Value Creation Through Artificial Intelligence and Cardiovascular Imaging: A Scientific Statement From the American Heart Association.

Multiple applications for machine learning and artificial intelligence (AI) in cardiovascular imaging are being proposed and developed. However, the processes involved in implementing AI in cardiovascular imaging are highly diverse, varying by imaging modality, patient subtype, features to be extracted and analyzed, and clinical application. This article establishes a framework that defines value from an organizational perspective, followed by value chain analysis to identify the activities in which AI might produce the greatest incremental value creation. The various perspectives that should be considered are highlighted, including clinicians, imagers, hospitals, patients, and payers. Integrating the perspectives of all health care stakeholders is critical for creating value and ensuring the successful deployment of AI tools in a real-world setting. Different AI tools are summarized, along with the unique aspects of AI applications to various cardiac imaging modalities, including cardiac computed tomography, magnetic resonance imaging, and positron emission tomography. AI is applicable and has the potential to add value to cardiovascular imaging at every step along the patient journey, from selecting the more appropriate test to optimizing image acquisition and analysis, interpreting the results for classification and diagnosis, and predicting the risk for major adverse cardiac events.

Incidental Coronary Artery Calcium: Opportunistic Screening of Previous Nongated Chest Computed Tomography Scans to Improve Statin Rates (NOTIFY-1 Project).

BACKGROUND: Coronary artery calcium (CAC) can be identified on nongated chest computed tomography (CT) scans, but this finding is not consistently incorporated into care. A deep learning algorithm enables opportunistic CAC screening of nongated chest CT scans. Our objective was to evaluate the effect of notifying clinicians and patients of incidental CAC on statin initiation. METHODS: NOTIFY-1 (Incidental Coronary Calcification Quality Improvement Project) was a randomized quality improvement project in the Stanford Health Care System. Patients without known atherosclerotic cardiovascular disease or a previous statin prescription were screened for CAC on a previous nongated chest CT scan from 2014 to 2019 using a validated deep learning algorithm with radiologist confirmation. Patients with incidental CAC were randomly assigned to notification of the primary care clinician and patient versus usual care. Notification included a patient-specific image of CAC and guideline recommendations regarding statin use. The primary outcome was statin prescription within 6 months. RESULTS: Among 2113 patients who met initial clinical inclusion criteria, CAC was identified by the algorithm in 424 patients. After chart review and additional exclusions were made, a radiologist confirmed CAC among 173 of 194 patients (89.2%) who were randomly assigned to notification or usual care. At 6 months, the statin prescription rate was 51.2% (44/86) in the notification arm versus 6.9% (6/87) with usual care (P<0.001). There was also more coronary artery disease testing in the notification arm (15.1% [13/86] versus 2.3% [2/87]; P=0.008). CONCLUSIONS: Opportunistic CAC screening of previous nongated chest CT scans followed by clinician and patient notification led to a significant increase in statin prescriptions. Further research is needed to determine whether this approach can reduce atherosclerotic cardiovascular disease events. REGISTRATION: URL: https://www. CLINICALTRIALS: gov; Unique identifier: NCT04789278.

Feasibility of virtual non-iodine coronary calcium scoring on dual source photon-counting coronary CT angiography: a dynamic phantom study.

