresses and diameter indexed to height and body surface area (BSA) is unknown. Our objective was to compare aTAA wall stresses with indexed diameters in relation to all-cause mortality at 3.75 years follow-up. Methods: Finite element analyses were performed in a veteran population with aortas ≥ 4.0 cm. Three-dimensional geometries were reconstructed from computed tomography with models accounting for pre-stress geometries. A fiber-embedded hyperelastic material model was applied to obtain wall stress distributions under systolic pressure. Peak wall stresses were compared across guideline thresholds for diameter/BSA and diameter/height. Hazard ratios for all-cause mortality and surgical aneurysm repair were estimated using cause-specific Cox proportional hazards models. Results: Of 253 veterans, 54 (21 %) had aneurysm repair at 3.75 years. Indexed diameter alone would have prompted repair at baseline in 17/253 (6.7 %) patients, including only 4/230 (1.7 %) with diameter < 5.5 cm. Peak wall stresses did not significantly differ across guideline thresholds for diameter/BSA (circumferential: p = 0.15; longitudinal: p = 0.18), but did differ for diameter/height (circumferential: p = 0.003; longitudinal: p = 0.048). All-cause mortality was independently associated with peak longitudinal stresses (p = 0.04). Peak longitudinal stresses were best predicted by diameter (c-statistic = 0.66), followed by diameter/height (c-statistic = 0.59), and diameter/BSA (c-statistic = 0.55). Conclusions: Diameter/height improved stratification of peak wall stresses compared to diameter/BSA. Peak longitudinal stresses predicted all-cause mortality independent of age and indexed diameter and may aid risk stratification for aTAA adverse events.

Diagnosis of intracranial aneurysms by computed tomography angiography using deep learning-based detection and segmentation.

BACKGROUND: Detecting and segmenting intracranial aneurysms (IAs) from angiographic images is a laborious task. OBJECTIVE: To evaluates a novel deep-learning algorithm, named vessel attention (VA)-Unet, for the efficient detection and segmentation of IAs. METHODS: This retrospective study was conducted using head CT angiography (CTA) examinations depicting IAs from two hospitals in China between 2010 and 2021. Training included cases with subarachnoid hemorrhage (SAH) and arterial stenosis, common accompanying vascular abnormalities. Testing was performed in cohorts with reference-standard digital subtraction angiography (cohort 1), with SAH (cohort 2), acquired outside the time interval of training data (cohort 3), and an external dataset (cohort 4). The algorithm's performance was evaluated using sensitivity, recall, false positives per case (FPs/case), and Dice coefficient, with manual segmentation as the reference standard. RESULTS: The study included 3190 CTA scans with 4124 IAs. Sensitivity, recall, and FPs/case for detection of IAs were, respectively, 98.58%, 96.17%, and 2.08 in cohort 1; 95.00%, 88.8%, and 3.62 in cohort 2; 96.00%, 93.77%, and 2.60 in cohort 3; and, 96.17%, 94.05%, and 3.60 in external cohort 4. The segmentation accuracy, as measured by the Dice coefficient, was 0.78, 0.71, 0.71, and 0.66 for cohorts 1-4, respectively. VA-Unet detection recall and FPs/case and segmentation accuracy were affected by several clinical factors, including aneurysm size, bifurcation aneurysms, and the presence of arterial stenosis and SAH. CONCLUSIONS: VA-Unet accurately detected and segmented IAs in head CTA comparably to expert interpretation. The proposed algorithm has significant potential to assist radiologists in efficiently detecting and segmenting IAs from CTA images.

Measurement of Abdominal Aortic Aneurysm Strain Using MR Deformable Image Registration: Accuracy and Relationship to Recent Aneurysm Progression.

