Fast simultaneous noncontrast angiography and intraplaque hemorrhage (fSNAP) sequence for carotid artery imaging

Application of mask images of contrast-enhanced MR angiography to detect carotid intraplaque hemorrhage in patients with moderate to severe symptomatic and asymptomatic carotid stenosis

PURPOSE

Carotid intraplaque hemorrhage (IPH) on MRI predicts stroke. Magnetization-prepared rapid acquisition gradient (MP-RAGE) is widely used to detect IPH. CE-MRA is used routinely to assess stenosis. Initial studies indicated that IPH can be identified on mask images of CE-MRA, while Time-of-Flight (TOF) images were reported to have high specificity but lower sensitivity. We investigated the diagnostic accuracy of detecting IPH on mask images of CE-MRA and TOF.

METHODS

Thirty-six patients with ≥ 50% stenosis enrolled in the ongoing 2nd European Carotid Surgery Trial underwent carotid MRI. A 5-point quality score was used. Inter-observer agreement between two independent readers was determined. The sensitivity and specificity of IPH detection on mask MRA and TOF were calculated with MP-RAGE as a reference standard.

RESULTS

Of the 36 patients included in the current analysis, 66/72 carotid arteries could be scored. The inter-observer agreements for identifying IPH on MP-RAGE, mask, and TOF were outstanding (κ: 0.93, 0.96, and 0.85). The image quality of mask (1.42 ± 0.66) and TOF (2.42 ± 0.66) was significantly lower than MP-RAGE (3.47 ± 0.61). When T1w images were used to delineate the outer carotid wall, very high specificities (>95%) of IPH detection on mask and TOF images were found, while the sensitivity was high for mask images (>81%) and poor for TOF (50-60%). Without these images, the specificity was still high (>97%), while the sensitivity reduced to 62-71%.

CONCLUSION

Despite the lower image quality, routinely acquired mask images from CE-MRA, but not TOF, can be used as an alternative to MP-RAGE images to visualize IPH.

Role and progress of artificial intelligence in radiodiagnosing vascular calcification: a narrative review

Background and Objective

Vascular calcification has important clinical significance due to its vital prognostic value for cardiovascular diseases, chronic kidney disease (CKD), diabetes, fracture, and other multisystem diseases. Radiology is the main diagnostic method of it, but facing great pressure such as the increasing workload and decreasing working accuracy rate. Therefore, radiology needs to find a way out to better realize the clinical value of vascular calcification. Artificial intelligence (AI) encompasses any algorithm imitating human intelligence. AI has shown great potential in image analysis, such as its high speed and accuracy, becoming the savior of the current situation. In order to promote more rational utilization, the role and progress of AI in this field were reviewed.

Methods

A search was conducted in PubMed and Web of Science. The key words included "artificial intelligence", "machine learning", "deep learning", and "vascular calcification". The qualitative analysis of literature was achieved through repeated deliberation after refining valuable content. The theme is the role and progress of AI in the diagnostic radiology of vascular calcification.

Key Content and Findings

Sixty-two articles were included. AI has been applied to the diagnostic radiology of 5 types of vascular calcification, including coronary artery calcification (CAC), thoracic aortic calcification (TAC), abdominal aortic calcification (AAC), carotid artery calcification, and breast artery calcification (BAC). Deep learning (DL), the latest technology in this field has been well applied and satisfactorily performed. Radiologists have been able to achieve efficient diagnosis of 5 types of vascular calcification through AI, with reliable accuracy.

Conclusions

Increasingly, advanced AI has achieved an accuracy comparable to that of human experts, with a faster speed. Moreover, the ability to reduce noise and artifacts enables more imaging equipment to obtain reliable quantification. AI has acquired the ability to cooperate with radiology departments in future work. However, the research in AAC and carotid artery calcification can be more in-depth, and more types of vascular calcification and more fields of radiology should be expanded to. The interpretation of results made by AI and the promotion of existing achievements to the development of other disciplines are also the focus in future.

Integrated head and neck imaging of symptomatic patients with stroke using simultaneous non-contrast cardiovascular magnetic resonance angiography and intraplaque hemorrhage imaging as compared with digital subtraction angiography

BACKGROUND

Both stenosis rate and intraplaque hemorrhage (IPH) are important predictors of stroke risk. Simultaneous non-contrast angiography and intraplaque hemorrhage (SNAP) cardiovascular magnetic resonance (CMR) imaging can detect both stenosis rate and IPH. We aimed to evaluate consistency between SNAP and digital subtraction angiography (DSA) to assess symptomatic patients with stroke and explore the performance of SNAP to identify IPH and the clinical factors associated with IPH.

METHODS

Eighty-one symptomatic patients with stroke, admitted to Wuhan Union Hospital who underwent CMR high-resolution vessel wall imaging (HR-VWI) and SNAP, were retrospectively identified. For patients who received interventional therapy, the imaging functions of SNAP and HR-VWI were compared with DSA. The diameters of the intracranial and carotid vessels were measured, and stenotic vessels were identified. The consistency of SNAP and HR-VWI in identifying IPH was also examined, and the correlations between IPH and clinical factors were analyzed.

