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Objective: The aim of this study was to determine the potential of photon-counting detector computed tomography (PCD-CT) for radiation dose reduction compared with conventional energy-integrated detector CT (EID-CT) in the assessment of interstitial lung disease (ILD) in systemic sclerosis (SSc) patients., Methods: In this retrospective study, SSc patients receiving a follow-up noncontrast chest examination on a PCD-CT were included between May 2021 and December 2021. Baseline scans were generated on a dual-source EID-CT by selecting the tube current-time product for each of the 2 x-ray tubes to obtain a 100% (D 100 ), a 66% (D 66 ), and a 33% dose image (D 33 ) from the same data set. Slice thickness and kernel were adjusted between the 2 scans. Image noise was assessed by placing a fixed region of interest in the subcutaneous fat. Two independent readers rated subjective image quality (5-point Likert scale), presence, extent, diagnostic confidence, and accuracy of SSc-ILD. D 100 interpreted by a radiologist with 22 years of experience served as reference standard. Interobserver agreement was calculated with Cohen κ, and mean variables were compared by a paired t test., Results: Eighty patients (mean 56 ± 14; 64 women) were included. Although CTDI vol of PCD-CT was comparable to D 33 (0.72 vs 0.76 mGy, P = 0.091), mean image noise of PCD-CT was comparable to D 100 (131 ± 15 vs 113 ± 12, P > 0.05). Overall subjective image quality of PCD-CT was comparable to D 100 (4.72 vs 4.71; P = 0.874). Diagnostic accuracy was higher in PCD-CT compared with D 33 /D 66 (97.6% and 92.5%/96.3%, respectively) and comparable to D 100 (98.1%)., Conclusions: With PCD-CT, a radiation dose reduction of 66% compared with EID-CT is feasible, without penalty in image quality and diagnostic performance for the evaluation of ILD., Competing Interests: Conflicts of interests and sources of funding: All authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. V.M. is funded by the research grant “Young Talents in Clinical Research” of the SAMS and the G. and J. Bangerter-Rhyner Foundation., (Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.)

The aim of this study was to provide insights into myocardial adaptation over time in myocyte injury caused by acute myocarditis with preserved ejection fraction. The effect of myocardial injury, as defined by the presence of late gadolinium enhancement (LGE), on the change of left ventricular (LV) segmental strain parameters was evaluated in a longitudinal analysis. Patients with a first episode of acute myocarditis were enrolled retrospectively. Peak radial (PRS), longitudinal (PLS) and circumferential (PCS) LV segmental strain values at baseline and at follow-up were computed using feature tracking cine cardiac magnetic resonance imaging. The change of segmental strain values in LGE positive (LGE+) and LGE negative (LGE-) segments was compared over a course of 89 ± 20 days. In 24 patients, 100 LGE+ segments and 284 LGE- segments were analysed. Between LGE+ and LGE- segments, significant differences were found for the change of segmental PCS (p < 0.001) and segmental PRS (p = 0.006). LGE + segments showed an increase in contractility, indicating recovery, and LGE- segments showed a decrease in contractility, indicating normalisation after a hypercontractile state or impairment of an initially normal contracting segment. No significant difference between LGE+ and LGE- segments was found for the change in segmental PLS. In the course of acute myocarditis with preserved ejection fraction, regional myocardial function adapts inversely in segments with and without LGE. As these effects seem to counterbalance each other, global functional parameters might be of limited use in monitoring functional recovery of these patients., (© 2022. The Author(s).)

Objectives: Although cardiovascular magnetic resonance (CMR) is increasingly used to diagnose pericardial inflammation, imaging can still be challenging using conventional CMR techniques. Parametric mapping (T1/T2 mapping) techniques have emerged as novel methods to quantify focal and global changes of the myocardium without contrast agent. The aim of the present study was to implement parametric mapping to facilitate diagnostic decision-making in pericardial inflammation., Methods: Twenty patients with pericardial inflammation underwent CMR (1.5T system) including T1-weighted/T2-weighted imaging, T1/T2 mapping and late gadolinium enhancement. T1/T2 mapping was performed in end-diastole covering three short-axis slices. Diagnosis of pericardial inflammation was made according to recent guidelines. T1/T2 measurements were pursued by manually drawing regions of interest (ROIs) in the thickened, diseased pericardium carefully avoiding contamination by other cardiac structures. Parametric values were correlated to further markers of pericardial inflammation, such as pericardial thickening and inflammatory parameters., Results: On average, the pericardium displayed a thickness of 4.8±1.0 mm. Mean T1 value was 1363.0±227.1 ms and T2 value was 123.3±52.6 ms, which were above patient's myocardial values (myocardial T1: 998.7±81.0 ms, p<0.001, median 1014.46 ms; T2: 68.0±28.9 m,p<0.001) and the values of a group of four patients with chronic pericarditis (T1: 953.0±16.7 ms; T2: 63.2±10.1 ms). T1 and T2 showed a correlation to the extent of the thickened pericardium (R=0.64, p=0.002 for T1, R=0.72, p=0.005 for T2). There was no correlation of pericardial T1/T2 to blood markers of inflammation, myocardial injury (C reactive protein, troponin, creatine kinase) or further CMR parameters., Conclusions: In patients with pericardial inflammation, parametric mapping showed elevated T1 and T2 values. Parametric mapping may help to facilitate diagnosis of pericardial inflammation if conventional parameters such as pericardial hyperintensity in T1-weighted or T2-weighted imaging or contrast agent uptake are heterogeneous., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Objectives: To evaluate CT-derived radiomics for machine learning-based classification of thymic epithelial tumor (TET) stage (TNM classification), histology (WHO classification) and the presence of myasthenia gravis (MG)., Methods: Patients with histologically confirmed TET in the years 2000-2018 were retrospectively included, excluding patients with incompatible imaging or other tumors. CT scans were reformatted uniformly, gray values were normalized and discretized. Tumors were segmented manually; 15 scans were re-segmented after 2 weeks by two readers. 1316 radiomic features were calculated (pyRadiomics). Features with low intra-/inter-reader agreement (ICC<0.75) were excluded. Repeated nested cross-validation was used for feature selection (Boruta algorithm), model training, and evaluation (out-of-fold predictions). Shapley additive explanation (SHAP) values were calculated to assess feature importance., Results: 105 patients undergoing surgery for TET were identified. After applying exclusion criteria, 62 patients (28 female; mean age, 57±14 years; range, 22-82 years) with 34 low-risk TET (LRT; WHO types A/AB/B1), 28 high-risk TET (HRT; WHO B2/B3/C) in early stage (49, TNM stage I-II) or advanced stage (13, TNM III-IV) were included. 14(23%) of the patients had MG. 334(25%) features were excluded after intra-/inter-reader analysis. Discriminatory performance of the random forest classifiers was good for histology(AUC, 87.6%; 95% confidence interval, 76.3-94.3) and TNM stage(AUC, 83.8%; 95%CI, 66.9-93.4) but poor for the prediction of MG (AUC, 63.9%; 95%CI, 44.8-79.5)., Conclusions: CT-derived radiomic features may be a useful imaging biomarker for TET histology and TNM stage., Competing Interests: The authors have declared that no competing interests exist.