BACKGROUND: Management of asymptomatic abdominal aortic aneurysm (AAA) based on maximum aneurysm diameter and growth rate fails to preempt many ruptures. Assessment of aortic wall biomechanical properties may improve assessment of progression and rupture risk. This study aimed to assess the accuracy of AAA wall strain measured by cine magnetic resonance imaging (MRI) deformable image registration (MR strain) and investigate its relationship with recent AAA progression. METHODS: The MR strain accuracy was evaluated in silico against ground truth strain in 54 synthetic MRIs generated from a finite element model simulation of an AAA patient's abdomen for different aortic pulse pressures, tissue motions, signal intensity variations, and image noise. Evaluation included bias with 95% confidence interval (CI) and correlation analysis. Association of MR strain with AAA growth rate was assessed in 25 consecutive patients with >6 months of prior surveillance, for whom cine balanced steady-state free-precession imaging was acquired at the level of the AAA as well as the proximal, normal-caliber aorta. Univariate and multivariate regressions were used to associate growth rate with clinical variables, maximum AAA diameter (Dmax), and peak circumferential MR strain through the cardiac cycle. The MR strain interoperator variability was assessed using bias with 95% CI, intraclass correlation coefficient, and coefficient of variation. RESULTS: In silico experiments revealed an MR strain bias of 0.48% ± 0.42% and a slope of correlation to ground truth strain of 0.963. In vivo, AAA MR strain (1.2% ± 0.6%) was highly reproducible (bias ± 95% CI, 0.03% ± 0.31%; intraclass correlation coefficient, 97.8%; coefficient of variation, 7.14%) and was lower than in the nonaneurysmal aorta (2.4% ± 1.7%). Dmax (ß = 0.087) and MR strain (ß= -1.563) were both associated with AAA growth rate. The MR strain remained an independent factor associated with growth rate (ß= -0.904) after controlling for Dmax. CONCLUSIONS: Deformable image registration analysis can accurately measure the circumferential strain of the AAA wall from standard cine MRI and may offer patient-specific insight regarding AAA progression.

Aortic area/height ratio, peak wall stresses, and outcomes in veterans with tricuspid versus bicuspid aortic valve-associated ascending thoracic aortic aneurysms.

BACKGROUND: In ascending thoracic aortic aneurysm risk stratification, aortic area/height ratio is a reasonable alternative to maximum diameter. Biomechanically, aortic dissection may be initiated by wall stress exceeding wall strength. Our objective was to evaluate the association between aortic area/height and peak aneurysm wall stresses in relation to valve morphology and 3-year all-cause mortality. METHODS: Finite element analysis was performed on 270 ascending thoracic aortic aneurysms (46 associated with bicuspid and 224 with tricuspid aortic valves) in veterans. Three-dimensional aneurysm geometries were reconstructed from computed tomography and models developed accounting for prestress geometries. Fiber-embedded hyperelastic material model was applied to obtain aneurysm wall stresses during systole. Correlations of aortic area/height ratio and peak wall stresses were compared across valve types. Area/height ratio was evaluated across peak wall stress thresholds obtained from proportional hazards models of 3-year all-cause mortality, with aortic repair treated as a competing risk. RESULTS: Aortic area/height 10 cm2/m or greater coincided with 23/34 (68%) 5.0 to 5.4 cm and 20/24 (83%) 5.5 cm or greater aneurysms. Area/height correlated weakly with peak aneurysm stresses: for tricuspid valves, r = 0.22 circumferentially and r = 0.24 longitudinally; and for bicuspid valves, r = 0.42 circumferentially and r = 0.14 longitudinally. Age and peak longitudinal stress, but not area/height, were independent predictors of all-cause mortality (age: hazard ratio, 2.20 per 9-year increase, P = .013; peak longitudinal stress: hazard ratio, 1.78 per 73-kPa increase, P = .035). CONCLUSIONS: Area/height was more predictive of high circumferential stresses in bicuspid than tricuspid valve aneurysms, but similarly less predictive of high longitudinal stresses in both valve types. Peak longitudinal stress, not area/height, independently predicted all-cause mortality. VIDEO ABSTRACT.

Contrast-enhanced CT radiomics improves the prediction of abdominal aortic aneurysm progression.

