Acta radiologica最新文献

Background: The accurate differentiation of primary central nervous system lymphoma (PCNSL) from glioblastoma multiforme (GBM) is clinically crucial due to the different treatment strategies between them.

Purpose: To define magnetic resonance imaging (MRI) perfusion findings in PCNSL to make a safe distinction from GBM with dynamic contrast-enhanced (DCE) T1 and DSC T2 MRI perfusion findings.

Material and methods: This retrospective analysis included 19 patients with histopathologically diagnosed PCNSL and 21 individuals with GBM. DCE T1 vascular permeability perfusion values including K-trans, Ve, Kep, IAUGC, and DSC T2 perfusion values including cerebral blood volume (CBV) and cerebral blood flow (CBF) in axial sections from the pathological lesion and contralateral normal brain parenchyma were measured quantitatively using region of interest analysis.

Results: The study observed no statistically significant difference between patients with PCNSL (T/B cell) and GBM in the median values of DCE T1 perfusion ratios (P > 0.05). Nevertheless, the DSC T2 perfusion ratios showed a substantial distinction between the two groups. In contrast to patients with PCNSL (1.185 vs. 1.224, respectively), those with GBM had higher median levels of r-CBV and r-CBF (2.898 vs. 2.467, respectively; P 0.01). A cutoff value of ≤1.473 for r-CBV (Lesion/N) and ≤1.6005 for r-CBF (Lesion/N) was found to estimate the positivity of PCNSL.

Conclusion: DSC T2 MRI perfusion values showed lower r-CBV and r-CBF values in PCNSL patients compared to GBM patients. According to the findings, r-CBV and r-CBF are the most accurate MRI perfusion parameters for distinguishing between PCSNL and GBM.

Background: Ovarian thecoma-fibroma and solid ovarian cancer have similar clinical and imaging features, and it is difficult for radiologists to differentiate them. Since the treatment and prognosis of them are different, accurate characterization is crucial.

Purpose: To non-invasively differentiate ovarian thecoma-fibroma and solid ovarian cancer by convolutional neural network based on magnetic resonance imaging (MRI), and to provide the interpretability of the model.

Material and methods: A total of 156 tumors, including 86 ovarian thecoma-fibroma and 70 solid ovarian cancer, were split into the training set, the validation set, and the test set according to the ratio of 8:1:1 by stratified random sampling. In this study, we used four different networks, two different weight modes, two different optimizers, and four different sizes of regions of interest (ROI) to test the model performance. This process was repeated 10 times to calculate the average performance of the test set. The gradient weighted class activation mapping (Grad-CAM) was used to explain how the model makes classification decisions by visual location map.

Results: ResNet18, which had pre-trained weight, using Adam and one multiple ROI circumscribed rectangle, achieved best performance. The average accuracy, precision, recall, and AUC were 0.852, 0.828, 0.848, and 0.919 (P < 0.01), respectively. Grad-CAM showed areas associated with classification appeared on the edge or interior of ovarian thecoma-fibroma and the interior of solid ovarian cancer.

Conclusion: This study shows that convolution neural network based on MRI can be helpful for radiologists in differentiating ovarian thecoma-fibroma and solid ovarian cancer.

Background: Breath-hold volumetric interpolated breath-hold examination (BH-VIBE) of multiphase contrast-enhanced liver magnetic resonance imaging (MPCE-LMRI) requires good cooperative individuals to comply with multiple breath-holds.

Purpose: To develop a free-breathing modified VIBE (FB-mVIBE) as a substitute of BH-VIBE in MPCE-LMRI.

Material and methods: We modified VIBE with a high acceleration factor (2 × 2) and four averages to produce the mVIBE scan. A total of 90 individuals (40 men; mean age = 54.6 ± 10.0 years) who had received MPCE-LMRI as part of a voluntary health check-up for oncology survey were enrolled. Each participant was scanned in four phases (pre-contrast, arterial phase, venous phase, and delay phase), and each phase had two sequential scans. To encounter the timing effect of contrast enhancement, three scan orders were designed: BH-VIBE and FB-mVIBE (group A, n = 30); BH-VIBE and FB-VIBE (group B, n = 30); and FB-mVIBE and BH-VIBE (group C, n = 30). The comparisons included the objective measurements and 25 visual-score by two abdominal radiologists independently.

Results: Consistency between raters was observed for all three sequences (intraclass correlation coefficient [ICC] = 0.741-0.829). For rater 1, the mean scores of FB-mVIBE (23.67 ± 1.32) were equal to those of BH-VIBE (23.83 ± 1.98) in groups C and B (P = 0.852). The mean scores of FB-mVIBE (22.07 ± 3.02), but significantly higher than those of FB-VIBE (14.7 ± 3.41) in groups A and B (P <0.001). Similar scores were found for rater 2. The objective measurement of FB-mVIBE were equal to or higher than BH-VIBE and markedly superior to FB-VIBE.

