• Korean Journal of Radiology
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• 제22권2호
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• pp.168-178
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• 2021

Objective: To provide an automatic method for segmentation and diameter measurement of type B aortic dissection (TBAD). Materials and Methods: Aortic computed tomography angiographic images from 139 patients with TBAD were consecutively collected. We implemented a deep learning method based on a three-dimensional (3D) deep convolutional neural (CNN) network, which realizes automatic segmentation and measurement of the entire aorta (EA), true lumen (TL), and false lumen (FL). The accuracy, stability, and measurement time were compared between deep learning and manual methods. The intra- and inter-observer reproducibility of the manual method was also evaluated. Results: The mean dice coefficient scores were 0.958, 0.961, and 0.932 for EA, TL, and FL, respectively. There was a linear relationship between the reference standard and measurement by the manual and deep learning method (r = 0.964 and 0.991, respectively). The average measurement error of the deep learning method was less than that of the manual method (EA, 1.64% vs. 4.13%; TL, 2.46% vs. 11.67%; FL, 2.50% vs. 8.02%). Bland-Altman plots revealed that the deviations of the diameters between the deep learning method and the reference standard were -0.042 mm (-3.412 to 3.330 mm), -0.376 mm (-3.328 to 2.577 mm), and 0.026 mm (-3.040 to 3.092 mm) for EA, TL, and FL, respectively. For the manual method, the corresponding deviations were -0.166 mm (-1.419 to 1.086 mm), -0.050 mm (-0.970 to 1.070 mm), and -0.085 mm (-1.010 to 0.084 mm). Intra- and inter-observer differences were found in measurements with the manual method, but not with the deep learning method. The measurement time with the deep learning method was markedly shorter than with the manual method (21.7 ± 1.1 vs. 82.5 ± 16.1 minutes, p < 0.001). Conclusion: The performance of efficient segmentation and diameter measurement of TBADs based on the 3D deep CNN was both accurate and stable. This method is promising for evaluating aortic morphology automatically and alleviating the workload of radiologists in the near future.

• Korean Journal of Radiology
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• 제22권3호
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• pp.324-333
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• 2021

Objective: The clinical course of an individual patient with heart failure is unpredictable with left ventricle ejection fraction (LVEF) only. We aimed to evaluate the prognostic value of cardiac magnetic resonance (CMR)-derived myocardial fibrosis extent and to determine the cutoff value for event-free survival in patients with non-ischemic cardiomyopathy (NICM) who had severely reduced LVEF. Materials and Methods: Our prospective cohort study included 78 NICM patients with significantly reduced LV systolic function (LVEF < 35%). CMR images were analyzed for the presence and extent of late gadolinium enhancement (LGE). The primary outcome was major adverse cardiac events (MACEs), defined as a composite of cardiac death, heart transplantation, implantable cardioverter-defibrillator discharge for major arrhythmia, and hospitalization for congestive heart failure within 5 years after enrollment. Results: A total of 80.8% (n = 63) of enrolled patients had LGE, with the median LVEF of 25.4% (19.8-32.4%). The extent of myocardial scarring was significantly higher in patients who experienced MACE than in those without any cardiac events (22.0 [5.5-46.1] %LV vs. 6.7 [0-17.1] %LV, respectively, p = 0.008). During follow-up, 51.4% of patients with LGE ≥ 12.0 %LV experienced MACE, along with 20.9% of those with LGE ≤ 12.0 %LV (log-rank p = 0.001). According to multivariate analysis, LGE extent more than 12.0 %LV was independently associated with MACE (adjusted hazard ratio, 6.71; 95% confidence interval, 2.54-17.74; p < 0.001). Conclusion: In NICM patients with significantly reduced LV systolic function, the extent of LGE is a strong predictor for long-term adverse cardiac outcomes. Event-free survival was well discriminated with an LGE cutoff value of 12.0 %LV in these patients.

