Sarcopenia as a Predictive Factor for Recurrence of Hepatocellular Carcinoma Following Radiofrequency Ablation

BACKGROUND

Sarcopenia is a skeletal muscle mass deficiency and a potential prognostic factor for the recurrence of hepatocellular carcinoma (HCC).

OBJECTIVE

To determine whether sarcopenia correlates with the recurrence rate of HCC after curative radiofrequency ablation (RFA) in early and very early HCC.

METHODS

We retrospectively reviewed 669 HCC patients who underwent their first curative RFA at Siriraj hospital from 2011 to 2020. Fifty-six patients who were diagnosed with HCC by triple-phase CT scan and had complete response on follow-up CT were included. All patients underwent skeletal muscle index (SMI) assessment at level L3 vertebra and sarcopenia was defined by the cut-off values of 52.4 cm2/m2 for men and 38.5 cm2/m2 for women. We compared patients with and without sarcopenia. Time to recurrence was evaluated by the Kaplan-Meier method. Univariate and multivariate Cox regression analysis was performed.

RESULTS

Sarcopenia was present in 37 of 56 patients (66.1%). There was no significant difference between groups except body mass index (BMI) (P<0.001) and serum alanine aminotransferase (ALT) (P=0.035). There was a promising result indicating the difference of time to recurrence between each group (P=0.046) and potential association of sarcopenia with HCC recurrence (HR=2.06; P=0.052). The Child-Pugh score and tumor number were independent risk factors for HCC recurrence (HR=2.04; P=0.005 and HR=2.68; P=0.017, respectively).

CONCLUSION

Sarcopenia is a potential prognostic factor for recurrence of HCC in Thai patients who underwent RFA. A larger study is required to properly confirm this association.

Tumor-to-bone distance and radiomic features on MRI distinguish intramuscular lipomas from well-differentiated liposarcomas

Background

To develop a machine learning model based on tumor-to-bone distance and radiomic features derived from preoperative MRI images to distinguish intramuscular (IM) lipomas and atypical lipomatous tumors/well-differentiated liposarcomas (ALTs/WDLSs) and compared with radiologists.

Methods

The study included patients with IM lipomas and ALTs/WDLSs diagnosed between 2010 and 2022, and with MRI scans (sequence/field strength: T1-weighted (T1W) imaging at 1.5 or 3.0 Tesla MRI). Manual segmentation of tumors based on the three-dimensional T1W images was performed by two observers to appraise the intra- and interobserver variability. After radiomic features and tumor-to-bone distance were extracted, it was used to train a machine learning model to distinguish IM lipomas and ALTs/WDLSs. Both feature selection and classification steps were performed using Least Absolute Shrinkage and Selection Operator logistic regression. The performance of the classification model was assessed using a tenfold cross-validation strategy and subsequently evaluated using the receiver operating characteristic curve (ROC) analysis. The classification agreement of two experienced musculoskeletal (MSK) radiologists was assessed using the kappa statistics. The diagnosis accuracy of each radiologist was evaluated using the final pathological results as the gold standard. Additionally, we compared the performance of the model and two radiologists in terms of the area under the receiver operator characteristic curves (AUCs) using the Delong’s test.

Results

There were 68 tumors (38 IM lipomas and 30 ALTs/WDLSs). The AUC of the machine learning model was 0.88 [95% CI 0.72–1] (sensitivity, 91.6%; specificity, 85.7%; and accuracy, 89.0%). For Radiologist 1, the AUC was 0.94 [95% CI 0.87–1] (sensitivity, 97.4%; specificity, 90.9%; and accuracy, 95.0%), and as to Radiologist 2, the AUC was 0.91 [95% CI 0.83–0.99] (sensitivity, 100%; specificity, 81.8%; and accuracy, 93.3%). The classification agreement of the radiologists was 0.89 of kappa value (95% CI 0.76–1). Although the AUC of the model was lower than of two experienced MSK radiologists, there was no statistically significant difference between the model and two radiologists (all P > 0.05).

Conclusions

The novel machine learning model based on tumor-to-bone distance and radiomic features is a noninvasive procedure that has the potential for distinguishing IM lipomas from ALTs/WDLSs. The predictive features that suggested malignancy were size, shape, depth, texture, histogram, and tumor-to-bone distance.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13018-023-03718-4.

Multi-level pooling encoder-decoder convolution neural network for MRI reconstruction

MRI reconstruction is one of the critical processes of MRI machines, along with the acquisition. Due to a slow processing time of signal acquiring, parallel imaging and reconstruction techniques are applied for acceleration. To accelerate the acquisition process, fewer raw data are sampled simultaneously with all RF coils acquisition. Then, the reconstruction uses under-sampled data from all RF coils to restore the final MR image that resembles the fully sampled MR image. These processes have been a traditional procedure inside the MRI system since the invention of the multi-coils MRI machine. This paper proposes the deep learning technique with a lightweight network. The deep neural network is capable of generating the high-quality reconstructed MR image with a high peak signal-to-noise ratio (PSNR). This also opens a high acceleration factor for MR data acquisition. The lightweight network is called Multi-Level Pooling Encoder-Decoder Net (MLPED Net). The proposed network outperforms the traditional encoder-decoder networks on 4-fold acceleration with a significant margin on every evaluation metric. The network can be trained end-to-end, and it is a lightweight structure that can reduce training time significantly. Experimental results are based on a publicly available MRI Knee dataset from the fastMRI competition.