BACKGROUND: The aim of our current systematic dynamic phantom study was first, to optimize reconstruction parameters of coronary CTA (CCTA) acquired on photon counting CT (PCCT) for coronary artery calcium (CAC) scoring, and second, to assess the feasibility of calculating CAC scores from CCTA, in comparison to reference calcium scoring CT (CSCT) scans. METHODS: In this phantom study, an artificial coronary artery was translated at velocities corresponding to 0, < 60, and 60-75 beats per minute (bpm) within an anthropomorphic phantom. The density of calcifications was 100 (very low), 200 (low), 400 (medium), and 800 (high) mgHA/cm3, respectively. CCTA was reconstructed with the following parameters: virtual non-iodine (VNI), with and without iterative reconstruction (QIR level 2, QIR off, respectively); kernels Qr36 and Qr44f; slice thickness/increment 3.0/1.5 mm and 0.4/0.2 mm. The agreement in risk group classification between CACCCTA and CACCSCT scoring was measured using Cohen weighted linear κ with 95% CI. RESULTS: For CCTA reconstructed with 0.4 mm slice thickness, calcium detectability was perfect (100%). At < 60 bpm, CACCCTA of low, and medium density calcification was underestimated by 53%, and 15%, respectively. However, CACCCTA was not significantly different from CACCSCT of very low, and high-density calcifications. The best risk agreement was achieved when CCTA was reconstructed with QIR off, Qr44f, and 0.4 mm slice thickness (κ = 0.762, 95% CI 0.671-0.853). CONCLUSION: In this dynamic phantom study, the detection of calcifications with different densities was excellent with CCTA on PCCT using thin-slice VNI reconstruction. Agatston scores were underestimated compared to CSCT but agreement in risk classification was substantial. CLINICAL RELEVANCE STATEMENT: Photon counting CT may enable the implementation of coronary artery calcium scoring from coronary CTA in daily clinical practice. KEY POINTS: Photon-counting CTA allows for excellent detectability of low-density calcifications at all heart rates. Coronary artery calcium scoring from coronary CTA acquired on photon counting CT is feasible, although improvement is needed. Adoption of the standard acquisition and reconstruction protocol for calcium scoring is needed for improved quantification of coronary artery calcium to fully employ the potential of photon counting CT.

Optimization of window settings for coronary arteries assessment using spectral CT-derived virtual monoenergetic imaging.

PURPOSE: To determine the optimal window setting for virtual monoenergetic images (VMI) reconstructed from dual-layer spectral coronary computed tomography angiography (DE-CCTA) datasets. MATERIAL AND METHODS: 50 patients (30 males; mean age 61.1 ± 12.4 years who underwent DE-CCTA from May 2021 to June 2022 for suspected coronary artery disease, were retrospectively included. Image quality assessment was performed on conventional images and VMI reconstructions at 70 and 40 keV. Objective image quality was assessed using contrast-to-noise ratio (CNR). Two independent observers manually identified the best window settings (B-W/L) for VMI 70 and VMI 40 visualization. B-W/L were then normalized with aortic attenuation using linear regression analysis to obtain the optimized W/L (O-W/L) settings. Additionally, subjective image quality was evaluated using a 5-point Likert scale, and vessel diameters were measured to examine any potential impact of different W/L settings. RESULTS: VMI 40 demonstrated higher CNR values compared to conventional and VMI 70. B-W/L settings identified were 1180/280 HU for VMI 70 and 3290/900 HU for VMI 40. Subsequent linear regression analysis yielded O-W/L settings of 1155/270 HU for VMI 70 and 3230/880 HU for VMI 40. VMI 40 O-W/L received the highest scores for each parameter compared to conventional (all p < 0.0027). Using O-W/L settings for VMI 70 and VMI 40 did not result in significant differences in vessel measurements compared to conventional images. CONCLUSION: Optimization of VMI requires adjustments in W/L settings. Our results recommend W/L settings of 1155/270 HU for VMI 70 and 3230/880 HU for VMI 40.

Multi-omics staging of locally advanced rectal cancer predicts treatment response: a pilot study.

Treatment response assessment of rectal cancer patients is a critical component of personalized cancer care and it allows to identify suitable candidates for organ-preserving strategies. This pilot study employed a novel multi-omics approach combining MRI-based radiomic features and untargeted metabolomics to infer treatment response at staging. The metabolic signature highlighted how tumor cell viability is predictively down-regulated, while the response to oxidative stress was up-regulated in responder patients, showing significantly reduced oxoproline values at baseline compared to non-responder patients (p-value < 10-4). Tumors with a high degree of texture homogeneity, as assessed by radiomics, were more likely to achieve a major pathological response (p-value < 10-3). A machine learning classifier was implemented to summarize the multi-omics information and discriminate responders and non-responders. Combining all available radiomic and metabolomic features, the classifier delivered an AUC of 0.864 (± 0.083, p-value < 10-3) with a best-point sensitivity of 90.9% and a specificity of 81.8%. Our results suggest that a multi-omics approach, integrating radiomics and metabolomic data, can enhance the predictive value of standard MRI and could help to avoid unnecessary surgical treatments and their associated long-term complications.