• Korean Journal of Radiology
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• 제22권5호
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• pp.725-734
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• 2021

Objective: To intraindividually compare hepatocellular carcinoma (HCC) washout between MRIs using hepatobiliary agent (HBA) and extracellular agent (ECA). Materials and Methods: This study included 114 prospectively enrolled patients with chronic liver disease (mean age, 55 ± 9 years; 94 men) who underwent both HBA-MRI and ECA-MRI before surgical resection for HCC between November 2016 and May 2019. For 114 HCCs, the lesion-to-liver visual signal intensity ratio (SIR) using a 5-point scale (-2 to +2) was evaluated in each phase. Washout was defined as negative visual SIR with temporal reduction of visual SIR from the arterial phase. Illusional washout (IW) was defined as a visual SIR of 0 with an enhancing capsule. The frequency of washout and MRI sensitivity for HCC using LR-5 or its modifications were compared between HBA-MRI and ECA-MRI. Subgroup analysis was performed according to lesion size (< 20 mm or ≥ 20 mm). Results: The frequency of portal venous phase (PP) washout with HBA-MRI was comparable to that of delayed phase (DP) washout with ECA-MRI (77.2% [88/114] vs. 68.4% [78/114]; p = 0.134). The frequencies were also comparable when IW was allowed (79.8% [91/114] for HBA-MRI vs. 81.6% [93/114] for ECA-MRI; p = 0.845). The sensitivities for HCC of LR-5 (using PP or DP washout) were comparable between HBA-MRI and ECA-MRI (78.1% [89/114] vs. 73.7% [84/114]; p = 0.458). In HCCs < 20 mm, the sensitivity of LR-5 was higher on HBA-MRI than on ECA-MRI (70.8% [34/48] vs. 50.0% [24/48]; p = 0.034). The sensitivity was similar to each other if IW was added to LR-5 (72.9% [35/48] for HBA-MRI vs. 70.8% [34/48] for ECA-MRI; p > 0.999). Conclusion: Extracellular phase washout for HCC diagnosis was comparable between MRIs with both contrast agents, except for tumors < 20 mm. Adding IW could improve the sensitivity for HCC on ECA-MRI in tumors < 20 mm.

• Korean Journal of Radiology
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• 제22권3호
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• pp.384-394
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• 2021

Objective: To quantitatively assess biochemical alterations in the cartilage of the subtalar and midtarsal joints in chronic lateral ankle instability (CLAI) patients with isolated anterior talofibular ligament (ATFL) injuries and combined calcaneofibular ligament (CFL) injuries using MRI T2 mapping. Materials and Methods: This study was performed according to regulations of the Committee for Human Research at our institution, and written informed consent was obtained from all participants. Forty CLAI patients (26 with isolated ATFL injuries and 14 with combined ATFL and CFL injuries) and 25 healthy subjects were recruited for this study. All participants underwent MRI scans with T2 mapping. Patients were assessed with the American Orthopedic Foot and Ankle Society (AOFAS) rating system. The subtalar and midtarsal joints were segmented into 14 cartilage subregions. The T2 value of each subregion was measured from T2 mapping images. Data were analyzed with ANOVA, the Student's t test, and Pearson's correlation coefficient. Results: T2 values of most subregions of the subtalar joint and the calcaneal facet of the calcaneocuboid joint in CLAI patients with combined CFL injuries were higher than those in healthy controls (all p < 0.05). However, there were no significant differences in T2 values in subtalar and midtarsal joints between patients with isolated ATFL injuries and healthy controls (all p > 0.05). Moreover, T2 values of the medial talar subregions of the posterior subtalar joint in patients with combined CFL injuries showed negative correlations with the AOFAS scores (r = -0.687, p = 0.007; r = -0.609, p = 0.021, respectively). Conclusion: CLAI with combined CFL injuries can lead to cartilage degeneration in subtalar and calcaneocuboid joints, while an isolated ATFL injury might not have a significant impact on the cartilage in these joints.

