Potential of Photon-Counting Detector CT for Radiation Dose Reduction for the Assessment of Interstitial Lung Disease in Patients With Systemic Sclerosis

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.

Computed tomography radiomics for the prediction of thymic epithelial tumor histology, TNM stage and myasthenia gravis

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.

Accuracy of dynamic three-dimensional magnetic resonance perfusion imaging for the detection of coronary artery disease in patients with reduced ejection fraction

Background

To assess the accuracy of 3D cardiovascular magnetic resonance (CMR) perfusion imaging for the detection of coronary artery disease (CAD) against fractional flow reserve (FFR) and quantitative coronary angiography (QCA) in patients with reduced ejection fraction (EF).

Methods

Out of 447 patients who underwent 3D CMR perfusion imaging (at 1.5 and 3.0 T under adenosine stress and at rest) at 5 European centers, 86 cases with an EF ≤50% were identified (mean age 64 ± 11 yrs, 80% male). Significant CAD was defined as a FFR value <0.8 and a QCA >50%. 86 individuals matched for age, gender and major cardiovascular risk factors, were chosen as the control group.

Results

The prevalence of CAD defined by FFR (<0.8) was 59% (EF≤50%) vs. 54% (EF>50%), P = 0.4). In relation to FFR, 3D perfusion imaging yielded a sensitivity of 84.5% (95% CI 76.0–90.4) and specificity of 77.3% (95% CI 66.7–85.3). The sensitivity of perfusion imaging was higher in patients with an EF≤50% (90.2 vs. 78.3%, P = 0.1) whereas specificity showed the reverse (62.9 vs. 90.0%, P = 0.005) The diagnostic accuracy was comparable in both subgroups (AUC 79.1 vs. 83.7%, P = 0.25). According to QCA, the prevalence of CAD was 78 vs. 72% (P = 0.4). Perfusion imaging yielded a sensitivity and specificity of 82.1 vs. 62.9%, P = 0.01 and 79.0 vs. 95.8%, P = 0.09 respectively with a high diagnostic accuracy in both subgroups (AUC 82.0 vs. 80.5%).

Conclusion

3D-CMR perfusion imaging yields a high sensitivity and diagnostic accuracy with regards to the detection of significant CAD irrespective of left ventricular (LV) systolic function.

Can magnetic resonance imaging radiomics of the pancreas predict postoperative pancreatic fistula?

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.

Prognostic value of texture analysis from cardiac magnetic resonance imaging in patients with Takotsubo syndrome: a machine learning based proof-of-principle approach

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.

Machine learning-based CT fractional flow reserve assessment in acute chest pain: first experience

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.

Multimodal Multiparametric Three-dimensional Image Fusion in Coronary Artery Disease: Combining the Best of Two Worlds

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.

Radiomics for Distinguishing Myocardial Infarction from Myocarditis at Late Gadolinium Enhancement at MRI: Comparison with Subjective Visual Analysis

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, 2019Supplemental material is available for this article.

Cardiac magnetic resonance imaging to detect ischemia in chronic coronary syndromes: state of the art

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.

The potential of machine learning to predict postoperative pancreatic fistula based on preoperative, non-contrast-enhanced CT: A proof-of-principle study

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.

Right coronary artery motion analysis: a novel method to measure right ventricular systolic function by selective coronary angiography

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, r² = 0.35; for RCA speed: r = 0.40, p < 0.001, r² = 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 .

Texture analysis of myocardial infarction in CT: Comparison with visual analysis and impact of iterative reconstruction

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.

Cardiovascular magnetic resonance T2* mapping for structural alterations in hypertrophic cardiomyopathy

•

HCM patients exhibited significantly decreased T2* values compared to controls.

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Within HCM patients, those with myocardial fibrosis presented with decreased T2* values.

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T2* provided good diagnostic accuracy to diagnose HCM with fibrosis.

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T2* may add information for identifying a higher risk sub-group of HCM patients.

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.

Quantitative Analysis of Vortical Blood Flow in the Thoracic Aorta Using 4D Phase Contrast MRI

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 ω→=∇v→ 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.

Flow measurement by cardiovascular magnetic resonance: a multi-centre multi-vendor study of background phase offset errors that can compromise the accuracy of derived regurgitant or shunt flow measurements

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.