Deep Learning Image Reconstruction for CT: Technical Principles and Clinical Prospects.

Filtered back projection (FBP) has been the standard CT image reconstruction method for 4 decades. A simple, fast, and reliable technique, FBP has delivered high-quality images in several clinical applications. However, with faster and more advanced CT scanners, FBP has become increasingly obsolete. Higher image noise and more artifacts are especially noticeable in lower-dose CT imaging using FBP. This performance gap was partly addressed by model-based iterative reconstruction (MBIR). Yet, its "plastic" image appearance and long reconstruction times have limited widespread application. Hybrid iterative reconstruction partially addressed these limitations by blending FBP with MBIR and is currently the state-of-the-art reconstruction technique. In the past 5 years, deep learning reconstruction (DLR) techniques have become increasingly popular. DLR uses artificial intelligence to reconstruct high-quality images from lower-dose CT faster than MBIR. However, the performance of DLR algorithms relies on the quality of data used for model training. Higher-quality training data will become available with photon-counting CT scanners. At the same time, spectral data would greatly benefit from the computational abilities of DLR. This review presents an overview of the principles, technical approaches, and clinical applications of DLR, including metal artifact reduction algorithms. In addition, emerging applications and prospects are discussed.

Inter-observer variability of expert-derived morphologic risk predictors in aortic dissection.

OBJECTIVES: Establishing the reproducibility of expert-derived measurements on CTA exams of aortic dissection is clinically important and paramount for ground-truth determination for machine learning. METHODS: Four independent observers retrospectively evaluated CTA exams of 72 patients with uncomplicated Stanford type B aortic dissection and assessed the reproducibility of a recently proposed combination of four morphologic risk predictors (maximum aortic diameter, false lumen circumferential angle, false lumen outflow, and intercostal arteries). For the first inter-observer variability assessment, 47 CTA scans from one aortic center were evaluated by expert-observer 1 in an unconstrained clinical assessment without a standardized workflow and compared to a composite of three expert-observers (observers 2-4) using a standardized workflow. A second inter-observer variability assessment on 30 out of the 47 CTA scans compared observers 3 and 4 with a constrained, standardized workflow. A third inter-observer variability assessment was done after specialized training and tested between observers 3 and 4 in an external population of 25 CTA scans. Inter-observer agreement was assessed with intraclass correlation coefficients (ICCs) and Bland-Altman plots. RESULTS: Pre-training ICCs of the four morphologic features ranged from 0.04 (-0.05 to 0.13) to 0.68 (0.49-0.81) between observer 1 and observers 2-4 and from 0.50 (0.32-0.69) to 0.89 (0.78-0.95) between observers 3 and 4. ICCs improved after training ranging from 0.69 (0.52-0.87) to 0.97 (0.94-0.99), and Bland-Altman analysis showed decreased bias and limits of agreement. CONCLUSIONS: Manual morphologic feature measurements on CTA images can be optimized resulting in improved inter-observer reliability. This is essential for robust ground-truth determination for machine learning models. KEY POINTS: • Clinical fashion manual measurements of aortic CTA imaging features showed poor inter-observer reproducibility. • A standardized workflow with standardized training resulted in substantial improvements with excellent inter-observer reproducibility. • Robust ground truth labels obtained manually with excellent inter-observer reproducibility are key to develop reliable machine learning models.

MRI-Based Radiomics Approach Predicts Tumor Recurrence in ER + /HER2 - Early Breast Cancer Patients.