Objectives: To evaluate whether a magnetic resonance imaging (MRI) radiomics-based machine learning classifier can predict postoperative pancreatic fistula (POPF) after pancreaticoduodenectomy (PD) and to compare its performance to T1 signal intensity ratio (T1 SIratio)., Methods: Sixty-two patients who underwent 3 T MRI before PD between 2008 and 2018 were retrospectively analyzed. POPF was graded and split into clinically relevant POPF (CR-POPF) vs. biochemical leak or no POPF. On T1- and T2-weighted images, 2 regions of interest were placed in the pancreatic corpus and cauda. 173 radiomics features were extracted using pyRadiomics. Additionally, the pancreas-to-muscle T1 SIratio was measured. The dataset was augmented and split into training (70 %) and test sets (30 %). A Boruta algorithm was used for feature reduction. For prediction of CR-POPF models were built using a gradient-boosted tree (GBT) and logistic regression from the radiomics features, T1 SIratio and a combination of the two. Diagnostic accuracy of the models was compared using areas under the receiver operating characteristics curve (AUCs)., Results: Five most important radiomics features were identified for prediction of CR-POPF. A GBT using these features achieved an AUC of 0.82 (95 % Confidence Interval [CI]: 0.74 - 0.89) when applied on the original (non-augmented) dataset. Using T1 SIratio, a GBT model resulted in an AUC of 0.75 (CI: 0.63 - 0.84) and a logistic regression model delivered an AUC of 0.75 (CI: 0.63 - 0.84). A GBT model combining radiomics features and T1 SIratio resulted in an AUC of 0.90 (CI 0.84 - 0.95)., Conclusion: MRI-radiomics with routine sequences provides promising prediction of CR-POPF., (Copyright © 2021 Elsevier B.V. All rights reserved.)

We compared a fast, single breath-hold three dimensional LGE sequence (3D LGE) with an established two dimensional multi breath-hold sequence (2D LGE) and evaluated image quality and the amount of myocardial fibrosis in patients with acute and chronic myocarditis. 3D LGE and 2D LGE (both spatial resolution 1.5 × 1.5 mm 2 , slice-thickness 8 mm, field of view 350 × 350 mm 2 ) were acquired in 25 patients with acute myocarditis (mean age 40 ± 18 years, 7 female) and 27 patients with chronic myocarditis (mean age 44 ± 22 years, 9 female) on a 1.5 T MR system. Image quality was evaluated by two independent, blinded readers using a 5-point Likert scale. Total myocardial mass, fibrotic mass and total fibrotic tissue percentage were quantified for both sequences in both groups. There was no significant difference in image quality between 3D und 2D acquisitions in patients with acute (p = 0.8) and chronic (p = 0.5) myocarditis. No significant differences between 3D and 2D acquisitions could be shown for myocardial mass (acute p = 0.2; chronic p = 0.3), fibrous tissue mass (acute p = 0.7; chronic p = 0.1) and total fibrous percentage (acute p = 0.4 and chronic p = 0.2). Inter-observer agreement was substantial to almost perfect. Acquisition time was significantly shorter for 3D LGE (24 ± 5 s) as compared to 2D LGE (350 ± 58 s, p < 0.001). In patients with acute and chronic myocarditis 3D LGE imaging shows equal diagnostic quality compared to standard 2D LGE imaging but with significantly reduced acquisition time.

Cardiac magnetic resonance (CMR) imaging has become an important technique for non-invasive diagnosis of takotsubo syndrome (TTS). The long-term prognostic value of CMR imaging in TTS has not been fully elucidated yet. This study sought to evaluate the prognostic value of texture analysis (TA) based on CMR images in patients with TTS using machine learning. In this multicenter study (InterTAK Registry), we investigated CMR imaging data of 58 patients (56 women, mean age 68 ± 12 years) with TTS. CMR imaging was performed in the acute to subacute phase (median time after symptom onset 4 days) of TTS. TA of the left ventricle was performed using free-hand regions-of-interest in short axis late gadolinium-enhanced and on T2-weighted (T2w) images. A total of 608 TA features adding the parameters age, gender, and body mass index were included. Dimension reduction was performed removing TA features with poor intra-class correlation coefficients (ICC ≤ 0.6) and those being redundant (correlation matrix with Pearson correlation coefficient r > 0.8). Five common machine-learning classifiers (artificial neural network Multilayer Perceptron, decision tree J48, NaïveBayes, RandomForest, and Sequential Minimal Optimization) with tenfold cross-validation were applied to assess 5-year outcome including major adverse cardiac and cerebrovascular events (MACCE). Dimension reduction yielded 10 TA features carrying prognostic information, which were all based on T2w images. The NaïveBayes machine learning classifier showed overall best performance with a sensitivity of 82.9% (confidence interval (CI) 80-86.2), specificity of 83.7% (CI 75.7-92), and an area-under-the receiver operating characteristics curve of 0.88 (CI 0.83-0.92). This proof-of-principle study is the first to identify unique T2w-derived TA features that predict long-term outcome in patients with TTS. These features might serve as imaging prognostic biomarkers in TTS patients.

Purpose of Review: The aim of this structured review is to summarize the current research applications and opportunities arising from artificial intelligence (AI) and texture analysis with regard to cardiac imaging., Recent Findings: Current research findings suggest tremendous potential for AI in cardiac imaging, especially with regard to objective image analyses, overcoming the limitations of an observer-dependent subjective image interpretation. Researchers have used this technique across multiple imaging modalities, for instance to detect myocardial scars in cardiac MR imaging, to predict contrast enhancement in non-contrast studies, and to improve image acquisition and reconstruction. AI in medical imaging has the potential to provide novel, much-needed applications for improving patient care pertaining to the cardiovascular system. While several shortcomings are still present in the current methodology, AI may serve as a resourceful assistant to radiologists and clinicians alike.

Background: Computed tomography (CT)-derived fractional flow reserve (FFR CT ) enables the non-invasive functional assessment of coronary artery stenosis. We evaluated the feasibility and potential clinical role of FFR CT in patients presenting to the emergency department with acute chest pain who underwent chest-pain CT (CPCT)., Methods: For this retrospective IRB-approved study, we included 56 patients (median age: 62 years, 14 females) with acute chest pain who underwent CPCT and who had at least a mild (≥25% diameter) coronary artery stenosis. CPCT was evaluated for the presence of acute plaque rupture and vulnerable plaque features. FFR CT measurements were performed using a machine learning-based software. We assessed the agreement between the results from FFR CT and patient outcome (including results from invasive catheter angiography and from any non-invasive cardiac imaging test, final clinical diagnosis and revascularization) for a follow-up of 3 months., Results: FFR CT was technically feasible in 38/56 patients (68%). Eleven of the 38 patients (29%) showed acute plaque rupture in CPCT; all of them underwent immediate coronary revascularization. Of the remaining 27 patients (71%), 16 patients showed vulnerable plaque features (59%), of whom 11 (69%) were diagnosed with acute coronary syndrome (ACS) and 10 (63%) underwent coronary revascularization. In patients with vulnerable plaque features in CPCT, FFRCT had an agreement with outcome in 12/16 patients (75%). In patients without vulnerable plaque features (n=11), one patient showed myocardial ischemia (9%). In these patients, FFR CT and patient outcome showed an agreement in 10/11 patients (91%)., Conclusions: Our preliminary data show that FFR CT is feasible in patients with acute chest pain who undergo CPCT provided that image quality is sufficient. FFR CT has the potential to improve patient triage by reducing further downstream testing but appears of limited value in patients with CT signs of acute plaque rupture., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at http://dx.doi.org/10.21037/cdt-20-381). The authors have no conflicts of interest to declare., (2020 Cardiovascular Diagnosis and Therapy. All rights reserved.)

