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The Society for Cardiovascular Magnetic Resonance (SCMR) last published its comprehensive expert panel report of clinical indications for CMR in 2004. This new Consensus Panel report brings those indications up to date for 2020 and includes the very substantial increase in scanning techniques, clinical applicability and adoption of CMR worldwide. We have used a nearly identical grading system for indications as in 2004 to ensure comparability with the previous report but have added the presence of randomized controlled trials as evidence for level 1 indications. In addition to the text, tables of the consensus indication levels are included for rapid assimilation and illustrative figures of some key techniques are provided.

Machine learning (ML) is making a dramatic impact on cardiovascular magnetic resonance (CMR) in many ways. This review seeks to highlight the major areas in CMR where ML, and deep learning in particular, can assist clinicians and engineers in improving imaging efficiency, quality, image analysis and interpretation, as well as patient evaluation. We discuss recent developments in the field of ML relevant to CMR in the areas of image acquisition & reconstruction, image analysis, diagnostic evaluation and derivation of prognostic information. To date, the main impact of ML in CMR has been to significantly reduce the time required for image segmentation and analysis. Accurate and reproducible fully automated quantification of left and right ventricular mass and volume is now available in commercial products. Active research areas include reduction of image acquisition and reconstruction time, improving spatial and temporal resolution, and analysis of perfusion and myocardial mapping. Although large cohort studies are providing valuable data sets for ML training, care must be taken in extending applications to specific patient groups. Since ML algorithms can fail in unpredictable ways, it is important to mitigate this by open source publication of computational processes and datasets. Furthermore, controlled trials are needed to evaluate methods across multiple centers and patient groups.

Objectives Radiologists’ perception is likely to influence the adoption of artificial intelligence (AI) into clinical practice. We investigated knowledge and attitude towards AI by radiologists and residents in Europe and beyond. Methods Between April and July 2019, a survey on fear of replacement, knowledge, and attitude towards AI was accessible to radiologists and residents. The survey was distributed through several radiological societies, author networks, and social media. Independent predictors of fear of replacement and a positive attitude towards AI were assessed using multivariable logistic regression. Results The survey was completed by 1,041 respondents from 54 mostly European countries. Most respondents were male ( n = 670, 65%), median age was 38 (24–74) years, n = 142 (35%) residents, and n = 471 (45%) worked in an academic center. Basic AI-specific knowledge was associated with fear (adjusted OR 1.56, 95% CI 1.10–2.21, p = 0.01), while intermediate AI-specific knowledge (adjusted OR 0.40, 95% CI 0.20–0.80, p = 0.01) or advanced AI-specific knowledge (adjusted OR 0.43, 95% CI 0.21–0.90, p = 0.03) was inversely associated with fear. A positive attitude towards AI was observed in 48% ( n = 501) and was associated with only having heard of AI, intermediate (adjusted OR 11.65, 95% CI 4.25–31.92, p < 0.001), or advanced AI-specific knowledge (adjusted OR 17.65, 95% CI 6.16–50.54, p < 0.001). Conclusions Limited AI-specific knowledge levels among radiology residents and radiologists are associated with fear, while intermediate to advanced AI-specific knowledge levels are associated with a positive attitude towards AI. Additional training may therefore improve clinical adoption. Key Points • Forty-eight percent of radiologists and residents have an open and proactive attitude towards artificial intelligence (AI), while 38% fear of replacement by AI. • Intermediate and advanced AI-specific knowledge levels may enhance adoption of AI in clinical practice, while rudimentary knowledge levels appear to be inhibitive. • AI should be incorporated in radiology training curricula to help facilitate its clinical adoption.