• Korean Journal of Radiology
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• 제22권5호
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• pp.770-781
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• 2021

Objective: Chemical exchange-dependent saturation transfer (CEST) MRI is sensitive for detecting solid-like proteins and may detect changes in the levels of mobile proteins and peptides in tissues. The objective of this study was to evaluate the characteristics of chemical exchange proton pools using the CEST MRI technique in patients with dementia. Materials and Methods: Our institutional review board approved this cross-sectional prospective study and informed consent was obtained from all participants. This study included 41 subjects (19 with dementia and 22 without dementia). Complete CEST data of the brain were obtained using a three-dimensional gradient and spin-echo sequence to map CEST indices, such as amide, amine, hydroxyl, and magnetization transfer ratio asymmetry (MTR_asym) values, using six-pool Lorentzian fitting. Statistical analyses of CEST indices were performed to evaluate group comparisons, their correlations with gray matter volume (GMV) and Mini-Mental State Examination (MMSE) scores, and receiver operating characteristic (ROC) curves. Results: Amine signals (0.029 for non-dementia, 0.046 for dementia, p = 0.011 at hippocampus) and MTR_asym values at 3 ppm (0.748 for non-dementia, 1.138 for dementia, p = 0.022 at hippocampus), and 3.5 ppm (0.463 for non-dementia, 0.875 for dementia, p = 0.029 at hippocampus) were significantly higher in the dementia group than in the non-dementia group. Most CEST indices were not significantly correlated with GMV; however, except amide, most indices were significantly correlated with the MMSE scores. The classification power of most CEST indices was lower than that of GMV but adding one of the CEST indices in GMV improved the classification between the subject groups. The largest improvement was seen in the MTR_asym values at 2 ppm in the anterior cingulate (area under the ROC curve = 0.981), with a sensitivity of 100 and a specificity of 90.91. Conclusion: CEST MRI potentially allows noninvasive image alterations in the Alzheimer's disease brain without injecting isotopes for monitoring different disease states and may provide a new imaging biomarker in the future.

• Korean Journal of Radiology
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• 제22권2호
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• pp.281-290
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• 2021

Objective: To assess the performance of content-based image retrieval (CBIR) of chest CT for diffuse interstitial lung disease (DILD). Materials and Methods: The database was comprised by 246 pairs of chest CTs (initial and follow-up CTs within two years) from 246 patients with usual interstitial pneumonia (UIP, n = 100), nonspecific interstitial pneumonia (NSIP, n = 101), and cryptogenic organic pneumonia (COP, n = 45). Sixty cases (30-UIP, 20-NSIP, and 10-COP) were selected as the queries. The CBIR retrieved five similar CTs as a query from the database by comparing six image patterns (honeycombing, reticular opacity, emphysema, ground-glass opacity, consolidation and normal lung) of DILD, which were automatically quantified and classified by a convolutional neural network. We assessed the rates of retrieving the same pairs of query CTs, and the number of CTs with the same disease class as query CTs in top 1-5 retrievals. Chest radiologists evaluated the similarity between retrieved CTs and queries using a 5-scale grading system (5-almost identical; 4-same disease; 3-likelihood of same disease is half; 2-likely different; and 1-different disease). Results: The rate of retrieving the same pairs of query CTs in top 1 retrieval was 61.7% (37/60) and in top 1-5 retrievals was 81.7% (49/60). The CBIR retrieved the same pairs of query CTs more in UIP compared to NSIP and COP (p = 0.008 and 0.002). On average, it retrieved 4.17 of five similar CTs from the same disease class. Radiologists rated 71.3% to 73.0% of the retrieved CTs with a similarity score of 4 or 5. Conclusion: The proposed CBIR system showed good performance for retrieving chest CTs showing similar patterns for DILD.