A clinical evaluation study of cardiothoracic ratio measurement using artificial intelligence

Background

Artificial intelligence, particularly the deep learning (DL) model, can provide reliable results for automated cardiothoracic ratio (CTR) measurement on chest X-ray (CXR) images. In everyday clinical use, however, this technology is usually implemented in a non-automated (AI-assisted) capacity because it still requires approval from radiologists. We investigated the performance and efficiency of our recently proposed models for the AI-assisted method intended for clinical practice.

Methods

We validated four proposed DL models (AlbuNet, SegNet, VGG-11, and VGG-16) to find the best model for clinical implementation using a dataset of 7517 CXR images from manual operations. These models were investigated in single-model and combined-model modes to find the model with the highest percentage of results where the user could accept the results without further interaction (excellent grade), and with measurement variation within ± 1.8% of the human-operating range. The best model from the validation study was then tested on an evaluation dataset of 9386 CXR images using the AI-assisted method with two radiologists to measure the yield of excellent grade results, observer variation, and operating time. A Bland–Altman plot with coefficient of variation (CV) was employed to evaluate agreement between measurements.

Results

The VGG-16 gave the highest excellent grade result (68.9%) of any single-model mode with a CV comparable to manual operation (2.12% vs 2.13%). No DL model produced a failure-grade result. The combined-model mode of AlbuNet + VGG-11 model yielded excellent grades in 82.7% of images and a CV of 1.36%. Using the evaluation dataset, the AlbuNet + VGG-11 model produced excellent grade results in 77.8% of images, a CV of 1.55%, and reduced CTR measurement time by almost ten-fold (1.07 ± 2.62 s vs 10.6 ± 1.5 s) compared with manual operation.

Conclusion

Due to its excellent accuracy and speed, the AlbuNet + VGG-11 model could be clinically implemented to assist radiologists with CTR measurement.

An automated liver segmentation in liver iron concentration map using fuzzy c-means clustering combined with anatomical landmark data

Background

To estimate median liver iron concentration (LIC) calculated from magnetic resonance imaging, excluded vessels of the liver parenchyma region were defined manually. Previous works proposed the automated method for excluding vessels from the liver region. However, only user-defined liver region remained a manual process. Therefore, this work aimed to develop an automated liver region segmentation technique to automate the whole process of median LIC calculation.

Methods

553 MR examinations from 471 thalassemia major patients were used in this study. LIC maps (in mg/g dry weight) were calculated and used as the input of segmentation procedures. Anatomical landmark data were detected and used to restrict ROI. After that, the liver region was segmented using fuzzy c-means clustering and reduced segmentation errors by morphological processes. According to the clinical application, erosion with a suitable size of the structuring element was applied to reduce the segmented liver region to avoid uncertainty around the edge of the liver. The segmentation results were evaluated by comparing with manual segmentation performed by a board-certified radiologist.

Results

The proposed method was able to produce a good grade output in approximately 81% of all data. Approximately 11% of all data required an easy modification step. The rest of the output, approximately 8%, was an unsuccessful grade and required manual intervention by a user. For the evaluation matrices, percent dice similarity coefficient (%DSC) was in the range 86–92, percent Jaccard index (%JC) was 78–86, and Hausdorff distance (H) was 14–28 mm, respectively. In this study, percent false positive (%FP) and percent false negative (%FN) were applied to evaluate under- and over-segmentation that other evaluation matrices could not handle. The average of operation times could be reduced from 10 s per case using traditional method, to 1.5 s per case using our proposed method.

Conclusion

The experimental results showed that the proposed method provided an effective automated liver segmentation technique, which can be applied clinically for automated median LIC calculation in thalassemia major patients.

Cardiothoracic ratio measurement using artificial intelligence: observer and method validation studies

Background

Artificial Intelligence (AI) is a promising tool for cardiothoracic ratio (CTR) measurement that has been technically validated but not clinically evaluated on a large dataset. We observed and validated AI and manual methods for CTR measurement using a large dataset and investigated the clinical utility of the AI method.

Methods

Five thousand normal chest x-rays and 2,517 images with cardiomegaly and CTR values, were analyzed using manual, AI-assisted, and AI-only methods. AI-only methods obtained CTR values from a VGG-16 U-Net model. An in-house software was used to aid the manual and AI-assisted measurements and to record operating time. Intra and inter-observer experiments were performed on manual and AI-assisted methods and the averages were used in a method variation study. AI outcomes were graded in the AI-assisted method as excellent (accepted by both users independently), good (required adjustment), and poor (failed outcome). Bland–Altman plot with coefficient of variation (CV), and coefficient of determination (R-squared) were used to evaluate agreement and correlation between measurements. Finally, the performance of a cardiomegaly classification test was evaluated using a CTR cutoff at the standard (0.5), optimum, and maximum sensitivity.

Results

Manual CTR measurements on cardiomegaly data were comparable to previous radiologist reports (CV of 2.13% vs 2.04%). The observer and method variations from the AI-only method were about three times higher than from the manual method (CV of 5.78% vs 2.13%). AI assistance resulted in 40% excellent, 56% good, and 4% poor grading. AI assistance significantly improved agreement on inter-observer measurement compared to manual methods (CV; bias: 1.72%; − 0.61% vs 2.13%; − 1.62%) and was faster to perform (2.2 ± 2.4 secs vs 10.6 ± 1.5 secs). The R-squared and classification-test were not reliable indicators to verify that the AI-only method could replace manual operation.