Oncotype Dx Recurrence Score (RS) has been validated in patients with ER + /HER2 - invasive breast carcinoma to estimate patient risk of recurrence and guide the use of adjuvant chemotherapy. We investigated the role of MRI-based radiomics features extracted from the tumor and the peritumoral tissues to predict the risk of tumor recurrence. A total of 62 patients with biopsy-proved ER + /HER2 - breast cancer who underwent pre-treatment MRI and Oncotype Dx were included. An RS > 25 was considered discriminant between low-intermediate and high risk of tumor recurrence. Two readers segmented each tumor. Radiomics features were extracted from the tumor and the peritumoral tissues. Partial least square (PLS) regression was used as the multivariate machine learning algorithm. PLS ß-weights of radiomics features included the 5% features with the largest ß-weights in magnitude (top 5%). Leave-one-out nested cross-validation (nCV) was used to achieve hyperparameter optimization and evaluate the generalizable performance of the procedure. The diagnostic performance of the radiomics model was assessed through receiver operating characteristic (ROC) analysis. A null hypothesis probability threshold of 5% was chosen (p < 0.05). The exploratory analysis for the complete dataset revealed an average absolute correlation among features of 0.51. The nCV framework delivered an AUC of 0.76 (p = 1.1∙10-3). When combining "early" and "peak" DCE images of only T or TST, a tendency toward statistical significance was obtained for TST with an AUC of 0.61 (p = 0.05). The 47 features included in the top 5% were balanced between T and TST (23 and 24, respectively). Moreover, 33/47 (70%) were texture-related, and 25/47 (53%) were derived from high-resolution images (1 mm). A radiomics-based machine learning approach shows the potential to accurately predict the recurrence risk in early ER + /HER2 - breast cancer patients.

Low-dose coronary calcium scoring CT using a dedicated reconstruction filter for kV-independent calcium measurements.

In this prospective, pilot study, we tested a kV-independent coronary artery calcium scoring CT protocol, using a novel reconstruction kernel (Sa36f). From December 2018 to November 2019, we performed an additional research scan in 61 patients undergoing clinical calcium scanning. For the standard protocol (120 kVp), images were reconstructed with a standard, medium-sharp kernel (Qr36d). For the research protocol (automated kVp selection), images were reconstructed with a novel kernel (Sa36f). Research scans were sequentially performed using a higher (cohort A, n = 31) and a lower (cohort B, n = 30) dose optimizer setting within the automatic system with customizable kV selection. Agatston scores, coronary calcium volumes, and radiation exposure of the standard and research protocol were compared. A phantom study was conducted to determine inter-scan variability. There was excellent correlation for the Agatston score between the two protocols (r = 0.99); however, the standard protocol resulted in slightly higher Agatston scores (29.4 [0-139.0] vs 17.4 [0-158.2], p = 0.028). The median calcium volumes were similar (11.5 [0-109.2] vs 11.2 [0-118.0] mm3; p = 0.176), and the number of calcified lesions was not significantly different (p = 0.092). One patient was reclassified to another risk category. The research protocol could be performed at a lower kV and resulted in a substantially lower radiation exposure, with a median volumetric CT dose index of 4.1 vs 5.2 mGy, respectively (p < 0.001). Our results showed that a consistent coronary calcium scoring can be achieved using a kV-independent protocol that lowers radiation doses compared to the standard protocol. KEY POINTS: • The Sa36f kernel enables kV-independent Agatston scoring without changing the original Agatston weighting threshold. • Agatston scores and calcium volumes of the standard and research protocols showed an excellent correlation. • The research protocol resulted in a significant reduction in radiation exposure with a mean reduction of 22% in DLP and 25% in CTDIvol.

Thoracic Endovascular Aortic Repair for Chronic Type B Aortic Dissection: Pre- and Postprocedural Imaging.

Aortic dissection is a chronic disease that requires lifelong clinical and imaging surveillance, long after the acute event. Imaging has an important role in prognosis, timing of repair, device sizing, and monitoring for complications, especially in the endovascular therapy era. Important anatomic features at preprocedural imaging include the location of the primary intimal tear and aortic zonal and branch vessel involvement, which influence the treatment strategy. Challenges of repair in the chronic phase include a small true lumen in conjunction with a stiff intimal flap, complex anatomy, and retrograde perfusion from distal reentry tears. The role of thoracic endovascular aortic repair (TEVAR) remains controversial for treatment of chronic aortic dissection. Standard TEVAR is aimed at excluding the primary intimal tear to decrease false lumen perfusion, induce false lumen thrombosis, promote aortic remodeling, and prevent aortic growth. In addition to covering the primary intimal tear with an endograft, several adjunctive techniques have been developed to mitigate retrograde false lumen perfusion. These techniques are broadly categorized into false lumen obliteration and landing zone optimization strategies, such as the provisional extension to induce complete attachment (PETTICOAT), false lumen embolization, cheese-wire fenestration, and knickerbocker techniques. Familiarity with these techniques is important to recognize expected changes and complications at postintervention imaging. The authors detail imaging options, provide examples of simple and complex endovascular repairs of aortic dissections, and highlight complications that can be associated with various techniques. Online supplemental material is available for this article. ©RSNA, 2022.