RESULTS

SNAP was more consistent with DSA than HR-VWI in measuring vascular stenosis (intraclass correlation coefficient [ICC]SNAP-DSA = 0.917, ICC HR-VWI-DSA = 0.878). Regarding the diameter measurements of each intracranial and carotid vessel segment, SNAP was superior or similar to HR-VWI, and both were consistent with DSA in the measurement of major intracranial vascular segments. HR-VWI and SNAP exhibited acceptable agreement in identifying IPH (Kappa = 0.839, 95% confidence interval [CI]: 0.704-0.974). Patients who underwent interventional therapy had a higher plaque burden (P < 0.001). Patients with IPH had lower levels of high-density lipoprotein cholesterol (HDL) (P = 0.038) and higher levels of blood glucose (P = 0.007) and cystatin C (P = 0.040).

CONCLUSIONS

CMR SNAP is consistent with DSA in measuring vessel diameters and identifying atherosclerosis stenosis in each intracranial and carotid vessel segment. SNAP is also a potential alternative to HR-VWI in identifying stenosis and IPH.

Accelerated Two-Point Dixon MR Angiography Improves Diagnostic Performance for Cervical Artery Diseases

BACKGROUND

Nonenhanced MR angiography (MRA) studies are often used to manage acute and chronic large cervical artery disease, but lengthy scan times limit their clinical usefulness.

PURPOSE

To develop an accelerated cervical MRA and test its diagnostic performance.

STUDY TYPE

Prospective.

POPULATION

Patients with cervical artery disease (n = 32, 17 males).

FIELD STRENGTH/SEQUENCE

3.0 T; accelerated two-point Dixon three-dimensional Cartesian spoiled gradient-echo (FLEXA) and conventional time-of-flight MRA (cMRA) sequences.

ASSESSMENT

All patients underwent FLEXA (1'28″) and cMRA (6'47″) acquisitions. Quantitative evaluation (artery-to-background signal ratio and a blur metric) and qualitative evaluation using diagnostic performance measured by the sensitivity, specificity, and positive/negative predictive values (PPV/NPV), and vessel and plaque visualization scores from three board-certified radiologists' (with 10, 11, and 12 years of experience) independent readings using maximum intensity projection (MIP) for luminal diseases and axial images for plaque. The reference standards were contrast-enhanced angiography and fat-saturated T1-weighted images, respectively.

STATISTICAL TESTS

All measures were compared between FLEXA and cMRA using the paired t, Wilcoxon signed-rank, McNemar's, or chi-squared test, as appropriate. Interreader agreement was assessed using Cohen's κ. P < 0.05 was considered statistically significant.

RESULTS

The artery-to-background signal ratio was significantly higher for FLEXA (FLEXA: 7.20 ± 1.63 [fat]; 4.26 ± 0.52 [muscle]; cMRA: 2.57 ± 0.49 [fat]), while image blurring was significantly less (FLEXA: 0.24 ± 0.016; cMRA: 0.30 ± 0.029). In luminal disease detection, sensitivity (FLEXA: 0.97/0.91/0.91; cMRA:0.71/0.69/0.63), specificity (FLEXA: 0.98/0.93/0.98; cMRA:0.93/0.85/0.92), PPV (FLEXA: 0.92/0.86/0.86; cMRA: 0.64/0.5/0.58), and NPV (FLEXA: 0.99/0.98/0.98; cMRA: 0.92/0.91/0.9) were significantly higher for FLEXA. interreader agreement was substantial to almost perfect for FLEXA (κ = 0.82/0.86/0.78) and moderate to substantial for cMRA (κ = 0.67/0.56/0.57). MIP visualization scores were significantly higher for FLEXA, with substantial to almost perfect interreader agreement (FLEXA: κ = 0.83/0.86/0.82; cMRA: κ = 0.89/0.79/0.79). In plaque detection, sensitivity (FLEXA: 0.9/0.9/0.7; cMRA: 0.3/0.6/0.2) and specificity (FLEXA: 1/0.87/1; cMRA: 0.93/0.63/0.97) were significantly higher for FLEXA in two of three readers. The interreader plaque detection agreement was fair to substantial (FLEXA: κ = 0.63/0.69/0.48; cMRA: κ = 0.21/0.45/0.20). Side-by-side plaque and vessel wall visualization was superior for FLEXA in all readers, with moderate to substantial interreader agreement (plaque: κ = 0.73/0.73/0.77; vessel wall: κ = 0.57/0.40/0.39).

DATA CONCLUSION

FLEXA enhanced visualization of the cervical arterial system and improved diagnostic performance for luminal abnormalities and plaques in patients with cervical artery diseases.

LEVEL OF EVIDENCE

1 TECHNICAL EFFICACY STAGE: 2.

SCMR Position Paper (2020) on clinical indications for cardiovascular magnetic resonance

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.