Conclusions

AI alone is not yet suitable to replace manual operations due to its high variation, but it is useful to assist the radiologist because it can reduce observer variation and operation time. Agreement of measurement should be used to compare AI and manual methods, rather than R-square or classification performance tests.

Diffusion-weighted magnetic resonance imaging for the assessment of liver fibrosis in chronic viral hepatitis

Background

Accurate noninvasive methods for the assessment of liver fibrosis are urgently needed. This prospective study evaluated the diagnostic accuracy of diffusion-weighted magnetic resonance imaging (DWI) for the staging of liver fibrosis and proposed a diagnostic algorithm using DWI to identify cirrhosis in patients with chronic viral hepatitis.

Methods

One hundred twenty-one treatment-naïve patients with chronic hepatitis B or C were evaluated with DWI followed by liver biopsy on the same day. Breath-hold single-shot echo-planar DWI was performed to measure the apparent diffusion coefficient (ADC) of the liver and spleen. Normalized liver ADC was calculated as the ratio of liver ADC to spleen ADC.

Results

There was an inverse correlation between fibrosis stage and normalized liver ADC (p<0.05). For the prediction of fibrosis stage ≥2, stage ≥3, and cirrhosis, the area under the receiver-operating curve of normalized liver ADC was 0.603, 0.704, and 0.847, respectively. The normalized liver ADC value ≤1.02×10⁻³ mm²/s had 88% sensitivity, 81% specificity, 25% positive predictive value (PPV), and 99% negative predictive value (NPV) for the diagnosis of cirrhosis. Using a sequential approach with the Fibrosis-4 index followed by DWI, normalized liver ADC ≤1.02×10⁻³ mm²/s in patients with Fibrosis-4 >3.25 yielded an 80% PPV for cirrhosis, and a 100% NPV to exclude cirrhosis in patients with Fibrosis-4 between 1.45 and 3.25. Only 15.7% of patients would require a liver biopsy. This sequential strategy can reduce DWI examinations by 53.7%.

Conclusion

Normalized liver ADC measurement on DWI is an accurate and noninvasive tool for the diagnosis of cirrhosis in patients with chronic viral hepatitis.

COVID-19 detection and heatmap generation in chest x-ray images

Purpose: The outbreak of COVID-19 or coronavirus was first reported in 2019. It has widely and rapidly spread around the world. The detection of COVID-19 cases is one of the important factors to stop the epidemic, because the infected individuals must be quarantined. One reliable way to detect COVID-19 cases is using chest x-ray images, where signals of the infection are located in lung areas. We propose a solution to automatically classify COVID-19 cases in chest x-ray images. Approach: The ResNet-101 architecture is adopted as the main network with more than 44 millions parameters. The whole net is trained using the large size of 1500 × 1500 x-ray images. The heatmap under the region of interest of segmented lung is constructed to visualize and emphasize signals of COVID-19 in each input x-ray image. Lungs are segmented using the pretrained U-Net. The confidence score of being COVID-19 is also calculated for each classification result. Results: The proposed solution is evaluated based on COVID-19 and normal cases. It is also tested on unseen classes to validate a regularization of the constructed model. They include other normal cases where chest x-ray images are normal without any disease but with some small remarks, and other abnormal cases where chest x-ray images are abnormal with some other diseases containing remarks similar to COVID-19. The proposed method can achieve the sensitivity, specificity, and accuracy of 97%, 98%, and 98%, respectively. Conclusions: It can be concluded that the proposed solution can detect COVID-19 in a chest x-ray image. The heatmap and confidence score of the detection are also demonstrated, such that users or human experts can use them for a final diagnosis in practical usages.

An Automated Segmentation of R2* Iron-Overloaded Liver Images Using a Fuzzy C-Mean Clustering Scheme

OBJECTIVES

The objectives of this study were to develop and test an automated segmentation of R2* iron-overloaded liver images using fuzzy c-mean (FCM) clustering and to evaluate the observer variations.

MATERIALS AND METHODS

Liver R2* images and liver iron concentration (LIC) maps of 660 thalassemia examinations were randomly separated into training (70%) and testing (30%) cohorts for development and evaluation purposes, respectively. Two-dimensional FCM used R2* images, and the LIC map was implemented to segment vessels from the parenchyma. Two automated FCM variables were investigated using new echo time and membership threshold selection criteria based on the FCM centroid distance and LIC levels, respectively. The new method was developed on a training cohort and compared with manual segmentation for segmentation accuracy and to a previous semiautomated method, and a semiautomated scheme was suggested to improve unsuccessful results. The automated variables found from the training cohort were assessed for their effectiveness in the testing cohort, both quantitatively and qualitatively (the latter by 2 abdominal radiologists using a grading method, with evaluations of observer variations). A segmentation error of less than 30% was considered to be a successful result in both cohorts, whereas, in the testing cohort, a good grade obtained from satisfactory automated results was considered a success.