Aims: Late gadolinium enhancement (LGE) visualizes scar tissue after myocardial infarction. However, in clinically used LGE sequences, subendocardial infarcts can be missed due to low contrast between blood pool and subendocardium. The purpose of his study was to compare scar visibility in a novel 3-dimensional (3D) single breath-hold inversion recovery sequence with fixed, short inversion time (TI = 100 milliseconds) (short LGE) and standard 3D LGE imaging with individually adjusted TI (LGE)., Methods: Short LGE and LGE (both sequences with the same settings: spatial resolution, 1.2 × 1.2 mm; slice thickness, 8 mm; field of view, 350 × 350 mm; single breath-hold) were acquired in 64 patients with previous MI (13 female; mean age, 57 ± 19 years) at 1.5 T. Inversion time was set to 100 milliseconds in short LGE and adjusted individually in LGE according to the Look-Locker sequence. Two independent readers evaluated 1088 segments (17-segment model), identified infarcted segments, and categorized scar visibility (5 = excellent, 1 = poor scar visibility) and scar transmurality (4 = transmural, 0 = no scar) using a 5-point Likert scale. Signal intensity ratios between short LGE and LGE for scar and blood pool, for scar and remote myocardium, and for remote myocardium and blood pool were calculated., Results: Short LGE showed 197 infarcted segments out of 1088 (18.1%); LGE revealed 191 segments (17.6%). Short LGE with dark scar and bright blood pool demonstrated better overall scar visibility, especially in subendocardially infarcted segments compared with LGE (4.2 vs 3.0, 5 = excellent visibility; P = 0.01). Signal intensity ratios for short LGE relative to LGE were 1.42 for scar/blood pool, 0.8 for scar/remote myocardium, and 0.22 for remote myocardium/blood.Overall transmurality was not rated higher in short LGE compared with LGE (P = 0.8). More fibrous tissue and total fibrous percentage (P = 0.04) were measured in short LGE compared with LGE, whereas myocardial mass was not significantly different (P = 0.5). Acquisition time was similar between short LGE and LGE (26 ± 4 seconds vs 25 ± 9 seconds, P = 0.7)., Conclusions: Short LGE is a fast, single breath-hold 3D LGE sequence with no need for myocardial nulling due to fixed inversion time with improved scar visibility, especially in subendocardial infarcts.

Purpose: To allow for comprehensive noninvasive diagnostics of coronary artery disease (CAD) by using three-dimensional (3D) image fusion of CT coronary angiography, CT-derived fractional flow reserve (CT FFR), whole-heart dynamic 3D cardiac MRI perfusion, and 3D cardiac MRI late gadolinium enhancement (LGE)., Materials and Methods: Seventeen patients (54 years ± 10 [standard deviation], one female) who underwent cardiac CT and cardiac MRI were included (combined subcohort of three prospective trials). Software facilitating multimodal 3D image fusion was developed. Postprocessing of CT data included segmentation of the coronary tree and heart contours, calculation of CT FFR values, and color coding of the coronary tree according to CT FFR. Postprocessing of cardiac MRI data included segmentation of the left ventricle (LV) in cardiac MRI perfusion and cardiac MRI LGE, co-registration of cardiac MRI to CT data, and projection of cardiac MRI perfusion and LGE values onto the high spatial resolution LV from CT., Results: Image quality was rated as good to excellent (scores: 2.5-2.6; 3 = excellent). CT coronary angiography revealed significant stenoses in seven of 17 cases (41%). CT FFR was possible in 16 of 17 cases (94%) and showed pathologic flow in seven of 17 cases (41%), six of which coincided with cases revealing significant stenoses at CT coronary angiography. Cardiac MRI perfusion identified eight of 17 patients (47%) with hypoperfusion (ischemic burden of 17% ± 5). Cardiac MRI LGE showed myocardial scar in three of 17 cases (18%, scar burden of 7% ± 4). Conventional two-dimensional readout of CT coronary angiography and cardiac MRI resulted in eight of 17 cases (47%) with uncertain findings. Most of these divergent findings could be solved when adding information from CT FFR and 3D image fusion (six of eight, 75%)., Conclusion: Multimodal 3D cardiac image fusion is feasible and may help with comprehensive noninvasive CAD diagnostics. Supplemental material is available for this article. © RSNA, 2020., Competing Interests: Disclosures of Conflicts of Interest: J.v.S. disclosed no relevant relationships. M.M. disclosed no relevant relationships. H.M. disclosed no relevant relationships. C.S. Activities related to the present article: disclosed no relevant relationships. Activities not related to the present article: is employed by and holds stock/stock options in Siemens Healthcare. Other relationships: has patents (pending and issued) with Siemens Healthcare. S.K. disclosed no relevant relationships. F.R. Activities related to the present article: disclosed no relevant relationships. Activities not related to the present article: is a board member of European Society of Cardiology; is a consultant for Amgen, Fresenius, CITI Research, and Vifor; has grants/grants pending with Abbott/St. Jude Medical, Amgen, Bayer, Novartis, Servier, and Swiss National Foundation; received payment for lectures including service on speakers bureaus from Abbott, AstraZeneca, Boehringer Ingelheim, Hôpitaux Universitaires des Genève/GECORE, Luzerner Kantonsspital, CCE Services (Boston Scientific), Medtronic, Medscape, Novartis, Roche, Ruwag, Sanofi-Aventis, Servier, and Swiss Heart Failure Academy (USZ). Other relationships: disclosed no relevant relationships. H.A. disclosed no relevant relationships. R.M. disclosed no relevant relationships., (2020 by the Radiological Society of North America, Inc.)

Background: Hypertrophic cardiomyopathy (HCM) is associated with an increased risk of adverse cardiac events. Beyond classic risk factors, relative myocardial ischaemia and succeeding myocardial alterations, which can be detected using either contrast agents or parametric mapping in cardiovascular magnetic resonance (CMR) imaging, have shown an impact on outcome in HCM. CMR may help to risk stratify using parametric T2* mapping. Therefore, the aim of the present study was to evaluate the association of T2* values or fibrosis with cardiovascular events in HCM., Methods: The relationship between T2* with supraventricular, ventricular arrhythmia or heart failure was retrospectively assessed in 91 patients with HCM referred for CMR on a 1.5T MR imaging system. Fibrosis as a reference was added to the model. Patients were subdivided into groups according to T2* value quartiles., Results: 47 patients experienced an event of ventricular arrhythmia, 25 of atrial fibrillation/flutter and 17 of heart failure. T2*≤28.7 ms yielded no association with ventricular events in the whole HCM cohort. T2* of non-obstructive HCM showed a significant association with ventricular events in univariate analysis, but not in multivariate analysis. For the combined endpoint of arrhythmic events, there was already an association for the whole HCM cohort, but again only in univariate analyses. Fibrosis stayed the strongest predictor in all analyses. There was no association for T2* and fibrosis with heart failure., Conclusions: Decreased T2* values by CMR only provide a small association with arrhythmic events in HCM, especially in non-obstructive HCM. No information is added for heart failure., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Objective: The aim of this study was to compare bone imaging between ultrashort echo-time (UTE) magnetic resonance (MR) imaging and cone-beam computed tomography (CBCT) as the reference standard in patients with medication-related osteonecrosis of the jaw (MRONJ)., Materials and Methods: A 1-year retrospective, blinded, and randomized qualitative analysis of UTE MR images and CBCT from 19 patients with clinically diagnosed MRONJ was performed by 2 independent radiologists. Medication-related osteonecrosis of the jaw imaging hallmarks such as osteolysis, periosteal thickening, and medullary osteosclerosis were rated visually (0 and 1 to 3 for normal and mild to severe changes) for defined anatomic regions of the jaw. In addition, segmentation of these regions was performed on coregistered MR/CBCT images for the following quantitative comparison of signal intensity (SI) on MR and gray values (GVs) on CBCT images. Interreader/modality agreement (Cohen kappa), standard testing for significant differences of (non)parametric values, and Pearson correlation of signal intensity/GV were used for statistical analysis., Results: The anterior corpus of the mandible was most often affected by MRONJ (P < 0.001). Overall, interreader agreement of qualitative MRONJ hallmark scores was almost perfect (κ = 0.81) and without significant differences between modalities (κ = 0.81 vs 0.82, CBCT vs MR, respectively). Intermodality agreement for qualitative gradings was substantial for both readers (κ = 0.77 and 0.70). Signal intensity/GV in MRONJ-affected areas differed significantly from healthy bone (P < 0.001) as well as correlation significantly between modalities (r = -0.77; P < 0.001)., Conclusions: Qualitative assessment of MRONJ with radiation-free UTE MR imaging is comparable to reference standard CBCT. Quantitative measurements of both modalities significantly distinguish diseased from normal bone with strong correlations among the quantitative values in both modalities.