OBJECTIVES: To determine if three-dimensional (3D) radiomic features of contrast-enhanced CT (CECT) images improve prediction of rapid abdominal aortic aneurysm (AAA) growth. METHODS: This longitudinal cohort study retrospectively analyzed 195 consecutive patients (mean age, 72.4 years ± 9.1) with a baseline CECT and a subsequent CT or MR at least 6 months later. 3D radiomic features were measured for 3 regions of the AAA, viz. the vessel lumen only; the intraluminal thrombus (ILT) and aortic wall only; and the entire AAA sac (lumen, ILT, and wall). Multiple machine learning (ML) models to predict rapid growth, defined as the upper tercile of observed growth (> 0.25 cm/year), were developed using data from 60% of the patients. Diagnostic accuracy was evaluated using the area under the receiver operating characteristic curve (AUC) in the remaining 40% of patients. RESULTS: The median AAA maximum diameter was 3.9 cm (interquartile range [IQR], 3.3-4.4 cm) at baseline and 4.4 cm (IQR, 3.7-5.4 cm) at the mean follow-up time of 3.2 ± 2.4 years (range, 0.5-9 years). A logistic regression model using 7 radiomic features of the ILT and wall had the highest AUC (0.83; 95% confidence interval [CI], 0.73-0.88) in the development cohort. In the independent test cohort, this model had a statistically significantly higher AUC than a model including maximum diameter, AAA volume, and relevant clinical factors (AUC = 0.78, 95% CI, 0.67-0.87 vs AUC = 0.69, 95% CI, 0.57-0.79; p = 0.04). CONCLUSION: A radiomics-based method focused on the ILT and wall improved prediction of rapid AAA growth from CECT imaging. KEY POINTS: • Radiomic analysis of 195 abdominal CECT revealed that an ML-based model that included textural features of intraluminal thrombus (if present) and aortic wall improved prediction of rapid AAA progression compared to maximum diameter. • Predictive accuracy was higher when radiomic features were obtained from the thrombus and wall as opposed to the entire AAA sac (including lumen), or the lumen alone. • Logistic regression of selected radiomic features yielded similar accuracy to predict rapid AAA progression as random forests or support vector machines.

Dynamic Contrast-Enhanced MRI in Abdominal Aortic Aneurysms as a Potential Marker for Disease Progression.

BACKGROUND: Abdominal aortic aneurysms (AAAs) may rupture before reaching maximum diameter (Dmax ) thresholds for repair. Aortic wall microvasculature has been associated with elastin content and rupture sites in specimens, but its relation to progression is unknown. PURPOSE: To investigate whether dynamic contrast-enhanced (DCE) MRI of AAA is associated with Dmax or growth. STUDY TYPE: Prospective. POPULATION: A total of 27 male patients with infrarenal AAA (mean age ± standard deviation = 75 ± 5 years) under surveillance with DCE MRI and 2 years of prior follow-up intervals with computed tomography (CT) or MRI. FIELD STRENGTH/SEQUENCE: A 3-T, dynamic three-dimensional (3D) fast gradient-echo stack-of-stars volumetric interpolated breath-hold examination (Star-VIBE). ASSESSMENT: Wall voxels were manually segmented in two consecutive slices at the level of Dmax . We measured slope to 1-minute and area under the curve (AUC) to 1 minute and 4 minutes of the signal intensity change postcontrast relative to that precontrast arrival, and, Ktrans , a measure of microvascular permeability, using the Patlak model. These were averaged over all wall voxels for association to Dmax and growth rate, and, over left/right and anterior/posterior quadrants for testing circumferential homogeneity. Dmax was measured orthogonal to the aortic centerline and growth rate was calculated by linear fit of Dmax measurements. STATISTICAL TESTS: Pearson correlation and linear mixed effects models. A P value <0.05 was considered statistically significant. RESULTS: In 44 DCE MRIs, mean Dmax was 45 ± 7 mm and growth rate in 1.5 ± 0.4 years of prior follow-up was 1.7 ± 1.2 mm per year. DCE measurements correlated with each other (Pearson r = 0.39-0.99) and significantly differed between anterior/posterior versus left/right quadrants. DCE measurements were not significantly associated with Dmax (P = 0.084, 0.289, 0.054 and 0.255 for slope, AUC at 1 minute and 4 minutes, and Ktrans , respectively). Slope and 4 minutes AUC significantly associated with growth rate after controlling for Dmax . CONCLUSION: Contrast uptake may be increased in lateral aspects of the AAA. Contrast enhancement 1-minute slope and 4-minutes AUC may be associated with a period of recent AAA growth that is independent of Dmax . EVIDENCE LEVEL: 3. TECHNICAL EFFICACY: Stage 2.