Accelerated acquisition of carotid MR angiography using 3D gradient-echo imaging with two-point Dixon

This pilot study tests the feasibility of rapid carotid MR angiography using the liver acquisition with volume acceleration-flex technique (LAVA MRA). Seven healthy volunteers and 21 consecutive patients suspected of carotid stenosis underwent LAVA and conventional time-of-flight (cTOF) MRAs. Artery-to-fat and artery-to-muscle signal intensity ratios were manually measured. LAVA MRA exhibited a significantly larger artery-to-fat signal intensity ratio compared with cTOF MRA in all slices (P < 0.001) and exhibited a larger (P < 0.001) or equivalent (P = 1.0) artery-to-muscle signal intensity ratio in the extracranial carotid arteries. The image quality of the cervical carotid bifurcation and the signal change on each MRA were visually assessed and compared among the MRAs. There was no significant difference between the two MRAs in visual assessment. LAVA MRA can provide visualization similar to cTOF MRA in the evaluation of the cervical carotid bifurcation while reducing scan time by one-fifth.

Simultaneous non-contrast angiography and intraplaque haemorrhage (SNAP) imaging for cervical artery dissections

AIM

To evaluate the feasibility of high-resolution magnetic resonance imaging (MRI) with a simultaneous non-contrast angiography and intraplaque haemorrhage (SNAP) sequence in identifying cervical artery dissections (CeAD).

MATERIALS AND METHODS

Fifty-three patients with suspected CeAD underwent the SNAP sequence (including non-contrast magnetic resonance angiography [MRA] and heavy T1-weighting vessel wall images simultaneously in a single scan) and conventional MRI sequences (including three-dimensional [3D] time-of-flight MRA and T1-weighted black-blood imaging [T1W BB]) and cervical vascular ultrasound (CVUS). In diagnosis of CeAD, the diagnostic sensitivity and specificity of SNAP, and the diagnostic coherence between SNAP and conventional sequences and between SNAP and CVUS was analysed. At follow-up, the absolute signal (AS) and signal index (SI) of the intramural haematoma (IMH) between vessel wall images on SNAP and T1W-BB images were compared. The image quality of SNAP was analysed by comparing the signal-to-noise ratio (SNR) between vessel wall images from the SNAP and T1W-BB sequences.

RESULTS

The SNAP sequence was found to provide good performance in the diagnosis of CeAD (sensitivity 72.2%, specificity 98.2%); good agreement was found between SNAP and conventional sequences (Cohen's κ=0.76, p<0.05); and excellent agreement was found between SNAP and CVUS (Cohen's κ=0.83, p<0.05). There was no significant difference between AS or SI of the IMH of the vessel wall images within the SNAP and T1W-BB sequences during the review. The SNAP sequence had higher SNR of the IMH compared to T1W-BB, T2W-BB, proton-density-weighted volume isotropic turbo-spin-echo acquisition imaging (PD-VISTA) sequences (p<0.05).

CONCLUSION

The SNAP sequence holds the potential to be preferred choice for screening of patients with a high suspicion of CeAD and for the follow-up of IMH after treatment.

Plaque components segmentation in carotid artery on simultaneous non-contrast angiography and intraplaque hemorrhage imaging using machine learning

PURPOSE

This study sought to determine the feasibility of using Simultaneous Non-contrast Angiography and intraPlaque Hemorrhage (SNAP) to detect the lipid-rich/necrotic core (LRNC), and develop a machine learning based algorithm to segment plaque components on SNAP images.

METHODS

Sixty-eight patients (age: 58±9 years, 24 males) with carotid artery atherosclerotic plaque were imaged on a 3 T MR scanner with both traditional multi-contrast vessel wall MR sequences (TOF, T1W, and T2W) and 3D SNAP sequence. The manual segmentations of carotid plaque components including LRNC, intraplaque hemorrhage (IPH), calcification (CA) and fibrous tissue (FT) on traditional multi-contrast images were used as reference. By utilizing the intensity and morphological information from SNAP, a machine learning based two steps algorithm was developed to firstly identify LRNC (with or without IPH), CA and FT, and then segmented IPH from LRNC. Ten-fold cross-validation was used to evaluate the performance of proposed method. The overall pixel-wise accuracy, the slice-wise sensitivity & specificity & Youden's index, and the Pearson's correlation coefficient of the component area between the proposed method and the manual segmentation were reported.

RESULTS

In the first step, all tested classifiers (Naive Bayes (NB), Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting Decision Tree (GBDT) and Artificial Neural Network (ANN)) had overall pixel-wise accuracy higher than 0.88. For RF, GBDT and ANN classifiers, the correlation coefficients of areas were all higher than 0.82 (p < 0.001) for LRNC and 0.79 for CA (p < 0.001), and the Youden's indexes were all higher than 0.79 for LRNC and 0.76 for CA, which were better than that of NB and SVM. In the second step, the overall pixel-wise accuracy was higher than 0.78 for the five classifiers, and RF achieved the highest Youden's index (0.69) with the correlation coefficients as 0.63 (p < 0.001).

CONCLUSIONS

The RF is the overall best classifier for our proposed method, and the feasibility of using SNAP to identify plaque components, including LRNC, IPH, CA, and FT has been validated. The proposed segmentation method using a single SNAP sequence might be a promising tool for atherosclerotic plaque components assessment.