Efficient noninvasive imaging techniques in the differentiation of intrahepatic cholangiocarcinoma (ICC) and hepatocellular carcinoma (HCC) are very important because of their different management and prognosis. Our purpose was to evaluate the difference of parameters extracted from intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) between the two groups and their performance for the differentiation, as well as the significance of perfusion information. IVIM studies (9 b-values) in 41 patients with either ICC or HCC were reviewed retrospectively by two observers. Diffusion coefficient (D), pseudodiffusion coefficient (D ∗ ), perfusion fraction (f), ADC, and the mean percentage of parenchymal enhancement (MPPE) at 30 s after contrast-enhancement were calculated and compared between ICC and HCC. The relationship between D ∗ , f values, and MPPE was evaluated by Spearman’s correlation test. The diagnostic efficacy of all parameters was analyzed by the receiver operating characteristic (ROC) curve. Interobserver and intraobserver agreements were analyzed. The parameters (D and ADC) of ICC were distinctly higher than those of HCC; whereas the parameters (f and MPPE of arterial phase) were distinctly lower (all false discovery rate [FDR]-corrected P  < 0.05). The metric D ∗ value of ICC was slightly higher than that of HCC (71.44 vs 69.41) with FDR-corrected P  > 0.05. Moreover, the value of parameter D was significantly lower than that of ADC (FDR-corrected P  < 0.05). The parameters (D and f values) extracted from IVIM showed excellent diagnostic efficiency in the identification, and the diagnostic efficiency of D value was significantly higher than that of the ADC. There were positive correlations between perfusion-related parameters (D ∗ , f values) and MPPE. Interobserver and intraobserver agreements were excellent or perfect in measurements of all parameters. Parameters derived from IVIM were valuable for distinguishing ICC and HCC. Moreover, the D value showed better diagnostic efficiency for the differential diagnosis than monoexponential fitting-derived ADC value. Meanwhile, the significant correlation between perfusion-related parameters and MPPE demonstrates that specific IVIM metrics may serve as a noninvasive indicator for the vascular perfusion information of ICC and HCC.

Background Regular monitoring of static lacunar infarction (SLI) lesions plays an important role in preventing disease development and managing prognosis. Magnetic resonance imaging is one method used to monitor SLI lesions. Purpose To evaluate the image quality of the T2 fluid-attenuated inversion recovery (T2-FLAIR) sequence using artificial intelligence-assisted compressed sensing (ACS) in detecting SLI lesions and assess its clinical applicability. Methods A total of 42 patients were prospectively enrolled and scanned by T2-FLAIR. Two independent readers reviewed the images acquired with accelerated modes 1D (acceleration factor 2) and ACS (acceleration factors 2, 3, and 4). The overall image quality and lesion image quality were analyzed, as were signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and number of lesions between groups. Results The subjective assessment of overall brain image quality and lesion image quality was consistent between the two readers. The lesion display quality and the overall image quality were better with the traditional 1D acceleration method than with the ACS accelerated method. There was no significant difference in the SNR of the lacunar infarction in the images between the groups. The CNR of the images with the 1D acceleration mode was significantly lower than that of images with the ACS acceleration mode. Images with the 1D, ACS2, and ACS3 acceleration modes showed no significant differences in terms of detecting lesions but scan time can be reduced by 40% (1D vs. ACS3). Conclusion ACS acceleration mode can greatly reduce the scan time. In addition, the images have good SNR, high CNR, and strong SLI lesion detection ability.

Objective. The value of multiphase contrast-enhanced CT in differentiating gastrointestinal stromal tumors (GISTs) and gastric leiomyomas (GLMs) which were ≤3 cm was evaluated using machine learning. Methods. A retrospective analysis was conducted on 45 cases of small gastric wall submucosal tumors (including 22 GISTs and 23 GLMs) with pathologically confirmed diameter ≤3 cm and completed multiphase CT-enhanced scan images. The CT features including tumor location, maximum diameter, shape, margins, growth pattern, plain/enhanced CT value, cystic degeneration, calcification, ulcer, progressive reinforcement, perilesional lymph nodes, and the CT value ratio of the tumor to the aorta at the same level in the enhanced phase III scan of the two groups were evaluated. Tumor location and maximum diameter were automatically evaluated by machine learning. Results. The GISTs and GLMs with a diameter ≤3 cm showed clear margins, uniform density on plain scan CT, and progressive homogeneous enhancement. The age of the GISTs is greater than that of the GLMs group. The plain scan CT value of the GISTs group was lower than that in the GLMs group. In the GISTs group, the lesions were mostly located in the fundus (68.18%), showing a mixed growth pattern (54.55%), and in the GLMs group, most lesions were located in the cardia (47.82%), showing an intraluminal growth pattern (95.65%). The abovementioned differences were statistically significant. Conclusions. Contrast-enhanced CT has limited value in differentiating small GISTs from GLMs, which are ≤3 cm. Older age (>49.0 years), a low plain CT value (<42.5 Hu), mixed growth inside and outside the cavity, and noncardiac location tended to be the criteria for the diagnosis of small GISTs of the gastric wall.