Objective To determine the accuracy of iodine quantification with dual energy computed tomography (DECT) in two high-end CT systems with different spectral imaging techniques. Methods Five tubes with different iodine concentrations (0, 5, 10, 15, 20 mg/ml) were analysed in an anthropomorphic thoracic phantom. Adding two phantom rings simulated increased patient size. For third-generation dual source CT (DSCT), tube voltage combinations of 150Sn and 70, 80, 90, 100 kVp were analysed. For dual layer CT (DLCT), 120 and 140 kVp were used. Scans were repeated three times. Median normalized values and interquartile ranges (IQRs) were calculated for all kVp settings and phantom sizes. Results Correlation between measured and known iodine concentrations was excellent for both systems ( R  = 0.999–1.000, p  < 0.0001). For DSCT, median measurement errors ranged from −0.5% (IQR −2.0, 2.0%) at 150Sn/70 kVp and −2.3% (IQR −4.0, −0.1%) at 150Sn/80 kVp to −4.0% (IQR −6.0, −2.8%) at 150Sn/90 kVp. For DLCT, median measurement errors ranged from −3.3% (IQR −4.9, −1.5%) at 140 kVp to −4.6% (IQR −6.0, −3.6%) at 120 kVp. Larger phantom sizes increased variability of iodine measurements ( p  < 0.05). Conclusion Iodine concentration can be accurately quantified with state-of-the-art DECT systems from two vendors. The lowest absolute errors were found for DSCT using the 150Sn/70 kVp or 150Sn/80 kVp combinations, which was slightly more accurate than 140 kVp in DLCT. Key Points • High - end CT scanners allow accurate iodine quantification using different DECT techniques . • Lowest measurement error was found in scans with largest photon energy separation . • Dual - source CT quantified iodine slightly more accurately than dual layer CT .

ObjectivesTo evaluate the added value of deep learning (DL) analysis of the left ventricular myocardium (LVM) in resting coronary CT angiography (CCTA) over determination of coronary degree of stenosis (DS), for identification of patients with functionally significant coronary artery stenosis.MethodsPatients who underwent CCTA prior to an invasive fractional flow reserve (FFR) measurement were retrospectively selected. Highest DS from CCTA was used to classify patients as having non-significant (≤ 24% DS), intermediate (25–69% DS), or significant stenosis (≥ 70% DS). Patients with intermediate stenosis were referred for fully automatic DL analysis of the LVM. The DL algorithm characterized the LVM, and likely encoded information regarding shape, texture, contrast enhancement, and more. Based on these encodings, features were extracted and patients classified as having a non-significant or significant stenosis. Diagnostic performance of the combined method was evaluated and compared to DS evaluation only. Functionally significant stenosis was defined as FFR ≤ 0.8 or presence of angiographic high-grade stenosis (≥ 90% DS).ResultsThe final study population consisted of 126 patients (77% male, 59 ± 9 years). Eighty-one patients (64%) had a functionally significant stenosis. The proposed method resulted in improved discrimination (AUC = 0.76) compared to classification based on DS only (AUC = 0.68). Sensitivity and specificity were 92.6% and 31.1% for DS only (≥ 50% indicating functionally significant stenosis), and 84.6% and 48.4% for the proposed method.ConclusionThe combination of DS with DL analysis of the LVM in intermediate-degree coronary stenosis may result in improved diagnostic performance for identification of patients with functionally significant coronary artery stenosis.Key Points• Assessment of degree of coronary stenosis on CCTA has consistently high sensitivity and negative predictive value, but has limited specificity for identifying the functional significance of a stenosis.• Deep learning algorithms are able to learn complex patterns and relationships directly from the images without prior specification of which image features represent presence of disease, and thereby may be more sensitive to subtle changes in the LVM caused by functionally significant stenosis.• Addition of deep learning analysis of the left ventricular myocardium to the evaluation of degree of coronary artery stenosis improves diagnostic performance and increases specificity of resting CCTA. This could potentially decrease the number of patients undergoing invasive coronary angiography.

Machine learning, and especially deep learning, is rapidly gaining acceptance and clinical usage in a wide range of image analysis applications and is regarded as providing high performance in detecting anatomical structures and identification and classification of patterns of disease in medical images. However, there are many roadblocks to the widespread implementation of machine learning in clinical image analysis, including differences in data capture leading to different measurements, high dimensionality of imaging and other medical data, and the black-box nature of machine learning, with a lack of insight into relevant features. Techniques such as radiomics have been used in traditional machine learning approaches to model the mathematical relationships between adjacent pixels in an image and provide an explainable framework for clinicians and researchers. Newer paradigms, such as topological data analysis (TDA), have recently been adopted to design and develop innovative image analysis schemes that go beyond the abilities of pixel-to-pixel comparisons. TDA can automatically construct filtrations of topological shapes of image texture through a technique known as persistent homology (PH); these features can then be fed into machine learning models that provide explainable outputs and can distinguish different image classes in a computationally more efficient way, when compared to other currently used methods. The aim of this review is to introduce PH and its variants and to review TDA’s recent successes in medical imaging studies.Key pointsTopological data analysis (TDA) provides information on the shape of data.In radiology, the shape of 2D and 3D images contains additional information.TDA can be combined with other applications, such as textural analysis.Persistent homology can provide a visual representation of extracted TDA data.