Association of 3-Year All-Cause Mortality and Peak Wall Stresses of Ascending Thoracic Aortic Aneurysms in Veterans.

Risk of aortic dissection in ascending thoracic aortic aneurysms is not sufficiently captured by size-based metrics. From a biomechanical perspective, dissection may be initiated when wall stress exceeds wall strength. Our objective was to assess the association between aneurysm peak wall stresses and 3-year all-cause mortality. Finite element analysis was performed in 273 veterans with chest computed tomography for surveillance of ascending thoracic aortic aneurysms. Three-dimensional geometries were reconstructed and models developed accounting for prestress geometries. A fiber-embedded hyperelastic material model was applied to obtain circumferential and longitudinal wall stresses under systolic pressure. Patients were followed up to 3 years following the scan to assess aneurysm repair and all-cause mortality. Fine-Gray subdistribution hazards were estimated for all-cause mortality based on age, aortic diameter, and peak wall stresses, treating aneurysm repair as a competing risk. When accounting for age, subdistribution hazard of mortality was not significantly increased by peak circumferential stresses (p = 0.30) but was significantly increased by peak longitudinal stresses (p = 0.008). Aortic diameter did not significantly increase subdistribution hazard of mortality in either model (circumferential model: p = 0.38; longitudinal model: p = 0.30). The effect of peak longitudinal stresses on subdistribution hazard of mortality was maximized at a binary threshold of 355kPa, which captured 34 of 212(16%) patients with diameter <5 cm, 11 of 36(31%) at 5.0-5.4 cm, and 11 of 25(44%) at ≥5.5 cm. Aneurysm peak longitudinal stresses stratified by age and diameter were associated with increased hazard of 3-year all-cause mortality in a veteran cohort. Risk prediction may be enhanced by considering peak longitudinal stresses.

Impact of Implicit Abdominal Aortic Aneurysm Screening in the Veterans Affairs Health Care System Over 10 Years.

Background Abdominal aortic aneurysm (AAA) screening programs have been active in the United States since 2005, but are not the only way AAAs are detected. AAA management and outcomes have not been investigated broadly in the context of "implicit AAA screening," whereby radiologic examinations not intended for focused screening can identify AAAs. Methods and Results We examined the association between imaging-based AAA screening, both explicit and implicit, and various outcomes for ≈1.6 million veterans in the Veterans Affairs health care system from 2005 to 2015. Screened-positive, screened-negative, and unscreened veterans were identified in the overall cohort and within a subgroup of veterans aged 65 years in 2005. The yearly composite screening rate increased over 10 years, from 11.7% to 18.3%, whereas the screened-positive rate decreased from 7.3% to 4.9%. Only 12.9% of screening examinations were explicit AAA screening ultrasounds. The subgroup's composite screening rate was 74% within its 10-year eligibility window, with implicit screening accounting for 91.8% of examinations. In the 2005 subgroup, all-cause mortality and Charlson comorbidity scores were higher for veterans who underwent screening compared with those unscreened (31.2% versus 23.1% and 0.47 versus 0.25, respectively; P<0.001). AAA rupture rates were similar between those unscreened and screened-negative individuals. Conclusions Accounting for both explicit and implicit screening, AAA screening in the Veterans Affairs population has moderate reach. Efforts to expand explicit AAA screening are not likely to impact either all-cause mortality or AAA rupture on the population scale as significantly as a careful accounting for and use of implicit screening data.