RESULTS

The centroid distance method has a segmentation accuracy comparable with the previous-best, semiautomated method. About 94% and 90% of the examinations in the training and testing cohorts were automatically segmented out successfully, respectively. The failed examinations were successfully segmented out with thresholding adjustment (3% and 8%) or by using alternative results from the previous 1-dimensional FCM method (3% and 2%) in the training and testing cohorts, respectively. There were no failed segmentation examinations in either cohort. The intraobserver and interobserver variabilities were found to be in substantial agreement.

CONCLUSIONS

Our new method provided a robust automated segmentation outcome with a high ease of use for routine clinical application.

Prevalence and predictors of cardiac and liver iron overload in patients with thalassemia: A multicenter study based on real-world data

Prevalence of cardiac and liver iron overload in patients with thalassemia in real-world practice may vary among different regions especially in the era of widely-used iron chelation therapy. The aim of this study was to determine the prevalence of cardiac and liver iron overload in and the management patterns of patients with thalassemia in real-world practice in Thailand. We established a multicenter registry for patients with thalassemia who underwent magnetic resonance imaging (MRI) as part of their clinical evaluation. All enrolled patients underwent cardiac and liver MRI for assessment of iron overload. There were a total of 405 patients enrolled in this study. The mean age of patients was 18.8±12.5years and 46.7% were male. Two hundred ninety-six (73.1%) of patients received regular blood transfusion. Prevalence of cardiac iron overload (CIO) and liver iron overload (LIO) was 5.2% and 56.8%, respectively. Independent predictors for iron overload from laboratory information were serum ferritin and transaminase for both CIO and LIO. Serum ferritin can be used as a screening tool to rule-out CIO and to diagnose LIO. Iron chelation therapy was given in 74.6%; 15.3% as a combination therapy.

Improved R2* liver iron concentration assessment using a novel fuzzy c-mean clustering scheme

BACKGROUND

In thalassemia patients, R2* liver iron concentration (LIC) measurement is a common clinical tool for assessing iron overload and for determining necessary chelator dose and evaluating its efficacy. Despite the importance of accurate LIC measurement, existing methods suffer from LIC variability, especially at the severe iron overload range due to inclusion of vessel parts in LIC calculation. In this study, we build upon previous Fuzzy C-Mean (FCM) clustering work to formulate a scheme with superior performance in segmenting vessel pixels from the parenchyma. Our method (MIX-FCM) combines our novel 2D-FCM with the existing 1D-FCM algorithm. This study further assessed possible optimal clustering parameters (OP scheme) and proposed a semi-automatic (SA) scheme for routine clinical application.

METHODS

Segmentation of liver parenchyma and vessels was performed on T2* images and their LIC maps in 196 studies from 147 thalassemia major patients. We used manual segmentation as the reference. 1D-FCM clustering was performed on the acquired image alone and 2D-FCM used both the acquired image and its LIC data. To execute the MIX-FCM method, the best outcome (OP-MIX-FCM) was selected from the aforementioned methods and was compared to the SA-MIX-FCM scheme. We used the percent value of the normalized interquartile range (nIQR) to its median to evaluate the variability of all methods.

RESULTS

2D-FCM clustering is more effective than 1D-FCM clustering at the severe overload range only, but inferior for other ranges (where 1D-FCM provides suitable results). This complementary performance between the two methods allows MIX-FCM to improve results for all ranges. OP-MIX-FCM clustering error was 2.1 ± 2.3%, compared with 10.3 ± 9.9% and 7.0 ± 11.9% from 1D- and 2D-FCM clustering, respectively. SA-MIX-FCM result was comparable to OP-MIX-FCM result, with both schemes showing ability to decrease overall nIQR by approximately 30%.

CONCLUSION

Our proposed 2D-FCM algorithm is not as superior to 1D-FCM as hypothesized. In contrast, our MIX-FCM method benefits from the best of both methods to obtain the highest segmentation accuracy at all ranges. Moreover, segmentation accuracy of the practical scheme (SA-MIX-FCM) is comparable to segmentation accuracy of the reference scheme (OP-MIX-FCM). Finally, we confirmed that segmentation is crucial to improving LIC assessments, especially at the severe iron overload range.

Benefit of double contrast MRI in diagnosis of hepatocellular carcinoma in patients with chronic liver diseases

OBJECTIVE

To assess the benefit on diagnosis of hepatocellular carcinoma (HCC) in patients with chronic liver disease or cirrhosis with double contrast MR imaging compared to the routine gadolinium-based MR imaging.

MATERIAL AND METHOD

Seventy-one consecutive patients with cirrhosis or chronic hepatitis underwent multiphase, gadolinium-enhanced liver MRI examination and sequentially superparamagnetic iron oxide (SPIO)-enhanced images. The presence signal intensities of lesions on non-contrast sequences, dynamic gadolinium-enhanced images and delayed 10-min post-SPIO T2*-weighted images were recorded.

RESULTS

Among 27 patients, 15 HCCs from 12 patients were diagnosed by surgical (n = 7) and non-surgical (n = 8) proofs. The overall sensitivity, specificity, positive predictive value, and negative predictive value of double contrast-enhanced images in 12 patients were 83.3% (95% CI: 58.5, 96.2), 33.3% (95% CI: 5.4, 88.4), 88.2% (95% CI: 63.5, 98.2), and 25% (95% CI: 4.1, 79.6) and these of gadolinium-enhanced images were 72.2% (95% CI: 46.5, 90.2), 33.3% (95% CI: 5.4, 88.4), 86.6% (95% CI: 59.5, 97.9), and 16.6% (95% CI: 2.7, 63.9), respectively. There were two benign hepatic nodules (1 adenoma, 1 dysplastic nodule) suspected as HCCs on MR images and two surgically proven-HCCs, invisible on gadolinium-enhanced images, detected as defect on only delayed 10-min post-SPIO T2*-weighted images.