Background: Postoperative pancreatic fistula remains an unsolved challenge after pancreatoduodenectomy. Important in this regard is the presence of a soft pancreatic texture which is a major risk factor. Advances in machine learning and texture analysis of medical images allow identification of features of parenchyma that are invisible to the human eye. The aim of this study was to investigate the potential of machine learning to predict postoperative pancreatic fistula based on preoperative, non-contrast-enhanced computed tomography., Methods: We screened a prospectively assessed database including all patients undergoing pancreatoduodenectomy at a tertiary center from 2008 until 2018 for patients based on the occurrence of postoperative pancreatic fistula. In total, 110 patients were included, consisting of 55 patients who developed a postoperative pancreatic fistula and 55 without postoperative pancreatic fistula. For machine learning-based texture analysis preoperative, non-contrast-enhanced computed tomography axial images were used. Machine learning results were tested using 10-fold cross validation. Previously validated clinical fistula risk scores (original and alternative fistula risk scores) served as reference tests., Results: Both the original and the alternative fistula risk scores showed good discrimination between patients without and with postoperative pancreatic fistula (area under the curve 0.76 and 0.72, respectively). Machine learning-based texture analysis showed potential to detect histologic fibrosis (area under the curve 0.84, sensitivity 75%; specificity 92%), histologic lipomatosis (area under the curve 0.82, sensitivity 78%; specificity 89%), and intraoperative pancreatic hardness (area under the curve 0.70, sensitivity 78%; specificity 74%). The features of the machine learning-based texture analysis were most accurate in predicting the occurrence of postoperative pancreatic fistula (area under the curve 0.95, sensitivity of 96%; specificity 98%) after pancreatoduodenectomy., Conclusion: This proof-of-principle study suggests the ability of machine learning in recognizing important features of pancreatic texture associated with an increased risk of postoperative pancreatic fistula based on preoperative computed tomography., (Copyright © 2019 Elsevier Inc. All rights reserved.)

The new 2019 European Society of Cardiology guidelines for the diagnosis and management of chronic coronary syndromes emphasize the role of noninvasive functional imaging of myocardial ischemia in diagnosing coronary artery disease to guide decision making regarding revascularization. Cardiac magnetic resonance imaging (CMR) stands out relative to other imaging modalities given its high safety profile, absence of ionizing radiation, and its versatility in encoding various image contrasts. It also allows an assessment of myocardial function, ischemia, and viability as well as permits tissue characterization including detection of edema in a single examination. In recent years, a number of meta‑analyses and studies considering the role of CMR for detecting ischemia have been published. The recent multicenter randomized MR‑INFORM trial has demonstrated the clinical utility of CMR in patients with stable angina and cardiovascular risk factors. This landmark study has proved that a perfusion CMR‑based strategy leads to a lower number of revascularizations while being noninferior to an invasive coronary angiography with fractional flow reserve-guided therapy in terms of major adverse cardiac events at 1 year. In light of recent and future technical improvements, CMR will become increasingly important in the assessment of myocardial ischemia in patients with chronic coronary syndromes.

Purpose: To evaluate whether radiomics features of late gadolinium enhancement (LGE) regions at cardiac MRI enable distinction between myocardial infarction (MI) and myocarditis and to compare radiomics with subjective visual analyses by readers with different experience levels., Materials and Methods: In this retrospective, institutional review board-approved study, consecutive MRI examinations of 111 patients with MI and 62 patients with myocarditis showing LGE were included. By using open-source software, classification performances attained from two-dimensional (2D) and three-dimensional (3D) texture analysis, shape, and first-order descriptors were compared, applying five different machine learning algorithms. A nested, stratified 10-fold cross-validation was performed. Classification performances were compared through Wilcoxon signed-rank tests. Supervised and unsupervised feature selection techniques were tested; the effect of resampling MR images was analyzed. Subjective image analysis was performed on 2D and 3D image sets by two independent, blinded readers with different experience levels., Results: When trained with recursive feature elimination (RFE), a support vector machine achieved the best results (accuracy: 88%) for 2D features, whereas linear discriminant analysis (LDA) showed the highest accuracy (85%) for 3D features ( P <.05). When trained with principal component analysis (PCA), LDA attained the highest accuracy with both 2D (86%) and 3D (89%; P =.4) features. Results found for classifiers trained with spline resampling were less accurate than those achieved with one-dimensional (1D) nearest-neighbor interpolation ( P <.05), whereas results for classifiers trained with 1D nearest-neighbor interpolation and without resampling were similar ( P =.1). As compared with the radiomics approach, subjective visual analysis performance was lower for the less experienced and higher for the experienced reader for both 2D and 3D data., Conclusion: Radiomics features of LGE permit the distinction between MI and myocarditis with high accuracy by using either 2D features and RFE or 3D features and PCA.© RSNA, 2019 Supplemental material is available for this article., Competing Interests: Disclosures of Conflicts of Interest: T.D.N. Activities related to the present article: author received grant from European School of Radiology (research fellowship organized by ESOR in many European hospitals). Activities not related to the present article: disclosed no relevant relationships. Other relationships: disclosed no relevant relationships. J.v.S. disclosed no relevant relationships. M.M. disclosed no relevant relationships. E.G. disclosed no relevant relationships. P.S. disclosed no relevant relationships. R.M. disclosed no relevant relationships. H.A. disclosed no relevant relationships., (2019 by the Radiological Society of North America, Inc.)

Right ventricular systolic dysfunction is prognostic in various cardiovascular diseases. Right ventricular systolic function is not commonly assessed in the catheterization laboratory. Therefore, we developed a novel, reproducible method to measure right ventricular systolic function during selective coronary angiography. We analyzed the angiographic systolic translational motion and maximum speed of the right coronary artery (RCA) in 97 consecutive patients and compared it to the tricuspid annular plane systolic excursion (TAPSE) as measured by echocardiography. All measurements were performed by two independent operators on two occasions. Inter-observer variability and intra-observer variability were excellent for RCA motion distance and for RCA maximum speed. There was a significant correlation of the RCA motion distance and RCA maximum speed with the TAPSE measured by echocardiography (Pearson's correlation for RCA distance: r = 0.59, p < 0.001, r 2  = 0.35; for RCA speed: r = 0.40, p < 0.001, r 2  = 0.16). The area under the receiver operating curve for the RCA motion distance was 0.88 (95% CI 0.80-0.96) for discrimination of normal and abnormal right ventricular systolic function. A cut-off value less than 22.3 mm systolic RCA motion had a specificity of 93.3% and a sensitivity of 75.6% for identifying an abnormal right ventricular systolic function. Analysis of the RCA motion is a reproducible and reliable method to measure right ventricular systolic function during selective coronary angiography. It is a simple and useful tool to assess right ventricular function in the catheterization laboratory and may serve for risk assessment for right ventricular failure. CLINICAL TRIAL REGISTRATION: Data for this study was collected retrospectively from Swiss Transcatheter Aortic Valve Implantation Registry (NCT01368250). https://clinicaltrials.gov/show/NCT01368250 .