Deep neural network-based detection and segmentation of intracranial aneurysms on 3D rotational DSA.

OBJECTIVE: Accurate diagnosis and measurement of intracranial aneurysms are challenging. This study aimed to develop a 3D convolutional neural network (CNN) model to detect and segment intracranial aneurysms (IA) on 3D rotational DSA (3D-RA) images. METHODS: 3D-RA images were collected and annotated by 5 neuroradiologists. The annotated images were then divided into three datasets: training, validation, and test. A 3D Dense-UNet-like CNN (3D-Dense-UNet) segmentation algorithm was constructed and trained using the training dataset. Diagnostic performance to detect aneurysms and segmentation accuracy was assessed for the final model on the test dataset using the free-response receiver operating characteristic (FROC). Finally, the CNN-inferred maximum diameter was compared against expert measurements by Pearson's correlation and Bland-Altman limits of agreement (LOA). RESULTS: A total of 451 patients with 3D-RA images were split into n = 347/41/63 training/validation/test datasets, respectively. For aneurysm detection, observed FROC analysis showed that the model managed to attain a sensitivity of 0.710 at 0.159 false positives (FP)/case, and 0.986 at 1.49 FP/case. The proposed method had good agreement with reference manual aneurysmal maximum diameter measurements (8.3 ± 4.3 mm vs. 7.8 ± 4.8 mm), with a correlation coefficient r = 0.77, small bias of 0.24 mm, and LOA of -6.2 to 5.71 mm. 37.0% and 77% of diameter measurements were within ±1 mm and ±2.5 mm of expert measurements. CONCLUSIONS: A 3D-Dense-UNet model can detect and segment aneurysms with relatively high accuracy using 3D-RA images. The automatically measured maximum diameter has potential clinical application value.

Improved Appropriateness of Advanced Diagnostic Imaging After Implementation of Clinical Decision Support Mechanism.

The Protecting Access to Medicare Act (PAMA) mandates clinical decision support mechanism (CDSM) consultation for all advanced imaging. There are a growing number of studies examining the association of CDSM use with imaging appropriateness, but a paucity of multicenter data. This observational study evaluates the association between changes in advanced imaging appropriateness scores with increasing provider exposure to CDSM. Each provider's first 200 consecutive anonymized requisitions for advanced imaging (CT, MRI, ultrasound, nuclear medicine) using a single CDSM (CareSelect, Change Healthcare) between January 1, 2017 and December 31, 2019 were collected from 288 US institutions. Changes in imaging requisition proportions among four appropriateness categories ("usually appropriate" [green], "may be appropriate" [yellow], "usually not appropriate" [red], and unmapped [gray]) were evaluated in relation to the chronological order of the requisition for each provider and total provider exposure to CDSM using logistic regression fits and Wald tests. The number of providers and requisitions included was 244,158 and 7,345,437, respectively. For 10,123 providers with ≥ 200 requisitions (2,024,600 total requisitions), the fraction of green, yellow, and red requisitions among the last 10 requisitions changed by +3.0% (95% confidence interval +2.6% to +3.4%), -0.8% (95% CI -0.5% to -1.1%), and -3.0% (95% CI 3.3% to -2.7%) in comparison with the first 10, respectively. Providers with > 190 requisitions had 8.5% (95% CI 6.3% to 10.7%) more green requisitions, 2.3% (0.7% to 3.9%) fewer yellow requisitions, and 0.5% (95% CI -1.0% to 2.0%) fewer red (not statistically significant) requisitions relative to providers with ≤ 10 requisitions. Increasing provider exposure to CDSM is associated with improved appropriateness scores for advanced imaging requisitions.

Abdominal aortic aneurysm measurement at CT/MRI: potential clinical ramifications of non-standardized measurement technique and importance of multiplanar reformation.