CONCLUSION

SPIO-enhanced images in double contrast-enhanced MR imaging had an additional value on HCC detection, compared to gadolinium-enhanced MR imaging, in patients with chronic liver disease or cirrhosis.

Prevalence and characters of anomalous coronary artery from coronary magnetic resonance angiography

BACKGROUND

Many types of anomalous coronary artery have been reported. Some forms of the anomaly are potentially malignant and can lead to sudden death.

OBJECTIVE

To determine the prevalence and characters of anomalous coronary artery, including the associations of myocardial ischemia.

MATERIAL AND METHOD

This is a retrospective study. The authors enrolled patients who were referred for cardiac magnetic resonance (CMR) and had magnetic resonance coronary angiography (MRCA) images. Imaging of the coronary arteries was acquired. The presence and patterns of anomalous coronary artery and the presence of myocardial ischemia was recorded. Myocardial perfusion study was also performed in most patients using adenosine stress test.

RESULTS

Anomalous coronary artery was detected in 56 out of 3,703 patients (1.51%). There were 24 men (42.9%). Average age was 62.1 +/- 15.0 years. Most common type was right coronary artery (RCA) from left coronary cusp. Malignant form was demonstrated in 31 patients (55.4%) and myocardial ischemia was detected in 10 patients (23.3%).

CONCLUSION

Prevalence of anomalous coronary artery was 1.5%. Most common types were RCA from left coronary cusp (30%) and high take-off RCA (30%).

Inter-site validations of the Pixel-Wise method for cardiac T2* analysis in transfusion-dependent Thai thalassemia patients

OBJECTIVE

To compare inter-site observer variability of the Pixel- Wise (PW) method for cardiac T2* analysis in thalassemia patients using the mono-exponential with a constant fitting (offset) model and to compare the cross-model variability of the offset model to the mono-exponential (typical) model.

MATERIAL AND METHOD

Eighty-eight cardiac T2* measurements were performed on 72 Thalassemia major patients. Both bright- and black-blood techniques were acquired and analyzed at both the reference (REF) and local (LOC) sites using the PW method by defined region of interest on the whole (at the REF site) and partial (at the LOC site) septum. The offset model was analyzed at the reference site while both the offset and typical models were performed at the local site. The inter-site variability of the T2* values were analyzed by independent observers blinded to the results.

RESULTS

The T2* values from the REF-offset, LOC-offset and LOC-typical methods were statistically comparable on both scanning techniques. The inter-site variations of the offset model were about 5.2% and 4.4% on the bright- and black-blood techniques, respectively, which was about 1.7% higher than from the intra-site, but was still in a reasonable range compared to the conventional method of around 5.4%. The cross-model comparisons presented with 0.4 ms of bias and variation of about 6.9% and 4.7%, respectively, which is about 1.4% higher than from the intra-site.

CONCLUSION

The observer variability on the PW method using the offset or typical model provided equivalent coefficient of variation on both scanning techniques, which was also comparable to the previous reports. The inter-site variability of the offset and cross models was also in a reasonable range, being less than 2% higher than the intra-site with bias of about 0.4 ms.

Late gadolinium enhancement from cardiac magnetic resonance in ischemic and non-ischemic cardiomyopathy

BACKGROUND

Diagnosis of coronary artery disease in patients with heart failure with systolic dysfunction usually requires coronary angiography. Cardiac magnetic resonance (CMR) is an accurate tool for the assessment of myocardial scar which may be the major cause of left ventricular systolic dysfunction.

OBJECTIVE

This study was to determine the prevalence and the difference in pattern of late gadolinium enhancement (LGE) between patients with ischemic (ICM) and non-ischemic cardiomyopathy (NICM).

MATERIAL AND METHOD

We enrolled 98 patients with heart failure and left ventricular systolic dysfunction with left ventricular ejection fraction less than 50%. Allpatients underwent CMR. CMR protocol included functional study and assessment of LGE. Left ventricular volume and ejection fraction was measured. The presence and extent of LGE including its pattern were assessed.

RESULTS

There were 58 patients with ICM and 40 patients with NICM. Patients with NICM had a lower left ventricular ejection fraction than those with ICM with a similar left ventricular wall thickness. LGE was detected in 53 patients with ICM (91.5%) and 10 patients with NICM (25%). LGE pattern was transmural or subendocardial pattern in patients with ICM and midwall scar in those with NICM.

CONCLUSION

The presence and pattern ofLGE can differentiate systolic heart failure from ICM and NICM.

Prevalence and prognosis of myocardial scar in patients with known or suspected coronary artery disease and normal wall motion

BACKGROUND

Some patients may have normal wall motion after myocardial infarction. The aim of this study was to determine the prevalence and prognosis of patients with myocardial scar in the absence of abnormal wall motion. We studied patients with suspected or known coronary artery disease (CAD) who were referred for cardiovascular magnetic resonance (CMR) for the assessment of global and regional cardiac function and late gadolinium enhancement (LGE) and had normal left ventricular wall motion. Prognostic value was determined by the occurrence of hard endpoints (cardiac death and nonfatal myocardial infarction) and major adverse cardiac events (MACE) which also included hospitalization due to unstable angina or heart failure or life threatening ventricular arrhythmia.