Diagnostic performance of single-phase dual-energy CT to differentiate vascular and nonvascular incidental renal lesions on portal venous phase: comparison with CT.

OBJECTIVES: To determine whether single-phase dual-energy CT (DECT) differentiates vascular and nonvascular renal lesions in the portal venous phase (PVP). Optimal iodine threshold was determined and compared to Hounsfield unit (HU) measurements. METHODS: We retrospectively included 250 patients (266 renal lesions) who underwent a clinically indicated PVP abdominopelvic CT on a rapid-kilovoltage-switching single-source DECT (rsDECT) or a dual-source DECT (dsDECT) scanner. Iodine concentration and HU measurements were calculated by four experienced readers. Diagnostic accuracy was determined using biopsy results and follow-up imaging as reference standard. Area under the curve (AUC) was calculated for each DECT scanner to differentiate vascular from nonvascular lesions and vascular lesions from hemorrhagic/proteinaceous cysts. Univariable and multivariable logistic regression analyses evaluated the association between variables and the presence of vascular lesions. RESULTS: A normalized iodine concentration threshold of 0.25 mg/mL yielded high accuracy in differentiating vascular and nonvascular lesions (AUC 0.93, p < 0.001), with comparable performance to HU measurements (AUC 0.93). Both iodine concentration and HU measurements were independently associated with vascular lesions when adjusted for age, gender, body mass index, and lesion size (AUC 0.95 and 0.95, respectively). When combined, diagnostic performance was higher (AUC 0.96). Both absolute and normalized iodine concentrations performed better than HU measurements (AUC 0.92 vs. AUC 0.87) in differentiating vascular lesions from hemorrhagic/proteinaceous cysts. CONCLUSION: A single-phase (PVP) DECT scan yields high accuracy to differentiate vascular from nonvascular renal lesions. Iodine concentration showed a slightly higher performance than HU measurements in differentiating vascular lesions from hemorrhagic/proteinaceous cysts. KEY POINTS: • A single-phase dual-energy CT scan in the portal venous phase differentiates vascular from nonvascular renal lesions with high accuracy (AUC 0.93). • When combined, iodine concentration and HU measurements showed the highest diagnostic performance (AUC 0.96) to differentiate vascular from nonvascular renal lesions. • Compared to HU measurements, iodine concentration showed a slightly higher performance in differentiating vascular lesions from hemorrhagic/proteinaceous cysts.

An international survey on AI in radiology in 1,041 radiologists and radiology residents part 1: fear of replacement, knowledge, and attitude.

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.

An international survey on AI in radiology in 1041 radiologists and radiology residents part 2: expectations, hurdles to implementation, and education.

OBJECTIVES: Currently, hurdles to implementation of artificial intelligence (AI) in radiology are a much-debated topic but have not been investigated in the community at large. Also, controversy exists if and to what extent AI should be incorporated into radiology residency programs. METHODS: Between April and July 2019, an international survey took place on AI regarding its impact on the profession and training. The survey was accessible for radiologists and residents and distributed through several radiological societies. Relationships of independent variables with opinions, hurdles, and education were assessed using multivariable logistic regression. RESULTS: The survey was completed by 1041 respondents from 54 countries. A majority (n = 855, 82%) expects that AI will cause a change to the radiology field within 10 years. Most frequently, expected roles of AI in clinical practice were second reader (n = 829, 78%) and work-flow optimization (n = 802, 77%). Ethical and legal issues (n = 630, 62%) and lack of knowledge (n = 584, 57%) were mentioned most often as hurdles to implementation. Expert respondents added lack of labelled images and generalizability issues. A majority (n = 819, 79%) indicated that AI should be incorporated in residency programs, while less support for imaging informatics and AI as a subspecialty was found (n = 241, 23%). CONCLUSIONS: Broad community demand exists for incorporation of AI into residency programs. Based on the results of the current study, integration of AI education seems advisable for radiology residents, including issues related to data management, ethics, and legislation. KEY POINTS: • There is broad demand from the radiological community to incorporate AI into residency programs, but there is less support to recognize imaging informatics as a radiological subspecialty. • Ethical and legal issues and lack of knowledge are recognized as major bottlenecks for AI implementation by the radiological community, while the shortage in labeled data and IT-infrastructure issues are less often recognized as hurdles. • Integrating AI education in radiology curricula including technical aspects of data management, risk of bias, and ethical and legal issues may aid successful integration of AI into diagnostic radiology.