Objectives: To compare texture analysis (TA) with subjective visual diagnosis of myocardial infarction (MI) in cardiac computed tomography (CT) and to evaluate the impact of iterative reconstruction (IR)., Methods: Ten patients (4 women, mean age 68 ± 11 years) with confirmed chronic MI and 20 controls (8 women, mean age 52 ± 11 years) with no cardiac abnormality underwent contrast-enhanced cardiac CT with the same protocol. Images were reconstructed with filtered back projection (FBP) and with advanced modeled IR at strength levels 3-5. Subjective diagnosis of MI was made by three independent, blinded readers with different experience levels. Classification of MI was performed using machine learning-based decision tree models for the entire data set and after splitting into training and test data to avoid overfitting., Results: Subjective visual analysis for diagnosis of MI showed excellent intrareader (kappa: 0.93) but poor interreader agreement (kappa: 0.3), with variable performance at different image reconstructions. TA showed high performance for all image reconstructions (correct classifications: 94%-97%, areas under the curve: 0.94-0.99). After splitting into training and test data, overall lower performances were observed, with best results for IR at level 5 (correct classifications: 73%, area under the curve: 0.65)., Conclusions: As compared with subjective, nonreliable visual analysis of inexperienced readers, TA enables objective and reproducible diagnosis of chronic MI in cardiac CT with higher accuracy. IR has a considerable impact on both subjective and objective image analysis., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Purpose: Hypertrophic cardiomyopathy (HCM) is characterized by a heterogeneous morphology and variable prognosis. A mismatch between left ventricular mass (LVM) and microvascular circulation with corresponding relative ischemia has been implicated to cause myocardial replacement fibrosis that deteriorates prognosis. Besides parametric T1 mapping, Cardiovascular Magnetic Resonance (CMR) T2* mapping is able to identify ischemia as well as fibrosis in cardiac and extracardiac diseases. Therefore, we aimed to investigate the value of T2* mapping to characterize structural alterations in patients with HCM., Methods: CMR was performed on a 1.5 T MR imaging system (Achieva, Philips, Best, Netherlands) using a 5-channel coil in patients with HCM (n = 103, 50.6 ± 16.4 years) and in age- and gender-matched controls (n = 20, 44.8 ± 16.9 years). T2* mapping (1 midventricular short axis slice) was acquired in addition to late gadolinium enhancement (LGE). T2* values were compared between patients with HCM and controls as well as between HCM patients with- and without fibrosis., Results: HCM patients showed significantly decreased T2* values compared to controls (26.2 ± 4.6 vs. 31.3 ± 4.3 ms, p < 0.001). Especially patients with myocardial fibrosis presented with decreased T2* values in comparison to those without fibrosis (25.2 ± 4.0 vs. 28.7 ± 5.3 ms, p = 0.003). A regression model including maximum wall thickness, LVM and T2* values provided good overall diagnostic accuracy of 80% to diagnose HCM with and without fibrosis., Conclusion: In this study, parametric mapping identified lower T2* values in HCM patients compared to controls, especially in a sub-group of patients with myocardial fibrosis. As myocardial fibrosis has been suggested to influence prognosis of patients with HCM, T2* mapping may add information for identifying a higher risk sub-group of HCM patients.

Background: Since patients with myocardial hypoperfusion due to coronary artery disease (CAD) with preserved viability are known to benefit from revascularization, accurate differentiation of hypoperfusion from scar is desirable., Purpose: To develop a framework for 3D fusion of whole-heart dynamic cardiac MR perfusion and late gadolinium enhancement (LGE) to delineate stress-induced myocardial hypoperfusion and scar., Study Type: Prospective feasibility study., Subjects: Sixteen patients (61 ± 14 years, two females) with known/suspected CAD., Field Strength/sequence: 1.5T (nine patients); 3.0T (seven patients); whole-heart dynamic 3D cardiac MR perfusion (3D-PERF, under adenosine stress); 3D LGE inversion recovery sequences (3D-SCAR)., Assessment: A software framework was developed for 3D fusion of 3D-PERF and 3D-SCAR. Computation steps included: 1) segmentation of the left ventricle in 3D-PERF and 3D-SCAR; 2) semiautomatic thresholding of perfusion/scar data; 3) automatic calculation of ischemic/scar burden (ie, pathologic relative to total myocardium); 4) projection of perfusion/scar values onto artificial template of the left ventricle; 5) semiautomatic coregistration to an exemplary heart contour easing 3D orientation; and 6) 3D rendering of the combined datasets using automatically defined color tables. All tasks were performed by two independent, blinded readers (J.S. and R.M.)., Statistical Tests: Intraclass correlation coefficients (ICC) for determining interreader agreement., Results: Image acquisition, postprocessing, and 3D fusion were feasible in all cases. In all, 10/16 patients showed stress-induced hypoperfusion in 3D-PERF; 8/16 patients showed LGE in 3D-SCAR. For 3D-PERF, semiautomatic thresholding was possible in all patients. For 3D-SCAR, automatic thresholding was feasible where applicable. Average ischemic burden was 11 ± 7% (J.S.) and 12 ± 7% (R.M.). Average scar burden was 8 ± 5% (J.S.) and 7 ± 4% (R.M.). Interreader agreement was excellent (ICC for 3D-PERF = 0.993, for 3D-SCAR = 0.99)., Data Conclusion: 3D fusion of 3D-PERF and 3D-SCAR facilitates intuitive delineation of stress-induced myocardial hypoperfusion and scar., Level of Evidence: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;48:1129-1138., (© 2018 International Society for Magnetic Resonance in Medicine.)

Purpose: We sought to determine the predictive value of 3-dimensional texture analysis of computerized tomography images for successful shock wave lithotripsy in patients with kidney stones., Materials and Methods: Patients with preoperative and postoperative computerized tomography, previously untreated kidney stones and a stone diameter of 5 to 20 mm were included in study. A total of 224, 3-dimensional texture analysis features of each kidney stone, including attenuation measured in HU and the clinical variables body mass index, initial stone size and skin to stone distance, were analyzed using 5 commonly used machine learning models. The data set was split in a ratio of 2/3 for model derivation and 1/3 for validation. Machine learning based predictions of shock wave lithotripsy success in the validation cohort were evaluated by calculating sensitivity, specificity and the AUC., Results: For shock wave lithotripsy success the 3 clinical variables body mass index, initial stone size and skin to stone distance showed an AUC of 0.68, 0.58 and 0.63, respectively. No predictive value was found for HU. A random forest classifier using 3, 3-dimensional texture analysis features had an AUC of 0.79. By combining these 3 features with clinical variables discriminatory accuracy improved further with an AUC of 0.85 for 3-dimensional texture analysis features and skin to stone distance, an AUC of 0.8 for 3-dimensional texture analysis features and body mass index, and an AUC of 0.81 for 3-dimensional texture analysis and stone size., Conclusions: This preliminary study indicates that the clinical variables body mass index, initial stone size and skin to stone distance show limited value to predict shock wave lithotripsy success while stone HU values were not predictive. Select 3-dimensional texture analysis features identified by machine learning provided incremental accuracy to predict the success of shock wave lithotripsy., (Copyright © 2018 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.)