RESULTS

A total 1148 patients (70.3%) were studied. LGE was detected in 104 patients (9.1%). Prevalence of LGE increased in patients with increased left ventricular mass. Average follow-up time was 955 ± 542 days. LGE was the strongest predictor for hard endpoints and MACE.

CONCLUSION

LGE was detected in 9.1% of patients with suspected or known CAD and normal wall motion. LGE was the strongest predictor of significant cardiac events.

Comparison of diagnostic and prognostic value of different electrocardiographic criteria to delayed-enhancement magnetic resonance imaging for healed myocardial infarction

The accuracy of various electrocardiographic (ECG) criteria for the diagnosis of healed myocardial infarction (MI) has never been validated. The objective of this study was to determine the accuracy and prognostic value of standard ECG criteria for the diagnosis of healed MI compared with cardiac magnetic resonance (CMR). Consecutive patients with known or suspected coronary artery disease who were referred for CMR were studied. Twelve-lead electrocardiography and CMR were performed the same day. A standard CMR protocol including a delayed-enhancement (DE) technique was performed. The prognostic value of using various ECG criteria and DE-CMR was assessed for the occurrence of cardiac death, nonfatal MI, or major adverse cardiac events. We studied 1,366 patients. Average follow-up was 31.4 +/- 15.8 months. Myocardial scar was detected in 507 patients (37.1%) using DE-CMR. Healed MI using various ECG criteria had sensitivity, specificity, and accuracy of 44% to 59%, 91% to 95%, and 75% to 79% compared with DE-CMR, respectively. Multivariable Cox regression analysis showed that myocardial scar using DE-CMR was the most powerful predictor for cardiac events, followed by left ventricular ejection fraction. In the absence of DE-CMR data, MI using European Society of Cardiology/American College of Cardiology (ESC/ACC) 2000 criteria was the most powerful predictor. In conclusion, various ECG criteria had limited sensitivity, but high specificity, for the diagnosis of healed MI compared with myocardial scar using DE-CMR. Myocardial scar, left ventricular ejection fraction, and MI using ESC/ACC 2000 criteria were important predictors for cardiac events.

Comparison of cardiovascular magnetic resonance of late gadolinium enhancement and diastolic wall thickness to predict recovery of left ventricular function after coronary artery bypass surgery

BACKGROUND

The objective was to compare the value of late gadolinium enhancement (LGE) and end-diastolic wall thickness (EDWT) assessed by cardiovascular magnetic resonance (CMR) in predicting recovery of left ventricular function after coronary artery bypass surgery (CABG).

METHODS

We enrolled patients with coronary artery disease and left ventricular ejection fraction < 45% who were scheduled for CABG. Regional contractility was assessed by cine CMR at baseline and 4 months after CABG. EDWT and LGE were assessed at baseline. Predictors for improvement of regional contractility were analyzed.

RESULTS

We studied 46 men and 4 women with an average age of 61 years. Baseline left ventricular ejection fraction was 37 +/- 13%. A total of 2,020 myocardial segments were analyzed. Abnormal wall motion and the LGE area were detected in 1,446 segments (71.6%) and 1,196 segments (59.2%) respectively. Wall motion improvement was demonstrated in 481 of 1,227 segments (39.2%) that initially had wall motion abnormalities at baseline. Logistic regression analysis showed that the LGE area, EDWT and resting wall motion grade predicted wall motion improvement. Comparison of Receiver-Operator-Characteristic (ROC) curves demonstrated that the LGE area was the most important predictor (p < 0.001). Adding information from LGE to the EDWT can decrease the number of false predictions by EDWT alone from 483 to 127 segments.

CONCLUSION

LGE and EDWT are independent predictors for functional recovery after revascularization. However, LGE appears to be a more important factor and independent of EDWT.

Correlation Between NT-pro BNP levels and left ventricular wall stress, sphericity index and extent of myocardial damage: a magnetic resonance imaging study

BACKGROUND

To determine the relationship between N-terminal pro-brain natriuretic peptide (NT-pro BNP) levels and left ventricular wall stress, sphericity index, function, and extent of myocardial damage in patients with coronary artery disease and abnormal left ventricular wall motion.

METHODS AND RESULTS

All patients underwent cardiac magnetic resonance imaging (CMR). Measurements of sphericity index and wall stress were performed. Percentages of myocardial scarring were calculated from delayed-enhancement images. Correlations between log NT-pro BNP levels and various parameters were evaluated. There were 125 patients with an average age of 62.6 +/- 9.6 years. Median levels of NT-proBNP were 1012 pg/mL. Average left ventricular ejection fraction (LVEF) was 37 +/- 14.4%. Log NT-proBNP levels had positive correlations with wall stress, sphericity index, left ventricular dimension, volume, mass, wall motion score, extent of myocardial scarring, and age, and had negative correlations with creatinine clearance, LVEF, stroke volume, and body size. Multiple linear regression analysis showed that diastolic and systolic wall stress and systolic sphericity index were independent predictors for log NT-proBNP levels.

CONCLUSIONS

NT-proBNP levels strongly correlated with left ventricular wall stress and moderately correlated with sphericity index.