Bladder cancer: do we need contrast injection for MRI assessment of muscle invasion? A prospective multi-reader VI-RADS approach.

OBJECTIVES: (1) To investigate whether a contrast-free biparametric MRI (bp-MRI) including T2-weighted images (T2W) and diffusion-weighted images (DWI) can be considered an accurate alternative to the standard multiparametric MRI (mp-MRI), consisting of T2, DWI, and dynamic contrast-enhanced (DCE) imaging for the muscle-invasiveness assessment of bladder cancer (BC), and (2) to evaluate how the diagnostic performance of differently experienced readers is affected according to the type of MRI protocol. METHODS: Thirty-eight patients who underwent a clinically indicated bladder mp-MRI on a 3-T scanner were prospectively enrolled. Trans-urethral resection of bladder was the gold standard. Two sets of images, set 1 (bp-MRI) and set 2 (mp-MRI), were independently reviewed by four readers. Descriptive statistics, including sensitivity and specificity, were calculated for each reader. Receiver operating characteristic (ROC) analysis was performed, and the areas under the curve (AUCs) were calculated for the bp-MRI and the standard mp-MRI. Pairwise comparison of the ROC curves was performed. RESULTS: The AUCs for bp- and mp-MRI were respectively 0.91-0.92 (reader 1), 0.90 (reader 2), 0.95-0.90 (reader 3), and 0.90-0.87 (reader 4). Sensitivity was 100% for both protocols and specificity ranged between 79.31 and 89.66% and between 79.31 and 83.33% for bp-MRI and mp-MRI, respectively. No significant differences were shown between the two MRI protocols (p > 0.05). No significant differences were shown accordingly to the reader's experience (p > 0.05). CONCLUSIONS: A bp-MRI protocol consisting of T2W and DWI has comparable diagnostic accuracy to the standard mp-MRI protocol for the detection of muscle-invasive bladder cancer. The experience of the reader does not significantly affect the diagnostic performance using VI-RADS. KEY POINTS: • The contrast-free MRI protocol shows a comparable accuracy to the standard multiparametric MRI protocol in the bladder cancer muscle-invasiveness assessment. • VI-RADS classification helps non-expert radiologists to assess the muscle-invasiveness of bladder cancer. • DCE should be carefully interpreted by less experienced readers due to inflammatory changes representing a potential pitfall.

Predictive Value of Cardiac CTA, Cardiac MRI, and Transthoracic Echocardiography for Cardioembolic Stroke Recurrence.