Accurate and reproducible measurement of abdominal aortic aneurysm (AAA) size is an essential component of patient management, and most reliably performed at CT using a multiplanar reformat (MPR) strategy. This approach is not universal, however. This study aims to characterize the measurement error present in routine clinical assessment of AAAs and the potential clinical ramifications. Patients were included if they had AAA assessed by CT and/or MRI at two time points at least 6 months apart. Clinical maximal AAA diameter, assessed by non-standardized methods, was abstracted from the radiology report at each time point and compared to the reference aneurysm diameter measured using a MPR strategy. Discrepancies between clinical and reference diameters, and associated aneurysm enlargement rates were analyzed. Two hundred thirty patients were included, with average follow-up 3.3±2.5 years. When compared to MPR-derived diameters, clinical aneurysm measurement inaccuracy was, on average, 3.3 mm. Broad limits of agreement were found for both clinical diameters [-6.7 to +6.5 mm] and aneurysm enlargement rates [-4.6 to +4.2 mm/year] when compared to MPR-based measures. Of 78 AAAs measuring 5-6 cm by the MPR method, 21 (26.9%) were misclassified by the clinical measurement with respect to a common repair threshold (5.5 cm), of which 5 were misclassified as below, and 16 were misclassified as above the threshold. The clinical use of non-standardized AAA measurement strategies can lead to incorrect classification of AAAs as larger or smaller than the commonly accepted repair threshold of 5.5 cm and can induce large errors in quantification of aneurysm enlargement rate.

Computer-aided quantification of non-contrast 3D black blood MRI as an efficient alternative to reference standard manual CT angiography measurements of abdominal aortic aneurysms.

BACKGROUND: Non-contrast 3D black blood MRI is a promising tool for abdominal aortic aneurysm (AAA) surveillance, permitting accurate aneurysm diameter measurements needed for patient management. PURPOSE: To evaluate whether automated AAA volume and diameter measurements obtained from computer-aided segmentation of non-contrast 3D black blood MRI are accurate, and whether they can supplant reference standard manual measurements from contrast-enhanced CT angiography (CTA). MATERIALS AND METHODS: Thirty AAA patients (mean age, 71.9 ± 7.9 years) were recruited between 2014 and 2017. Participants underwent both non-contrast black blood MRI and CTA within 3 months of each other. Semi-automatic (computer-aided) MRI and CTA segmentations utilizing deformable registration methods were compared against manual segmentations of the same modality using the Dice similarity coefficient (DSC). AAA lumen and total aneurysm volumes and AAA maximum diameter, quantified automatically from these segmentations, were compared against manual measurements using Pearson correlation and Bland-Altman analyses. Finally, automated measurements from non-contrast 3D black blood MRI were evaluated against manual CTA measurements using the Wilcoxon test, Pearson correlation and Bland-Altman analyses. RESULTS: Semi-automatic segmentations had excellent agreement with manual segmentations (lumen DSC: 0.91 ± 0.03 and 0.94 ± 0.03; total aneurysm DSC: 0.92 ± 0.02 and 0.94 ± 0.03, for black blood MRI and CTA, respectively). Automated volume and maximum diameter measurements also had excellent correlation to their manual counterparts for both black blood MRI (volume: r = 0.99, P < 0.001; diameter: r = 0.97, P < 0.001) and CTA (volume: r = 0.99, P < 0.001; diameter: r = 0.97, P < 0.001). Compared to manual CTA measurements, bias and limits of agreement (LOA) for automated MRI measurements (lumen volume: 1.49, [-4.19 7.17] cm3; outer wall volume: -2.46, [-14.05 9.13] cm3; maximal diameter: 0.08, [-6.51 6.67] mm) were largely equivalent to those of manual MRI measurements, particularly for maximum AAA diameter (lumen volume: 0.73, [-6.47 7.93] cm3; outer wall volume: 0.98, [-10.54 12.5] cm3; maximal diameter: 0.08, [-3.67 3.83] mm). CONCLUSION: Semi-automatic segmentation of non-contrast 3D black blood MRI efficiently provides reproducible morphologic AAA assessment yielding accurate AAA diameters and volumes with no clinically relevant differences compared to either automatic or manual measurements based on CTA.

Assessment of Superparamagnetic Iron Oxide Nanoparticle Poly(Ethylene Glycol) Coatings on Magnetic Resonance Relaxation for Early Disease Detection.