Objectives: We aimed to assess the diagnostic performance of a combined protocol with coronary computed tomography angiography (CCTA) and stress CT perfusion imaging (CTP) in heart transplant patients for comprehensive morphological and functional imaging., Methods: In this prospective study, 13 patients undergoing routine follow-up 8±6 years after heart transplantation underwent CCTA and dynamic adenosine stress CTP using a third-generation dual-source CT scanner, cardiac magnetic resonance (MR) adenosine stress perfusion imaging at 1.5 T, and catheter coronary angiography. In CCTA stenoses >50% luminal diameter narrowing were noted. Myocardial perfusion deficits were documented in CTP and MR. Quantitative myocardial blood flow (MBF) was calculated with CTP. Left ventricular ejection fraction was determined on cardiac MR cine images. Radiation doses of CT were determined., Results: One of the 13 patients had to be excluded because of severe motion artifacts. CCTA identified three patients with stenosis >50%, which were confirmed with catheter coronary angiography. CTP showed four patients with stress-induced myocardial hypoperfusion, which were confirmed by MR stress perfusion imaging. Quantitative analysis of global MBF showed lower mean values as compared to known reference values (MBF under stress 125.5 ± 34.5 ml/100 ml/min). Average left ventricular ejection fraction was preserved (56 ± 5%)., Conclusions: In heart transplant patients, a comprehensive CT protocol for the assessment of morphology and function including CCTA and CTP showed good concordance to results from MR perfusion imaging and catheter coronary angiography., Key Points: • Stress CT perfusion imaging enables the detection of myocardial ischemia • CT myocardial perfusion imaging can be combined with coronary computed tomography angiography • Combining perfusion and coronary CT imaging is accurate in heart transplant patients • CT myocardial perfusion imaging can be performed at a reasonable radiation dose.

Objectives: The aims of this study were to assess the value of a dedicated sharp convolution kernel for photon counting detector (PCD) computed tomography (CT) for coronary stent imaging and to evaluate to which extent iterative reconstructions can compensate for potential increases in image noise., Materials and Methods: For this in vitro study, a phantom simulating coronary artery stenting was prepared. Eighteen different coronary stents were expanded in plastic tubes of 3 mm diameter. Tubes were filled with diluted contrast agent, sealed, and immersed in oil calibrated to an attenuation of -100 HU simulating epicardial fat. The phantom was scanned in a modified second generation 128-slice dual-source CT scanner (SOMATOM Definition Flash, Siemens Healthcare, Erlangen, Germany) equipped with both a conventional energy integrating detector and PCD. Image data were acquired using the PCD part of the scanner with 48 × 0.25 mm slices, a tube voltage of 100 kVp, and tube current-time product of 100 mAs. Images were reconstructed using a conventional convolution kernel for stent imaging with filtered back-projection (B46) and with sinogram-affirmed iterative reconstruction (SAFIRE) at level 3 (I463). For comparison, a dedicated sharp convolution kernel with filtered back-projection (D70) and SAFIRE level 3 (Q703) and level 5 (Q705) was used. The D70 and Q70 kernels were specifically designed for coronary stent imaging with PCD CT by optimizing the image modulation transfer function and the separation of contrast edges. Two independent, blinded readers evaluated subjective image quality (Likert scale 0-3, where 3 = excellent), in-stent diameter difference, in-stent attenuation difference, mathematically defined image sharpness, and noise of each reconstruction. Interreader reliability was calculated using Goodman and Kruskal's γ and intraclass correlation coefficients (ICCs). Differences in image quality were evaluated using a Wilcoxon signed-rank test. Differences in in-stent diameter difference, in-stent attenuation difference, image sharpness, and image noise were tested using a paired-sample t test corrected for multiple comparisons., Results: Interreader and intrareader reliability were excellent (γ = 0.953, ICCs = 0.891-0.999, and γ = 0.996, ICCs = 0.918-0.999, respectively). Reconstructions using the dedicated sharp convolution kernel yielded significantly better results regarding image quality (B46: 0.4 ± 0.5 vs D70: 2.9 ± 0.3; P < 0.001), in-stent diameter difference (1.5 ± 0.3 vs 1.0 ± 0.3 mm; P < 0.001), and image sharpness (728 ± 246 vs 2069 ± 411 CT numbers/voxel; P < 0.001). Regarding in-stent attenuation difference, no significant difference was observed between the 2 kernels (151 ± 76 vs 158 ± 92 CT numbers; P = 0.627). Noise was significantly higher in all sharp convolution kernel images but was reduced by 41% and 59% by applying SAFIRE levels 3 and 5, respectively (B46: 16 ± 1, D70: 111 ± 3, Q703: 65 ± 2, Q705: 46 ± 2 CT numbers; P < 0.001 for all comparisons)., Conclusions: A dedicated sharp convolution kernel for PCD CT imaging of coronary stents yields superior qualitative and quantitative image characteristics compared with conventional reconstruction kernels. Resulting higher noise levels in sharp kernel PCD imaging can be partially compensated with iterative image reconstruction techniques.

Objectives: The aim of this study was to test whether texture analysis and machine learning enable the detection of myocardial infarction (MI) on non-contrast-enhanced low radiation dose cardiac computed tomography (CCT) images., Materials and Methods: In this institutional review board-approved retrospective study, we included non-contrast-enhanced electrocardiography-gated low radiation dose CCT image data (effective dose, 0.5 mSv) acquired for the purpose of calcium scoring of 27 patients with acute MI (9 female patients; mean age, 60 ± 12 years), 30 patients with chronic MI (8 female patients; mean age, 68 ± 13 years), and in 30 subjects (9 female patients; mean age, 44 ± 6 years) without cardiac abnormality, hereafter termed controls. Texture analysis of the left ventricle was performed using free-hand regions of interest, and texture features were classified twice (Model I: controls versus acute MI versus chronic MI; Model II: controls versus acute and chronic MI). For both classifications, 6 commonly used machine learning classifiers were used: decision tree C4.5 (J48), k-nearest neighbors, locally weighted learning, RandomForest, sequential minimal optimization, and an artificial neural network employing deep learning. In addition, 2 blinded, independent readers visually assessed noncontrast CCT images for the presence or absence of MI., Results: In Model I, best classification results were obtained using the k-nearest neighbors classifier (sensitivity, 69%; specificity, 85%; false-positive rate, 0.15). In Model II, the best classification results were found with the locally weighted learning classification (sensitivity, 86%; specificity, 81%; false-positive rate, 0.19) with an area under the curve from receiver operating characteristics analysis of 0.78. In comparison, both readers were not able to identify MI in any of the noncontrast, low radiation dose CCT images., Conclusions: This study indicates the ability of texture analysis and machine learning in detecting MI on noncontrast low radiation dose CCT images being not visible for the radiologists' eye.

Objective: To apply texture analysis (TA) in computed tomography (CT) of urinary stones and to correlate TA findings with the number of required shockwaves for successful shock wave lithotripsy (SWL)., Materials and Methods: CT was performed on thirty-four urinary stones in an in vitro setting. Urinary stones underwent SWL and the number of required shockwaves for disintegration was recorded. TA was performed after post-processing for pixel spacing and image normalization. Feature selection and dimension reduction were performed according to inter- and intrareader reproducibility and by evaluating the predictive ability of the number of shock waves with the degree of redundancy between TA features. Three regression models were tested: (1) linear regression with elimination of colinear attributes (2), sequential minimal optimization regression (SMOreg) employing machine learning, and (3) simple linear regression model of a single TA feature with lowest squared error., Results: Highest correlations with the absolute number of required SWL shockwaves were found for the linear regression model (r = 0.55, p = 0.005) using two weighted TA features: Histogram 10th Percentile, and Gray-Level Co-Occurrence Matrix (GLCM) S(3, 3) SumAverg. Using the median number of required shockwaves (n = 72) as a threshold, receiver-operating characteristic analysis showed largest area-under-the-curve values for the SMOreg model (AUC = 0.84, r = 0.51, p < 0.001) using four weighted TA features: Histogram 10 th Percentile, and GLCM S(1, 1) InvDfMom, S(3, 3) SumAverg, and S(4, -4) SumVarnc., Conclusion: Our in vitro study illustrates the proof-of-principle of TA of urinary stone CT images for predicting the success of stone disintegration with SWL.