Pre-operative evaluation with magnetic resonance imaging in tetralogy of fallot and pulmonary atresia with ventricular septal defect

BACKGROUND

Preoperative evaluation of patients with pulmonary atresia and ventricular septal defect (PA/ VSD) are generally done by echocardiogram and cardiac catheterization. The authors' objective of the present study was to compare the findings of Gadolinium (Gd) enhanced cardiac magnetic resonance angiography (MRA) with cardiac catheterization.

MATERIAL AND METHOD

Patients who had PA/VSD were prospectively evaluated using cardiac catheterization and cardiac MRA. A branch of the pulmonary arteries was divided into: main pulmonary artery (MPA), left and right branch pulmonary artery (LPA & RPA), major aortopulmonary collateral arteries (MAPCA) and minor collaterals. Each study was interpreted blindly. The agreement of findings was compared using Kappa statistics.

RESULTS

There were 43 patients who received both cardiac catheterization and cardiac MRI within a 2 month period The average age was 13.8 +/- 8.4 (2-30) years-old. There was an agreement among measurement of both MPA and LPA & RPA with Kappa statistics of more than 0.8. Gd-enhanced MRA was able to identify more branches of MAPCA when compared to cardiac catheterization.

CONCLUSIONS

The results of the present study indicate that Gd-enhanced MRA is a feasible, fast and accurate technique for identification of all sources of pulmonary blood supply in patients with complex pulmonary atresia. The present study was a noninvasive alternative to cardiac catheterization. Gd-enhanced MRA can better delineate small (minor) branches of collateral.

Accuracy of visual assessment in the detection and quantification of myocardial scar by delayed enhancement magnetic resonance imaging

BACKGROUND

Delayed-enhancement magnetic resonance imaging (DE-MRI) is now a standard for the detection of myocardial scar and viability. Standard analysis needs expensive software.

OBJECTIVE

To determine the accuracy of visual assessment in the detection and quantification of myocardial scar by DE-MRI technique.

MATERIAL AND METHOD

The authors enrolled 32 patients with coronary artery disease (CAD) as documented by coronary angiography (CAG) and left ventricular dysfunction. All patients underwent cardiac magnetic resonance imaging for the assessment of global and regional myocardial function and DE-MRI. The presence and amount of scar in each myocardial segment was assessed by standard method. Visual assessment was performed by two methods: 1) visual drawing of the boundary of the hyperenhancement region and calculation of percentages of scar in an individual segment; 2) visual estimation of grading of hyperenhancement area from 0 (no scar) to 4 (> 75% scar). The agreement for scar detection and correlation of scar quantification for individual segments were evaluated.

RESULTS

Thirty-one of 32 patients in the present study had myocardial scar. One thousand four hundred and thirty two myocardial segments were analyzed. Visual detection of myocardial scar has an excellent level of agreement with standard method of scar (Kappa = 0.963 and 0.952, p<0.001 for visual method I and II). Visual method I and II has an accuracy of 98.2% and 97.6% respectively in the detection of myocardial scar compared to standard method. Percentages of myocardial scar in each myocardial segment by visual method I correlate very well with standard method (Intraclass Correlation Coefficient = 0.885). Visual grading of amount of myocardial scar also has an excellent correlation with standard method (Spearman rank correlation coefficient = 0.934).

CONCLUSION

Visual assessment of myocardial scar is accurate for the detection and quantification of scar.

Magnetic Resonance Imaging Abnormalities in Right Ventricular Outflow Tract Tachycardia and the Prediction of Radiofrequency Ablation Outcome

BACKGROUND

Recent reports have shown abnormalities on cardiac magnetic resonance imaging (MRI) in patients with right ventricular outflow tract (RVOT) tachycardia.

OBJECTIVES

OBJECTIVES of this study were to demonstrate abnormalities on MRI and signal-averaged ECG (SAECG) in patients with RVOT tachycardia and their correlation with the outcome of radiofrequency (RF) ablation.

METHODS

We studied 41 patients with symptomatic RVOT tachycardia and 15 controls. SAECG and cardiac MRI were performed on every subject. An evaluation of structural abnormality, chamber size, function, and wall motion abnormality of the left and right ventricle was performed by MRI. Focal wall thinning was evaluated by the black blood technique and fatty infiltration was evaluated by the T1 image with and without fat suppression.

RESULTS

MRI abnormalities were demonstrated in 24 (58.5%) patients with RVOT tachycardia. The abnormalities included localized wall bulging in 22 (53.7%), focal wall thinning in 10 (24.4%), and fatty replacement in 9 (22%) patients. MRI abnormality was found in only one patient in the control group (P < 0.001). Late potentials from SAECG were demonstrated in six (10.7%) patients but none in the controls (P = 0.117). Among 29 patients who underwent RF ablation, 3 patients had a failed procedure and 3 having arrhythmia recurrence needed repeated ablation. MRI abnormalities and late potentials were associated with an unfavorable outcome of RF ablation.

CONCLUSIONS

MRI abnormalities were frequently found in patients with RVOT tachycardia. MRI abnormalities and late potentials can predict outcomes of RF ablation.

Can spatial and temporal motion integration compensate for deficits in local motion mechanisms?