OBJECTIVE: Superparamagnetic Iron Oxide Nanoparticles (SPIONs) are widely researched as contrast agents in clinical magnetic resonance imaging (MRI). SPIONs are frequently coated with anti-biofouling substances such as poly(ethylene glycol) (PEG) to prevent protein deposition and improve circulation time in vivo. However, few previous studies have comprehensively examined optimization of SPION MR properties with respect to physicochemical properties of the core SPION and the polymeric coating. The aim of this study is to determine effects of different methods of chemical attachment of a polymer, polymer chain length, and polymer coating density on the MR relaxivities of SPIONs, thereby contributing to a better understanding of the interaction of these parameters and the efficacy of the designed agent. RESULTS: These studies indicate that the chemical composition and, in particular, the hydrophobicity/hydrophilicity of the chemical group linking PEG chains to a SPION core may play a larger role in the resulting MR relaxivities than other variable properties such as SPION core size and PEG chain length. CONCLUSIONS: The method of SPION fabrication and chemical composition of the coating play a significant role in the MR relaxivities of the resulting particles. These results should be considered in the fabrication of particles for clinical purposes, particularly when optimization of the MR relaxivities is desired.

RadioGraphics Update: Medical 3D Printing for the Radiologist.

Editor's Note.-Articles in the RadioGraphics Update section provide current knowledge to supplement or update information found in full-length articles previously published in RadioGraphics. Authors of the previously published article provide a brief synopsis that emphasizes important new information such as technological advances, revised imaging protocols, new clinical guidelines involving imaging, or updated classification schemes. Articles in this section are published solely online and are linked to the original article.

Initial evaluation of a convolutional neural network used for noninvasive assessment of coronary artery disease severity from coronary computed tomography angiography data.

PURPOSE: Coronary computed tomography angiography (CTA) has one of the highest diagnostic sensitivities for detection of the significance of coronary artery disease (CAD); however, sensitivity is moderate and may result in increased catheterization rates. We performed an efficacy study to determine whether a trained machine learning algorithm that uses coronary CTA data may improve CAD diagnosis accuracy. METHODS: Sixty-four-patient image datasets based on coronary CTA were retrospectively collected to generate eight views considering 45° increments around the coronary artery centerline. The dataset was randomly split into training and testing cohorts. Invasive FFR measurements were used as ground truth labels. A convolutional neural network (CNN) was trained and the model's capacity to predict severity of CAD was assessed on the testing cohort. Classification accuracy and area under the receiver operating characteristic curve (AUROC) analysis were performed. Similar CAD severity classification accuracy and AUROC analyses were performed using only percent diameter stenosis (%DS) and CT-derived FFR performed by 13 operators with various levels of expertise. RESULTS: Classification accuracy over the test cohort was 80.9% using the trained network and 72.4% using the user-operated CT-derived FFR software. AUROC over the test cohort was 0.862 using the trained network, 0.807 using %DS, and 0.758 using the human-operated CT-derived FFR software. CONCLUSIONS: A trained neural network compared noninferiorly in-terms of classification accuracy and AUROC with human operators of a CT-derived FFR software, and in-terms of AUROC with clinical decision-making using %DS.

Patient-specific 3D-printed coronary models based on coronary computed tomography angiography volumes to investigate flow conditions in coronary artery disease.