The purpose of this work was to develop a framework for 3D fusion of CT coronary angiography (CTCA) and whole-heart dynamic 3D cardiac magnetic resonance perfusion (3D-CMR-Perf) image data-correlating coronary artery stenoses to stress-induced myocardial perfusion deficits for the assessment of coronary artery disease (CAD). Twenty-three patients who underwent CTCA and 3D-CMR-Perf for various indications were included retrospectively. For CTCA, image quality and coronary diameter stenoses > 50% were documented. For 3D-CMR-Perf, image quality and stress-induced perfusion deficits were noted. A software framework was developed to allow for 3D image fusion of both datasets. Computation steps included: (1) fully automated segmentation of coronary arteries and heart contours from CT; (2) manual segmentation of the left ventricle in 3D-CMR-Perf images; (3) semi-automatic co-registration of CT/CMR datasets; (4) projection of the 3D-CMR-Perf values on the CT left ventricle. 3D fusion analysis was compared to separate inspection of CTCA and 3D-CMR-Perf data. CT and CMR scans resulted in an image quality being rated as good to excellent (mean scores 3.5 ± 0.5 and 3.7 ± 0.4, respectively, scale 1-4). 3D-fusion was feasible in all 23 patients, and perfusion deficits could be correlated to culprit coronary lesions in all but one case (22/23 = 96%). Compared to separate analysis of CT and CMR data, coronary supply territories of 3D-CMR-Perf perfusion deficits were refined in two cases (2/23 = 9%), and the relevance of stenoses in CTCA was re-judged in four cases (4/23 = 17%). In conclusion, 3D fusion of CTCA/3D-CMR-Perf facilitates anatomic correlation of coronary lesions and stress-induced myocardial perfusion deficits thereby helping to refine diagnostic assessment of CAD.

Purpose: The aim of this study was to investigate computed tomography (CT) imaging characteristics of coronary stents using a novel photon-counting detector (PCD) in comparison with a conventional energy-integrating detector (EID)., Materials and Methods: In this in vitro study, 18 different coronary stents were expanded in plastic tubes of 3 mm diameter, were filled with contrast agent (diluted to an attenuation of 250 Hounsfield units [HU] at 120 kVp), and were sealed. Stents were placed in an oil-filled custom phantom calibrated to an attenuation of -100 HU at 120 kVp for resembling pericardial fat. The phantom was positioned in the gantry at 2 different angles at 0 degree and 90 degrees relative to the z axis, and was imaged in a research dual-source PCD-CT scanner. Detector subsystem "A" used a standard 64-row EID, while detector subsystem "B" used a PCD, allowing high-resolution scanning (detector pixel-size 0.250 × 0.250 mm in the isocenter). Images were obtained from both detector systems at identical tube voltage (100 kVp) and tube current-time product (100 mA), and were both reconstructed using a typical convolution kernel for stent imaging (B46f) and using the same reconstruction parameters. Two independent, blinded readers evaluated in-stent visibility and measured noise, intraluminal stent diameter, and in-stent attenuation for each detector subsystem. Differences in noise, intraluminal stent diameter, and in-stent attenuation where tested using a paired t test; differences in subjective in-stent visibility were evaluated using a Wilcoxon signed-rank test., Results: Best results for in-stent visibility, noise, intraluminal stent diameter, and in-stent attenuation in EID and PCD were observed at 0-degree phantom position along the z axis, suggesting higher in-plane compared with through-plane resolution. Subjective in-stent visibility was superior in coronary stent images obtained from PCD compared with EID (P < 0.001). Mean in-stent diameter was 28.8% and 8.4% greater in PCD (0.85 ± 0.24 mm; 0.83 ± 0.14 mm) as compared with EID acquisitions (0.66 ± 0.21 mm; 0.76 ± 0.13 mm) for both 0-degree and 90-degree phantom positions, respectively. Average noise was significantly lower (P < 0.001) for PCD (5 ± 0.2 HU) compared with EID (8.3 ± 0.2 HU). The increase in in-stent attenuation (0 degree: Δ 245 ± 163 HU vs Δ 156.5 ± 126 HU; P = 0.006; 90 degrees: Δ 194 ± 141 HU vs Δ 126 ± 78 HU; P = 0.001) was significantly lower for PCD compared with EID acquisitions., Conclusions: At matched CT scan protocol settings and identical image reconstruction parameters, the PCD yields superior in-stent lumen delineation of coronary artery stents as compared with conventional EID arrays.

Background: The objective of this work was to support three-dimensional fusion of coronary CT angiography (coronary CTA) and CT myocardial perfusion (CT-Perf) data visualizing coronary artery stenoses and corresponding stress-induced myocardial perfusion deficits for diagnostics of coronary artery disease., Methods: Twelve patients undergoing coronary CTA/CT-Perf after heart transplantation were included (56 ± 12 years, all males). CT image quality was rated. Coronary diameter stenoses >50% were documented for coronary CTA. Stress-induced perfusion deficits were noted for CT-Perf. A software was implemented facilitating 3D fusion imaging of coronary CTA/CT-Perf data. Coronary arteries and heart contours were segmented automatically. To overcome anatomical mismatch of coronary CTA/CT-Perf image acquisition, perfusion values were projected on the left ventricle as visualized in coronary CTA. Three resulting datasets (coronary tree/heart contour/perfusion values) were fused for combined three-dimensional rendering. 3D fusion was compared with conventional analysis of coronary CTA/CT-Perf data and to results from catheter coronary angiography., Results: CT image quality was rated good-excellent (3.5 ± 0.5, scale 1-4). 3D fusion imaging of coronary CTA/CT-Perf data was feasible in 11/12 patients (92%). One patient (8%) was excluded from further analysis due to severe motion artifacts. 2 of 11 remaining patients (18%) showed both stress-induced perfusion deficits and relevant coronary stenoses. Using 3D fusion imaging, the ischemic region could be correlated to a culprit coronary lesion in one case (1/2 = 50%) and diagnostic findings could be rectified in the other case (1/2 = 50%). Coronary CTA was in full correspondence with catheter coronary angiography., Conclusion: A method for 3D fusion of coronary CTA/CT-Perf is introduced correlating relevant coronary lesions and corresponding stress-induced myocardial perfusion deficits., (Copyright © 2017 Society of Cardiovascular Computed Tomography. Published by Elsevier Inc. All rights reserved.)