We studied the motion perception of a patient, AMG, who had a lesion in the left occipital lobe centered on visual areas V3 and V3A, with involvement of underlying white matter. As shown by a variety of psychophysical tests involving her perception of motion, the patient was impaired at motion discriminations that involved the detection of small displacements of random-dot displays, including local speed discrimination. However, she was unimpaired on tests that required spatial and temporal integration of moving displays, such as motion coherence. The results indicate that she had a specific impairment of the computation of local but not global motion and that she could not integrate motion information across different spatial scales. Such a specific impairment has not been reported before.

Interactive 3-dimensional segmentation of MRI data in personal computer environment

We describe a method of interactive three-dimensional segmentation and visualization for anatomical magnetic resonance imaging (MRI) data in a personal computer environment. The visual feedback necessary during 3-D segmentation was provided by a ray casting algorithm, which was designed to allow users to interactively decide the visualization quality depending on the task-requirement. Structures such as gray matter, white matter, and facial skin from T1-weighted high-resolution MRI data were segmented and later visualized with surface rendering. Personal computers with central processing unit (CPU) speeds of 266, 400, and 700 MHz, were used for the implementation. The 3-D visualization upon each execution of the segmentation operation was achieved in the order of 2 s with a 700 MHz CPU. Our results suggest that 3-D volume segmentation with semi real-time visual feedback could be effectively implemented in a PC environment without the need for dedicated graphics processing hardware.

MRI monitoring of laser ablation using optical flow

The optical flow method is used for visualizing and quantifying the dynamics of tissue changes observed by MRI during thermal ablations. An approach was implemented for parallel two-dimensional optical flow calculations including the replacement of spurious velocities. Velocity magnitude results were found to be accurate in low-noise cases in tests using series of synthetic images. Optical flow results are presented from thermal ablation experiments utilizing a homogeneous polyacrylamide gel phantom and heterogeneous rabbit liver tissue in vivo, exhibiting heating and cooling with the accompanying quantitative characterization of the dilation and contraction of the thermally affected region. Results demonstrate that optical flow is capable of noninvasive real-time monitoring and control of interstitial laser therapy (ILT).

Digital wavelet-encoded MRI: a new wavelet-encoding methodology

A new digital wavelet-encoding method for MRI is described. The method differs from previously described wavelet-encoding approaches, because the point-spread function is made independent of the wavelet basis used. This has a significant practical advantage, because wavelet bases can now be considered that would otherwise be excluded due to the difficulty of precisely exciting wavelet-shaped RF profiles. The method has been implemented on a clinical MRI system, and human images are presented.

Monitoring and visualization techniques for MR-guided laser ablations in an open MR system

Our purpose was to develop temperature-sensitive MR sequences and image-processing techniques to assess their potential of monitoring interstitial laser therapy (ILT) in brain tumors (n = 3) and liver tumors (n = 7). ILT lasted 2 to 26 minutes, whereas images from T1-weighted fast-spin-echo (FSE) or spoiled gradient-recalled (SPGR) sequences were acquired within 5 to 13 seconds. Pixel subtraction and visualization of T1-weighted images or optical flow computation was done within less than 110 msec. Alternating phase-mapping of real and imaginary components of SPGR sequences was performed within 220 msec. Pixel subtraction of T1-weighted images identified thermal changes in liver and brain tumors but could not evaluate the temperature values as chemical shift-based imaging, which was, however, more affected by susceptibility effects and motion. Optical flow computation displayed the predicted course of thermal changes and revealed that the rate of heat deposition can be anisotropic, which may be related to heterogeneous tumor structure and/or vascularization.

Real-time interactive three-dimensional segmentation

RATIONALE AND OBJECTIVES

The authors developed a real-time, interactive three-dimensional (3D) segmentation pipeline that uses relatively low-level segmentation operations and provides two-dimensional and 3D visualization through a user-friendly graphical interface.

MATERIALS AND METHODS

The low-level segmentation processes were implemented on a massively parallel computer; the graphical user interface was written with a public domain software toolkit. Since their implementation 2 years ago, these segmentation tools have been applied to approximately 300 computed tomographic and magnetic resonance imaging data sets. Two typical clinical cases are presented to demonstrate their applications.

RESULTS

The entire processing pipeline can be executed in a few seconds. The tools are simple to learn because they involve the use of low-level procedures and a user-friendly graphical interface with a short interactive response time. Segmentation of the bones, aorta, kidneys, and kidney cysts in case 1 could be performed in about 16 minutes. The time needed to segment each organ in case 2 ranged from about 15 minutes for the skin and brain to about 1 minute for the tumor.

CONCLUSION

Satisfactory results can be obtained in a relatively short time with the real-time interactive 3D segmentation system. Operation of the system can be easily learned by a wide variety of nonspecialized users with some medical background.

Non-Fourier encoding with multiple spin echoes

The advantages and limitations of multiple spin-echo sequences for non-Fourier encoding are investigated. Complications caused by improper encoding of alternate magnetization pathways due to imperfect refocusing pulses are analyzed. It is shown that mirror image ghosts result if the encoding RF pulse matrix is real-valued. These ghosts can be avoided as long as the rows of the RF pulse matrix are conjugate symmetric, which implies that spatial profiles are real valued. Non-Fourier encoding using bases derived from wavelet, Hadamard, and other real-valued orthogonal functions does not result in a mirror ghost artifact. A RARE sequence for non-Fourier encoding has been implemented on a clinical imaging system and successfully applied for brain imaging.