Conclusion: FB-mVIBE is a practical alternative to BH-VIBE for individuals who cannot cooperate with multiple breath-holds for MPCE-LMRI.

Background: Artificial intelligence-based computer-assisted diagnosis (AI-CAD) is increasingly used for mammographic exams, and its role in mammographic density assessment should be evaluated.

Purpose: To assess the inter-modality agreement between radiologists, automated volumetric density measurement program (Volpara), and AI-CAD system in breast density categorization using the Breast Imaging-Reporting and Data System (BI-RADS) density categories.

Material and methods: A retrospective review was conducted on 1015 screening digital mammograms that were performed in Asian female patients (mean age = 56 years ± 10 years) in our health examination center between December 2022 and January 2023. Four radiologists with two different levels of experience (expert and general radiologists) performed density assessments. Agreement between the radiologists, Volpara, and AI-CAD (Lunit INSIGHT MMG) was evaluated using weighted kappa statistics and matched rates.

Results: Inter-reader agreement between expert and general radiologists was substantial (k = 0.65) with a matched rate of 72.8%. The agreement was substantial between expert or general radiologists and Volpara (k = 0.64-0.67) with a matched rate of 72.0% but moderate between expert or general radiologists and AI-CAD (k = 0.45-0.58) with matched rates of 56.7%-67.0%. The agreement between Volpara and AI-CAD was moderate (k = 0.53) with a matched rate of 60.8%.

Conclusion: The agreement in breast density categorization between radiologists and automated volumetric density measurement program (Volpara) was higher than the agreement between radiologists and AI-CAD (Lunit INSIGHT MMG).

Background: Brain magnetic resonance imaging voxel-based morphometry (VBM) and perfusion single-photon emission computed tomography (SPECT) are useful for differentiating dementia with Lewy bodies (DLB) from Alzheimer's disease (AD).

Purpose: To determine whether combining multiple parameters of VBM and SPECT using a multiparametric scoring system (MSS) improves diagnostic accuracy in differentiating DLB from AD.

Material and methods: In total, 23 patients with DLB and 57 patients with AD underwent imaging using a voxel-based specific regional analysis system for AD (VSRAD), an easy Z-score imaging system, and a Z-Graph using three-dimensional stereotactic surface projection. The cutoff values were determined using the receiver operating characteristic curve to differentiate DLB from AD for all parameters. Patients were scored 1 (DLB) or 0 (AD) for each statistically significant parameter, according to a threshold. The total score was determined for each case to obtain a cutoff value for the MSS.

Results: The mean Z-scores in the medial temporal lobes using the VSRAD were significantly lower in patients with DLB than in those with AD. Each Z-score of the summed Z-scores in all four segmented regions of the occipital lobes using the Z-Graph was significantly higher in patients with DLB than in those with AD. Among the five parameters, the highest accuracy was 80% for the Z-score of the summed Z-scores in the left medial occipital lobe. For the MSS, a cutoff value of four improved the diagnostic accuracy to 82%.

Conclusion: MSS was more accurate than any single parameter of VBM or SPECT in differentiating DLB from AD.

Background: Image quality and diagnostic accuracy in computed tomography angiography (CTA) reach their limits in imaging of below-the-knee vessels.

Purpose: To evaluate whether image quality in CTA of lower limbs is further improvable by combining side-separate reconstruction with a larger matrix size and whether resulting noise can be compromised with iterative reconstruction (IR).

Material and methods: From CTA of the lower extremities of 26 patients (5 women, 21 men; mean age = 68.5 ± 10.3 years), the lower legs were reconstructed side-separately with different reconstruction algorithms and matrix sizes including filtered back projection (FBP) with a 512 × 512 matrix, FBP with a 1024 × 1024 matrix, IR (SAFIRE) with a 512 × 512 matrix, and IR (SAFIRE) with a 1024 × 1024 matrix. A total of 208 CT series were evaluated. Subjective image quality was assessed by two readers using a 5-point Likert scale. Image noise was assessed by measuring signal-to-noise and contrast-to-noise ratios.

Results: Subjective image quality was rated significantly higher when using a 1024 × 1024 matrix (P < 0.001) and could further be increased with IR. Vessel sharpness was rated significantly better with a larger matrix (P < 0.001). Visible and measured image noise was significantly higher with a 1024 × 1024 matrix but could be reduced by using IR (P < 0.001), even to a level below FBP with a 512 × 512 matrix while reconstructing with a larger matrix (P < 0.001).

Conclusion: Image quality, image noise, and vessel sharpness can be further improved in CTA of the lower extremities with side-separate reconstruction using a 1024 × 1024 matrix size and IR.