Background: Accurate assessment of coronary stents after PCI using non-invasive imaging remains challenging despite technological improvements. New bioresorbable vascular scaffolds (BVS) have recently become available promising improved non-invasive imaging properties, which however have not be examined specifically yet. Therefore we investigated CT and MRI visualization properties of the only two CE-marked coronary BVSs., Methods: The Abbott Absorb and the Elixir DESolve BVS were placed in plastic tubes filled with contrast agent and scanned with a latest generation CT respectively MR system. For CT image quality was assessed by two blinded, independent readers and in-scaffold diameter difference as well as in-scaffold attenuation difference were measured. For MRI in-scaffold signal intensity, in-scaffold lumen visibility and in-scaffold signal homogeneity were measured., Results: In CTA both BVSs showed no significant difference to nominal tube diameter (DESolve 101%, Absorb 100%) and to nominal tube attenuation (DESolve 96%, Absorb 98%) and were both rated with the highest score for unrestricted lumen visualization. In MRA both BVSs showed unimpaired signal intensity (DESolve 103%, Absorb 100%), lumen visibility (DESolve 92%, Absorb 89%) and lumen homogeneity (DESolve SD 7.1%, Absorb SD 9.5%) when compared to the unstented tube. There was no significant difference between CTA and MRA results of both BVSs., Conclusions: Coronary BVSs show no relevant impairment for subjective and objective measures of in-stent lumen visualization by CT and MRI and will therefore allow reliable non-invasive assessment of coronary artery patency after PCI with deployment of a BVS, which is an (additional) advantage when compared to conventional stents., (Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.)

Objective: The aim of this study was to demonstrate the feasibility of whole-body diffusion tensor imaging (DTI) as a promising tool for research applications, for instance, for investigation of systemic muscle diseases., Materials and Methods: Twelve healthy volunteers (mean age, 26.6 years; range, 20-39 years) underwent whole-body magnetic resonance imaging at 3 T using an echo planar imaging sequence (b value, 400 s/mm) with 6 different spatial encoding directions. Coronal maps of DTI parameters including mean diffusivity, fractional anisotropy, and diffusion tensor eigenvalues (λ1-3) were generated using in-house MATLAB routines. Diffusion tensor imaging parameters were evaluated by region-of-interest analysis in skeletal muscle, cerebral gray and white matter, the kidneys, and the liver., Results: The acquisition time was 79 minutes 12 seconds. The different organs could be clearly depicted on the parametrical maps. Exemplary values in skeletal muscle were mean diffusivity, 1.67 ± 0.16 × 10(-3) mm2/s; fractional anisotropy, 0.26 ± 0.03; λ1, 2.17 ± 0.20 × 10(-3) mm2/s; λ2, 1.64 ± 0.17 × 10(-3) mm2/s; and λ3, 1.22 ± 0.12 × 10(-3) mm2/s., Conclusion: Whole-body DTI is technically feasible. Further refinements are required to achieve a higher signal-to-noise ratio and improved spatial resolution. A possible clinical application could be the assessment of systemic myopathies.

Introduction: Phase contrast MRI allows for the examination of complex hemodynamics in the heart and adjacent great vessels. Vortex flow patterns seem to play an important role in certain vascular pathologies. We propose two- and three-dimensional metrics for the objective quantification of aortic vortex blood flow in 4D phase contrast MRI., Materials and Methods: For two-dimensional vorticity assessment, a standardized set of 6 regions-of-interest (ROIs) was defined throughout the course of the aorta. For each ROI, a heatmap of time-resolved vorticity values [Formula: see text] was computed. Evolution of minimum, maximum, and average values as well as opposing rotational flow components were analyzed. For three-dimensional analysis, vortex core detection was implemented combining the predictor-corrector method with λ2 correction. Strength, elongation, and radial expansion of the detected vortex core were recorded over time. All methods were applied to 4D flow MRI datasets of 9 healthy subjects, 2 patients with mildly dilated aorta, and 1 patient with aortic aneurysm., Results: Vorticity quantification in the 6 standardized ROIs enabled the description of physiological vortex flow in the healthy aorta. Helical flow developed early in the ascending aorta (absolute vorticity = 166.4±86.4 s-1 at 12% of cardiac cycle) followed by maximum values in mid-systole in the aortic arch (240.1±45.2 s-1 at 16%). Strength, elongation, and radial expansion of 3D vortex cores escalated in early systole, reaching a peak in mid systole (strength = 241.2±30.7 s-1 at 17%, elongation = 65.1±34.6 mm at 18%, expansion = 80.1±48.8 mm2 at 20%), before all three parameters similarly decreased to overall low values in diastole. Flow patterns were considerably altered in patient data: Vortex flow developed late in mid/end-systole close to the aortic bulb and no physiological helix was found in the aortic arch., Conclusions: We have introduced objective measures for quantification of vortical flow in 4D phase contrast MRI. Vortex blood flow in the thoracic aorta could be consistently described in all healthy volunteers. In patient data, pathologically altered vortex flow was observed.

Background: Despite continuous improvements in CT technology, accurate stent lumen delineation remains challenging., Purpose: The aim was to evaluate the quality of coronary stent lumen delineation with CT using a detector with integrated electronics., Methods: Twelve coronary stents placed in plastic tubes and filled with contrast agent (CT number 250 HU) were imaged with either a 128-section dual-source CT machine equipped with conventional detector or with integrated electronics. On both scanners, images were reconstructed with filtered back projection (slice thickness 0.6 mm; increment 0.4 mm) and sinogram-affirmed iterative reconstruction (slice thickness 0.6 mm; increment 0.4 mm), and with iterative reconstruction (slice thickness 0.5 mm; increment 0.3 mm) on the integrated scanner. Two blinded, independent readers assessed image quality, noise, in-stent diameter, in-stent attenuation, and image sharpness by using signal intensity profiles across stents., Results: Interreader agreement for image quality assessment was substantial (κ = 0.798). Both readers rated best image quality in data sets from integrated detector at highest spatial resolution (86 or 72% of stents rated best quality). Image noise was significantly lower in data sets scanned with integrated detector, being lowest at 0.6 mm slice thickness (14.3 vs 21.0 HU; P < .001). Differences between measured and true in-stent diameters and differences in attenuation across stents were smallest, and average/maximum image sharpness was highest in data sets from the integrated detector using iterative reconstructions., Conclusion: CT coronary stent imaging is significantly improved by using a detector with integrated electronics combined with iterative reconstructions., (Copyright © 2013 Society of Cardiovascular Computed Tomography. Published by Elsevier Inc. All rights reserved.)

Aims: Cardiovascular magnetic resonance (CMR) allows non-invasive phase contrast measurements of flow through planes transecting large vessels. However, some clinically valuable applications are highly sensitive to errors caused by small offsets of measured velocities if these are not adequately corrected, for example by the use of static tissue or static phantom correction of the offset error. We studied the severity of uncorrected velocity offset errors across sites and CMR systems., Methods and Results: In a multi-centre, multi-vendor study, breath-hold through-plane retrospectively ECG-gated phase contrast acquisitions, as are used clinically for aortic and pulmonary flow measurement, were applied to static gelatin phantoms in twelve 1.5 T CMR systems, using a velocity encoding range of 150 cm/s. No post-processing corrections of offsets were implemented. The greatest uncorrected velocity offset, taken as an average over a 'great vessel' region (30 mm diameter) located up to 70 mm in-plane distance from the magnet isocenter, ranged from 0.4 cm/s to 4.9 cm/s. It averaged 2.7 cm/s over all the planes and systems. By theoretical calculation, a velocity offset error of 0.6 cm/s (representing just 0.4% of a 150 cm/s velocity encoding range) is barely acceptable, potentially causing about 5% miscalculation of cardiac output and up to 10% error in shunt measurement., Conclusion: In the absence of hardware or software upgrades able to reduce phase offset errors, all the systems tested appeared to require post-acquisition correction to achieve consistently reliable breath-hold measurements of flow. The effectiveness of offset correction software will still need testing with respect to clinical flow acquisitions.

The integration of medical devices with software applications is crucial for image-guided medical applications. This work describes a general device interface that has been designed for high-frequency streaming of multi-modal events, thus providing maximum performance and flexibility for such applications. Several sample applications and performance tests are provided to demonstrate the usability of the concept.