• Korean Journal of Radiology
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• 제22권1호
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• pp.106-117
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• 2021

Objective: To explore the value of magnetic resonance imaging (MRI)-based whole tumor texture analysis in differentiating borderline epithelial ovarian tumors (BEOTs) from FIGO stage I/II malignant epithelial ovarian tumors (MEOTs). Materials and Methods: A total of 88 patients with histopathologically confirmed ovarian epithelial tumors after surgical resection, including 30 BEOT and 58 MEOT patients, were divided into a training group (n = 62) and a test group (n = 26). The clinical and conventional MRI features were retrospectively reviewed. The texture features of tumors, based on T2-weighted imaging, diffusion-weighted imaging, and contrast-enhanced T1-weighted imaging, were extracted using MaZda software and the three top weighted texture features were selected by using the Random Forest algorithm. A non-texture logistic regression model in the training group was built to include those clinical and conventional MRI variables with p value < 0.10. Subsequently, a combined model integrating non-texture information and texture features was built for the training group. The model, evaluated using patients in the training group, was then applied to patients in the test group. Finally, receiver operating characteristic (ROC) curves were used to assess the diagnostic performance of the models. Results: The combined model showed superior performance in categorizing BEOTs and MEOTs (sensitivity, 92.5%; specificity, 86.4%; accuracy, 90.3%; area under the ROC curve [AUC], 0.962) than the non-texture model (sensitivity, 78.3%; specificity, 84.6%; accuracy, 82.3%; AUC, 0.818). The AUCs were statistically different (p value = 0.038). In the test group, the AUCs, sensitivity, specificity, and accuracy were 0.840, 73.3%, 90.1%, and 80.8% when the non-texture model was used and 0.896, 75.0%, 94.0%, and 88.5% when the combined model was used. Conclusion: MRI-based texture features combined with clinical and conventional MRI features may assist in differentitating between BEOT and FIGO stage I/II MEOT patients.

• Korean Journal of Radiology
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• 제22권2호
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• pp.253-262
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• 2021

Objective: To determine whether the values of hepatic apparent diffusion coefficient (ADC) can differentiate biliary atresia (BA) from non-BA or be correlated with the grade of hepatic fibrosis in infants with cholestasis. Materials and Methods: This retrospective cohort study included infants who received liver MRI examinations to evaluate cholestasis from July 2009 to October 2017. Liver ADC, ADC ratio of liver/spleen, aspartate aminotransferase to platelet ratio index (APRI), and spleen size were compared between the BA and non-BA groups. The diagnostic performances of all parameters for significant fibrosis (F3-4) were obtained by receiver-operating characteristics (ROCs) curve analysis. Results: Altogether, 227 infants (98 males and 129 females, mean age = 57.2 ± 36.3 days) including 125 BA patients were analyzed. The absolute ADC difference between two reviewers was 0.10 mm²/s for both liver and spleen. Liver ADC value was specific (80.4%) and ADC ratio was sensitive (88.0%) for the diagnosis of BA with comparable performance. There were 33 patients with F0, 15 with F1, 71 with F2, 35 with F3, and 11 with F4. All four parameters of APRI (τ = 0.296), spleen size (τ = 0.312), liver ADC (τ = -0.206), and ADC ratio (τ = -0.288) showed significant correlation with fibrosis grade (all, p < 0.001). The cutoff values for significant fibrosis (F3-4) were 0.783 for APRI (area under the ROC curve [AUC], 0.721), 5.9 cm for spleen size (AUC, 0.719), 1.044 x 10^-3 mm²/s for liver ADC (AUC, 0.673), and 1.22 for ADC ratio (AUC, 0.651). Conclusion: Liver ADC values and ADC ratio of liver/spleen showed limited additional diagnostic performance for differentiating BA from non-BA and predicting significant hepatic fibrosis in infants with cholestasis.