BACKGROUND: 3D printed patient-specific coronary models have the ability to enable repeatable benchtop experiments under controlled blood flow conditions. This approach can be applied to CT-derived patient geometries to emulate coronary flow and related parameters such as Fractional Flow Reserve (FFR). METHODS: This study uses 3D printing to compare such benchtop FFR results with a non-invasive CT-FFR research software algorithm and catheter based invasive FFR (I-FFR) measurements. Fifty-two patients with a clinical indication for I-FFR underwent a research Coronary CT Angiography (CCTA) prior to catheterization. CT images were used to measure CT-FFR and to generate patient-specific 3D printed models of the aortic root and three main coronary arteries. Each patient-specific model was connected to a programmable pulsatile pump and benchtop FFR (B-FFR) was derived from pressures measured proximal and distal to coronary stenosis using pressure transducers. B-FFR was measured for two coronary outflow rates ('normal', 250 ml min-1; and 'hyperemic', 500 ml min-1) by adjusting the model's distal coronary resistance. RESULTS: Pearson correlations and ROC AUC were calculated using invasive I-FFR as reference. The Pearson correlation factor of CT-FFR and B-FFR-500 was 0.75 and 0.71, respectively. Areas under the ROCs for CT-FFR and B-FFR-500 were 0.80 (95%CI: 0.70-0.87) and 0.81 (95%CI: 0.64-0.91) respectively. CONCLUSION: Benchtop flow simulations with 3D printed models provide the capability to measure pressure changes at any location in the model, for ultimately emulating the FFR at several simulated physiological blood flow conditions. CLINICAL TRIAL REGISTRATION: https://clinicaltrials.gov/show/NCT03149042.

Initial evaluation of three-dimensionally printed patient-specific coronary phantoms for CT-FFR software validation.

We developed three-dimensionally (3D) printed patient-specific coronary phantoms that are capable of sustaining physiological flow and pressure conditions. We assessed the accuracy of these phantoms from coronary CT acquisition, benchtop experimentation, and CT-FFR software. Five patients with coronary artery disease underwent 320-detector row coronary CT angiography (CCTA) (Aquilion ONE, Canon Medical Systems) and a catheter lab procedure to measure fractional flow reserve (FFR). The aortic root and three main coronary arteries were segmented (Vitrea, Vital Images) and 3D printed (Eden 260V, Stratasys). Phantoms were connected into a pulsatile flow loop, which replicated physiological flow and pressure gradients. Contrast was introduced and the phantoms were scanned using the same CT scanner model and CCTA protocol as used for the patients. Image data from the phantoms were input to a CT-FFR research software (Canon Medical Systems) and compared to those derived from the clinical data, along with comparisons between image measurements and benchtop FFR results. Phantom diameter measurements were within 1 mm on average compared to patient measurements. Patient and phantom CT-FFR results had an absolute mean difference of 4.34% and Pearson correlation of 0.95. We have demonstrated the capabilities of 3D printed patient-specific phantoms in a diagnostic software.

Clinical applications of three-dimensional printing in otolaryngology-head and neck surgery: A systematic review.

OBJECTIVES: Medical three-dimensional (3D) printing, the fabrication of handheld models from medical images, has the potential to become an integral part of otolaryngology-head and neck surgery (Oto-HNS) with broad impact across its subspecialties. We review the basic principles of this technology and provide a comprehensive summary of reported clinical applications in the field. METHODS: Standard bibliographic databases (MEDLINE, Embase, Cumulative Index to Nursing and Allied Health Literature, Web of Science, and The Cochrane Central Registry for Randomized Trials) were searched from their inception to May 2018 for the terms: "3D printing," "three-dimensional printing," "rapid prototyping," "additive manufacturing," "computer-aided design," "bioprinting," and "biofabrication" in various combinations with the terms: "ptolaryngology," "head and neck surgery," and "otology." Additional articles were identified from the references of retrieved articles. Only studies describing clinical applications of 3D printing were included. RESULTS: Of 5,532 records identified through database searching, 87 articles were included for qualitative synthesis. Widespread implementation of 3D printing in Oto-HNS is still at its infancy. Nonetheless, it is increasingly being utilized across all subspecialties from preoperative planning to design and fabrication of patient-specific implants and surgical guides. An emerging application considered highly valuable is its use as a teaching tool for medical education and surgical training. CONCLUSIONS: As technology and training standards evolve and as healthcare moves toward personalized medicine, 3D printing is emerging as a key technology in patient care in Oto-HNS. Treating physicians and surgeons who wish to stay abreast of these developments will benefit from a fundamental understanding of the principles and applications of this technology. Laryngoscope, 129:2045-2052, 2019.