BACKGROUND. Transthoracic echocardiography (TTE) is the standard of care for initial evaluation of patients with suspected cardioembolic stroke. Although TTE is useful for assessing certain sources of cardiac emboli, its diagnostic capability is limited in the detection of other sources, including left atrial thrombus and aortic plaques. OBJECTIVE. The purpose of this article was to investigate sensitivity, specificity, and predictive value of cardiac CTA (CCTA), cardiac MRI (CMRI), and TTE for recurrence in patients with suspected cardioembolic stroke. METHODS. We retrospectively included 151 patients with suspected cardioembolic stroke who underwent TTE and either CMRI (n = 75) or CCTA (n = 76) between January 2013 and May 2017. We evaluated for the presence of left atrial thrombus, left ventricular thrombus, vulnerable aortic plaque, cardiac tumors, and valvular vegetation as causes of cardioembolic stroke. The end point was stroke recurrence. Sensitivity, specificity, PPV, and NPV for recurrent stroke were calculated; the diagnostic accuracy of CMRI, CCTA, and TTE was compared between and within groups using AUC. RESULTS. Twelve and 14 recurrent strokes occurred in the CCTA and CMRI groups, respectively. Sensitivity, specificity, PPV, and NPV were 33.3%, 93.7%, 50.0%, and 88.2% for CCTA; 14.3%, 80.3%, 14.3%, and 80.3% for CMRI; 14.3%, 83.6%, 16.7%, and 80.9% for TTE in the CMRI group; and 8.3%, 93.7%, 20.0%, and 84.5% for TTE in the CCTA group. Accuracy was not different (p > .05) between CCTA (AUC = 0.63; 95% CI, 0.49-0.77), CMRI (0.53; 95% CI, 0.42-0.63), TTE in the CMRI group (0.51; 95% CI, 0.40-0.61), and TTE in the CCTA group (0.51; 95% CI, 0.42-0.59). In the CCTA group, atrial and ventricular thrombus were detected by CCTA in three patients and TTE in one patient; in the CMRI group, thrombus was detected by CMRI in one patient and TTE in two patients. CONCLUSION. CCTA, CMRI, and TTE showed comparably high specificity and NPV for cardioembolic stroke recurrence. CCTA and CMRI may be valid alternatives to TTE. CCTA may be preferred given potentially better detection of atrial and ventricular thrombus. CLINICAL IMPACT. CCTA and CMRI have similar clinical performance as TTE for predicting cardioembolic stroke recurrence. This observation may be especially important when TTE provides equivocal findings.

Bone marrow magnetic resonance imaging: physiologic and pathologic findings that radiologist should know.

Magnetic resonance imaging (MRI) plays a leading role in the non-invasive evaluation of bone marrow (BM). Normal BM pattern depends on the ratio and distribution of yellow and red marrow, which are subject to changes with age, pathologies, and treatments. Neonates show almost entirely red marrow. Over time, yellow marrow conversion takes place with a characteristic sequence leading to a red marrow persistence in proximal metaphyses of long bones. In adults, normal BM is composed of both red (40% water, 40% fat) and yellow marrow (15% water, 80% fat). Due to the higher content of fat, yellow marrow normally appears hyperintense on T1-weighted (T1w) fast spin echo (FSE) sequences and hypo-/iso-intense in short tau inversion recovery (STIR) T2-weighted (T2w); red marrow appears slightly hyperintense in T1w FSE and hyper-/iso-intense in STIR T2w. Pathologic BM has reduced fat and increased water percentages, resulting hypointense in T1w FSE and hyperintense in STIR T2w. In oncologic patients, BM MRI signal largely depends on the treatment (irradiation and/or chemotherapy) and its timing. BM fat and water amount and location in normal red/yellow and pathologic marrow are responsible for different signals in MRI sequences whose knowledge by radiologists may help to differentiate between normal and pathologic findings. Our aim was to discuss and illustrate the MRI of BM physiologic conversion and pathologic reconversion occurring in malignancies and after treatments in cancer patients.

Multimodality Imaging of Hepatocellular Carcinoma: From Diagnosis to Treatment Response Assessment in Everyday Clinical Practice.

The Liver Imaging Reporting and Data System (LI-RADS) is a recently developed classification aiming to improve the standardization of liver imaging assessment in patients at risk of developing hepatocellular carcinoma (HCC). The LI-RADS v2017 implemented new algorithms for ultrasound (US) screening and surveillance, contrast-enhanced US diagnosis and computed tomography/magnetic resonance imaging treatment response assessment. A minor update of LI-RADS was released in 2018 to comply with the American Association for the Study of the Liver Diseases guidance recommendations. The scope of this review is to provide a practical overview of LI-RADS v2018 focused both on the multimodality HCC diagnosis and treatment response assessment.

Diffusion-Weighted MR Imaging of Primary and Secondary Lung Cancer: Predictive Value for Response to Transpulmonary Chemoembolization and Transarterial Chemoperfusion.