• Korean Journal of Radiology
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• 제22권1호
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• pp.118-130
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• 2021

Objective: This study aimed to investigate the blood-brain barrier (BBB) disruption in mild traumatic brain injury (mTBI) patients with post-concussion syndrome (PCS) using dynamic contrast-enhanced (DCE) magnetic resonance (MR) imaging and automatic whole brain segmentation. Materials and Methods: Forty-two consecutive mTBI patients with PCS who had undergone post-traumatic MR imaging, including DCE MR imaging, between October 2016 and April 2018, and 29 controls with DCE MR imaging were included in this retrospective study. After performing three-dimensional T1-based brain segmentation with FreeSurfer software (Laboratory for Computational Neuroimaging), the mean K^trans and v_p from DCE MR imaging (derived using the Patlak model and extended Tofts and Kermode model) were analyzed in the bilateral cerebral/cerebellar cortex, bilateral cerebral/cerebellar white matter (WM), and brainstem. K^trans values of the mTBI patients and controls were calculated using both models to identify the model that better reflected the increased permeability owing to mTBI (tendency toward higher K^trans values in mTBI patients than in controls). The Mann-Whitney U test and Spearman rank correlation test were performed to compare the mean K^trans and v_p between the two groups and correlate K^trans and v_p with neuropsychological tests for mTBI patients. Results: Increased permeability owing to mTBI was observed in the Patlak model but not in the extended Tofts and Kermode model. In the Patlak model, the mean K^trans in the bilateral cerebral cortex was significantly higher in mTBI patients than in controls (p = 0.042). The mean v_p values in the bilateral cerebellar WM and brainstem were significantly lower in mTBI patients than in controls (p = 0.009 and p = 0.011, respectively). The mean K^trans of the bilateral cerebral cortex was significantly higher in patients with atypical performance in the auditory continuous performance test (commission errors) than in average or good performers (p = 0.041). Conclusion: BBB disruption, as reflected by the increased K^trans and decreased v_p values from the Patlak model, was observed throughout the bilateral cerebral cortex, bilateral cerebellar WM, and brainstem in mTBI patients with PCS.

• Korean Journal of Radiology
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• 제22권1호
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• pp.131-138
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• 2021

Objective: Iterative reconstruction degrades image quality. Thus, further advances in image reconstruction are necessary to overcome some limitations of this technique in low-dose computed tomography (LDCT) scan of the chest. Deep-learning image reconstruction (DLIR) is a new method used to reduce dose while maintaining image quality. The purposes of this study was to evaluate image quality and noise of LDCT scan images reconstructed with DLIR and compare with those of images reconstructed with the adaptive statistical iterative reconstruction-Veo at a level of 30% (ASiR-V 30%). Materials and Methods: This retrospective study included 58 patients who underwent LDCT scan for lung cancer screening. Datasets were reconstructed with ASiR-V 30% and DLIR at medium and high levels (DLIR-M and DLIR-H, respectively). The objective image signal and noise, which represented mean attenuation value and standard deviation in Hounsfield units for the lungs, mediastinum, liver, and background air, and subjective image contrast, image noise, and conspicuity of structures were evaluated. The differences between CT scan images subjected to ASiR-V 30%, DLIR-M, and DLIR-H were evaluated. Results: Based on the objective analysis, the image signals did not significantly differ among ASiR-V 30%, DLIR-M, and DLIR-H (p = 0.949, 0.737, 0.366, and 0.358 in the lungs, mediastinum, liver, and background air, respectively). However, the noise was significantly lower in DLIR-M and DLIR-H than in ASiR-V 30% (all p < 0.001). DLIR had higher signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) than ASiR-V 30% (p = 0.027, < 0.001, and < 0.001 in the SNR of the lungs, mediastinum, and liver, respectively; all p < 0.001 in the CNR). According to the subjective analysis, DLIR had higher image contrast and lower image noise than ASiR-V 30% (all p < 0.001). DLIR was superior to ASiR-V 30% in identifying the pulmonary arteries and veins, trachea and bronchi, lymph nodes, and pleura and pericardium (all p < 0.001). Conclusion: DLIR significantly reduced the image noise in chest LDCT scan images compared with ASiR-V 30% while maintaining superior image quality.