Conventional Computed Tomography (CCT) measures the total x-ray attenuation for a whole x-ray spectrum, which can be used for medical diagnosis of anatomically visible pathologies. Spectral Computed Tomography (SCT) is an extension of CCT and enables the separation of two distinct processes of x-ray attenuation in tissues. The combined pair of attenuation contributions is material-specific, enabling rapid quantification of in vivo tissue composition. The combined, detailed knowledge about anatomy and pathological tissue composition allows for disease phenotyping, with great potential for improved medical diagnosis. In this paper we introduce X-Ray Attenuation Decomposition (XAD), to intuitively display and quantify the material contents of biological tissues and integrate it into the conventional, anatomical visualization of CT images. XAD gives an interactive display of material contents superimposed on anatomical information, making it easy to identify both the anatomical location as well as the concentrations of specific materials.

Cardiac cine imaging is routinely used in patient with suspected or known cardiac dysfunction. Water and fat (W/F) separated cardiovascular magnetic resonance (CMR) will be helpful to distinguish adipose tissue, blood, and myocardium. Inclusion of a multi-echo acquisition in the conventional balanced steady-state free precession (bSSFP) cine sequence can introduce artifacts and reduce temporal resolution. Spiral MRI is known for its signal-to-noise ratio (SNR) efficiency and has the potential to improve temporal efficiency for W/F separated cine imaging. The present work implements a spoiled gradient echo sequence (SPGR) with spiral trajectory to obtain W/F separated cine images simultaneously. Three different sequences were performed for comparison, a Cartesian 2-TE bSSFP sequence, a Cartesian 3-TE bSSFP sequence, and the proposed spiral SPGR sequence. Five volunteers were recruited for the scans on a 1.5T scanner with spatial resolution 1.7×1.7×8.0mm3 over a 400×400mm2 FOV. In addition to qualitative comparisons, a quantitative measurement is performed in terms of the contrast-to-noise ratio (CNR). The proposed method to obtain W/F separated cine images provides better temporal efficiency and fewer artifacts compared to conventional Cartesian bSSFP sequences. The 2-TE bSSFP features the highest artifact level, including susceptibility artifacts and fat/water swaps. The proposed method reduces scan time by approximately 50% with similar spatial and temporal resolution with lower specific absorption rate (SAR). The contrast between the blood pool and myocardium is higher when using the spiral readout (p≤0.05). The results suggest that the presented sequence has potential to facilitate simultaneous imaging for water and fat components in a cine scan while shortening exam time and lowering SAR. [Display omitted]

Objectives To present the results of a systematic literature search aimed at determining to what extent the radiation dose can be reduced with iterative reconstruction (IR) for cardiopulmonary and body imaging with computed tomography (CT) in the clinical setting and what the effects on image quality are with IR versus filtered back-projection (FBP) and to provide recommendations for future research on IR. Methods We searched Medline and Embase from January 2006 to January 2012 and included original research papers concerning IR for CT. Results The systematic search yielded 380 articles. Forty-nine relevant studies were included. These studies concerned: the chest( n  = 26), abdomen( n  = 16), both chest and abdomen( n  = 1), head( n  = 4), spine( n  = 1), and no specific area ( n  = 1). IR reduced noise and artefacts, and it improved subjective and objective image quality compared to FBP at the same dose. Conversely, low-dose IR and normal-dose FBP showed similar noise, artefacts, and subjective and objective image quality. Reported dose reductions ranged from 23 to 76 % compared to locally used default FBP settings. However, IR has not yet been investigated for ultra-low-dose acquisitions with clinical diagnosis and accuracy as endpoints. Conclusion Benefits of IR include improved subjective and objective image quality as well as radiation dose reduction while preserving image quality. Future studies need to address the value of IR in ultra-low-dose CT with clinically relevant endpoints. Key Points • Iterative reconstruction improves image quality of CT images at equal acquisition parameters. • IR preserves image quality compared to normal-dose filtered back-projection. • The reduced radiation dose made possible by IR is advantageous for patients. • IR has not yet been investigated with clinical diagnosis and accuracy as endpoints.