PURPOSE: To examine predictive value of apparent diffusion coefficient (ADC) in diffusion-weighted imaging (DWI) for response of patients with primary and secondary lung neoplasms undergoing transpulmonary chemoembolization (TPCE) and transarterial chemoperfusion (TACP) treatment. MATERIALS AND METHODS: Thirty-one patients (mean age ± SD 64 ± 12.4 y) with 42 lung target lesions (13 primary and 29 secondary) underwent DWI and subsequent ADC analysis on a 1.5T MR imaging scanner before and 30.3 days ± 6.4 after first session of TPCE or TACP. After 3.1 treatment sessions ± 1.4 performed in 2- to 4-week intervals, morphologic response was analyzed by comparing tumor diameter and volume before and after treatment on unenhanced T1-weighted MR images. On a per-lesion basis, response was classified according to Response Evaluation Criteria In Solid Tumors. RESULTS: Threshold ADC increase of 20.7% indicated volume response with 88% sensitivity and 78% specificity (area under the curve [AUC] = 0.84). Differences between ADC changes in volume response groups were significant (P = .002). AUC for volume response predicted by ADC before treatment was 0.77. Median ADC before treatment and mean ADC change were 1.09 × 10-3 mm2/second and 0.36 × 10-3 mm2/second ± 0.23, 1.45 × 10-3 mm2/second and 0.14 × 10-3 mm2/second ± 0.16, and 1.30 × 10-3 mm2/second and 0.06 × 10-3 mm2/second ± 0.19 in partial response, stable disease, and progressive disease groups. In primary lung cancer lesions, strong negative correlation of ADC change with change in diameter (ρ = -.87, P < .001) and volume (ρ = -.66, P = .016) was found. In metastases, respective correlation coefficients were ρ = -.18 (P = .356) and ρ = -.35 (P = .061). CONCLUSIONS: ADC quantification shows considerable diagnostic value for predicting response and monitoring TPCE and TACP treatment of patients with primary and secondary lung neoplasms.

Nonbinary quantification technique accounting for myocardial infarct heterogeneity: Feasibility of applying percent infarct mapping in patients.

BACKGROUND: Binary threshold-based quantification techniques ignore myocardial infarct (MI) heterogeneity, yielding substantial misquantification of MI. PURPOSE: To assess the technical feasibility of MI quantification using percent infarct mapping (PIM), a prototype nonbinary algorithm, in patients with suspected MI. STUDY TYPE: Prospective cohort POPULATION: Patients (n = 171) with suspected MI referred for cardiac MRI. FIELD STRENGTH/SEQUENCE: Inversion recovery balanced steady-state free-precession for late gadolinium enhancement (LGE) and modified Look-Locker inversion recovery (MOLLI) T1 -mapping on a 1.5T system. ASSESSMENT: Infarct volume (IV) and infarct fraction (IF) were quantified by two observers based on manual delineation, binary approaches (2-5 standard deviations [SD] and full-width at half-maximum [FWHM] thresholds) in LGE images, and by applying the PIM algorithm in T1 and LGE images (PIMT1 ; PIMLGE ). STATISTICAL TEST: IV and IF were analyzed using repeated measures analysis of variance (ANOVA). Agreement between the approaches was determined with Bland-Altman analysis. Interobserver agreement was assessed by intraclass correlation coefficient (ICC) analysis. RESULTS: MI was observed in 89 (54.9%) patients, and 185 (38%) short-axis slices. IF with 2, 3, 4, 5SDs and FWHM techniques were 15.7 ± 6.6, 13.4 ± 5.6, 11.6 ± 5.0, 10.8 ± 5.2, and 10.0 ± 5.2%, respectively. The 5SD and FWHM techniques had the best agreement with manual IF (9.9 ± 4.8%) determination (bias 1.0 and 0.2%; P = 0.1426 and P = 0.8094, respectively). The 2SD and 3SD algorithms significantly overestimated manual IF (9.9 ± 4.8%; both P < 0.0001). PIMLGE measured significantly lower IF (7.8 ± 3.7%) compared to manual values (P < 0.0001). PIMLGE , however, showed the best agreement with the PIMT1 reference (7.6 ± 3.6%, P = 0.3156). Interobserver agreement was rated good to excellent for IV (ICCs between 0.727-0.820) and fair to good for IF (0.589-0.736). DATA CONCLUSION: The application of the PIMLGE technique for MI quantification in patients is feasible. PIMLGE , with its ability to account for voxelwise MI content, provides significantly smaller IF than any thresholding technique and shows excellent agreement with the T1 -based reference. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018.