Background: With rising breast augmentations worldwide, there is an increasing clinical need for an early and accurate detection of implant complications.

Purpose: To compare the quality of chemical shift encoding-based (CSE) water-fat-silicone separation compared to double inversion recovery (DIR) silicone-only imaging in breast magnetic resonance imaging (MRI).

Material and methods: This retrospective, single-center study included women with silicone implants subjected to 3-T MRI between January 2021 and March 2022. MRI included (i) two-dimensional silicone-only T2-weighted turbo spin echo DIR acquisition and (ii) three-dimensional CSE imaging based on multi-echo gradient-echo sequence enabling water-, fat-, and silicone-image separation. Images were evaluated and compared by three independent radiologists using a clinically established rating including differentiability of the silicone implant, visibility and contouring of the adjacent fibrous capsule, and accuracy of intralesional folds in a ranking of 1-5. The apparent contrast-to-noise (aCNR) was calculated.

Results: In 71 women, the average quality of water-fat-silicone images from CSE imaging was assessed as "good" (assessment 4 ± 0.9). In 68 (96%) patients, CSE imaging achieved a concise delineation of the silicone implant and precise visualization of the fibrous capsule that was not distinguishable in DIR imaging. Implant ruptures were more easily detected in CSE imaging. The aCNR was higher in CSE compared to DIR imaging (18.43 ± 9.8 vs. 14.73 ± 2.5; P = 0.002).

Conclusion: Intrinsically co-registered water-fat-silicone-separated CSE-based images enable a reliable assessment of silicone implants. The simultaneously improved differentiability of the implant and fibrous capsule may provide clinicians with a valuable tool for an accurate evaluation of implant integrity and early detection of potential complications.

Background: Cerebral small vessel disease (CSVD) causes cognitive decline and perivascular space enlargement is one of the image markers for CSVD.

Purpose: To search for clinical significance in the time-course augmentation of perivascular space in basal ganglia (BG-PVS) for cognitive decline.

Material and methods: This study population included 179 participants from a community-based cohort, aged 70 years at baseline. They had undergone magnetic resonance imaging (MRI) studies two or three times between 2000 and 2008. Based on the severity of BG-PVS or white matter hyperintensity lesions (WMHL) in 2000, the participants were divided into low-grade or high-grade groups, respectively. In addition, their time-course augmentation was evaluated, and we created a categorical BG-PVS WMHL change score based on their augmentation (1 = neither, 2 = BG-PVS augmentation only, 3 = WMHL augmentation only, 4 = both). Cognitive function was assessed based on the Mini-Mental State Examination (MMSE); the change was defined as the difference between scores in 2000 and 2008. We used simple or multiple regression analysis for MMSE score change according to MRI findings and clinical characteristics that were probably related to cognitive decline.

Results: In univariate analysis, MMSE score change was negatively associated with BG-PVS high grade at baseline and BG-PVS WMHL change score 4; this remained significant in multivariate analysis. In the final model based on the Akaike Information Criterion, BG-PVS WMHL change score 4 was associated with a 3.3-point decline in subsequent MMSE score.

Conclusions: This study suggested that augmentation in both BG-PVS and WMHL was associated with subsequent cognitive decline.

Background: Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) with an extended Tofts linear (ETL) model for tissue and tumor evaluation has been established, but its effectiveness in evaluating the pancreas remains uncertain.

Purpose: To understand the pharmacokinetics of normal pancreas and serve as a reference for future studies of pancreatic diseases.

Material and methods: Pancreatic pharmacokinetic parameters of 54 volunteers were calculated using DCE-MRI with the ETL model. First, intra- and inter-observer reliability was assessed through the use of the intra-class correlation coefficient (ICC) and coefficient of variation (CoV). Second, a subgroup analysis of the pancreatic DCE-MRI pharmacokinetic parameters was carried out by dividing the 54 individuals into three groups based on the pancreatic region, three groups based on age, and two groups based on sex.

Results: There was excellent agreement and low variability of intra- and inter-observer to pancreatic DCE-MRI pharmacokinetic parameters. The intra- and inter-observer ICCs of K^trans, k_ep, v_e, and v_p were 0.971, 0.952, 0.959, 0.944 and 0.947, 0.911, 0.978, 0.917, respectively. The intra- and inter-observer CoVs of K^trans, k_ep, v_e, v_p were 9.98%, 5.99%, 6.47%, 4.76% and 10.15%, 5.22%, 6.28%, 5.40%, respectively. Only the pancreatic v_e of the older group was higher than that of the young and middle-aged groups (P = 0.042, 0.001), and the v_p of the pancreatic head was higher than that of the pancreatic body and tail (P = 0.014, 0.043).

Conclusion: The application of DCE-MRI with an ETL model provides a reliable, robust, and reproducible means of non-invasively quantifying pancreatic pharmacokinetic parameters.