Effect of decreased contrast injection flow rate on aortic enhancement in 80-KV peak CT with contrast dose reduction.

BACKGROUND: Effect of decreased injection flow rate of contrast agent at the same iodine dose and delivery rate on aortic enhancement has not been clearly elucidated. PURPOSE: To evaluate the effect of decreased injection flow rate of contrast agent on aortic peak enhancement in a dynamic flow phantom and on aortic enhancement in clinical dynamic 80-kVp computed tomography (CT) with contrast dose reduction. MATERIAL AND METHODS: In the dynamic flow phantom experiment, the effect of a decreased injection flow rate at the same total iodine dose and delivery rate on simulated aortic peak enhancement was evaluated. In the clinical retrospective study, we searched 312 patients with renal dysfunction who underwent an 80-kVp abdominal dynamic CT with 40% reduction of contrast agent from a standard 120-kVp protocol and measured the aortic enhancement at the level of the hepatic hilum. Independent predictors for aortic enhancement were determined by multiple linear regression analysis, and after adjustment of significant predictors, independent variables for acquiring optimal aortic enhancement, ≥300 HU, were determined by multiple logistic regression analysis. RESULTS: In the phantom experiment, decreased flow rate showed a significant but small descent effect (6%-9%) on simulated aortic peak enhancement. In the multiple linear regression analysis, only age was an independent predictor of aortic enhancement; there was no independent predictor for optimal age-adjusted aortic enhancement of ≥300 HU. CONCLUSIONS: Decreased injection flow rate had a small influence on aortic enhancement in vitro but had no significant effect on the aortic enhancement in clinical dynamic 80-kVp CT.

Carotid-anterior cerebral artery (ACA) anastomosis associated with azygos ACA and ophthalmic artery arising from the middle meningeal artery: a case report.

Among variations of the anterior cerebral artery (ACA), anastomosis of its A1-A2 junction with the ophthalmic segment of the internal carotid artery is rare and described as carotid-ACA anastomosis or infraoptic course of the ACA. One common variant, an azygos ACA, demonstrates no pairing of the A2 segment. To our knowledge, association of a carotid-ACA anastomosis with an azygos ACA is not reported in the English-language literature. We report a case diagnosed by magnetic resonance angiography in which right carotid-ACA anastomosis was associated with an azygos ACA and the right ophthalmic artery originated from the middle meningeal artery.

Three-dimensional sectional measurement approach for serial volume changes in shoulder muscles after arthroscopic rotator cuff repair.

Purpose: This study assessed the serial volume changes in multiple shoulder muscles simultaneously following arthroscopic rotator cuff repair (ARCR) by a three-dimensional (3D) modeling-based sectional measurement. These volume changes were correlated with background preoperative factors. Methods: Four consecutive magnetic resonance imaging scans (preoperatively and postoperatively at 3, 6, and 12 months) of 33 shoulders from 31 patients who underwent arthroscopic rotator cuff repair were examined. We focused on the sectional volume differences of the supraspinatus, infraspinatus, teres minor, and subscapularis between preoperatively and 3 months postoperatively (Dif.pre.3mo) and between 3 and 12 months postoperatively (Dif.3.12mo). The correlation between volume differences and clinical/demographic parameters was determined by a multivariate analysis. Results: No statistically significant differences were observed for most serial changes in the shoulder muscle volumes. The tear-site muscles (supraspinatus and infraspinatus) showed similar tendencies for volume changes, whereas the non-tear-site muscles (teres minor and subscapularis) differed. A negative correlation was observed between Dif.pre.3mo and Dif.3.12mo for the supraspinatus, infraspinatus, and teres minor. These perioperative volume differences might correlate with tear size and symptom duration in the supraspinatus, as well as with a history of steroid injections and work and sports activity levels in the infraspinatus and teres minor. Conclusion: The serial volume changes in multiple shoulder muscles after ARCR measured using our 3D sectional approach exhibited different tendencies and clinical implications depending on the primary and non-primary site of tears. Our method may serve as a potential indicator to facilitate muscle recovery and prevent the progression of postoperative muscle atrophy.

Three-dimensional convolutional neural network-based classification of chronic kidney disease severity using kidney MRI.

A three-dimensional convolutional neural network model was developed to classify the severity of chronic kidney disease (CKD) using magnetic resonance imaging (MRI) Dixon-based T1-weighted in-phase (IP)/opposed-phase (OP)/water-only (WO) imaging. Seventy-three patients with severe renal dysfunction (estimated glomerular filtration rate [eGFR] < 30 mL/min/1.73 m2, CKD stage G4-5); 172 with moderate renal dysfunction (30 ≤ eGFR < 60 mL/min/1.73 m2, CKD stage G3a/b); and 76 with mild renal dysfunction (eGFR ≥ 60 mL/min/1.73 m2, CKD stage G1-2) participated in this study. The model was applied to the right, left, and both kidneys, as well as to each imaging method (T1-weighted IP/OP/WO images). The best performance was obtained when using bilateral kidneys and IP images, with an accuracy of 0.862 ± 0.036. The overall accuracy was better for the bilateral kidney models than for the unilateral kidney models. Our deep learning approach using kidney MRI can be applied to classify patients with CKD based on the severity of kidney disease.

Three-dimensional magnetic resonance imaging-based statistical shape analysis and machine learning-based prediction of patellofemoral instability.

This study performed three-dimensional (3D) magnetic resonance imaging (MRI)-based statistical shape analysis (SSA) by comparing patellofemoral instability (PFI) and normal femur models, and developed a machine learning (ML)-based prediction model. Twenty (19 patients) and 31 MRI scans (30 patients) of femurs with PFI and normal femurs, respectively, were used. Bone and cartilage segmentation of the distal femurs was performed and subsequently converted into 3D reconstructed models. The pointwise distance map showed anterior elevation of the trochlea, particularly at the central floor of the proximal trochlea, in the PFI models compared with the normal models. Principal component analysis examined shape variations in the PFI group, and several principal components exhibited shape variations in the trochlear floor and intercondylar width. Multivariate analysis showed that these shape components were significantly correlated with the PFI/non-PFI distinction after adjusting for age and sex. Our ML-based prediction model for PFI achieved a strong predictive performance with an accuracy of 0.909 ± 0.015, and an area under the curve of 0.939 ± 0.009 when using a support vector machine with a linear kernel. This study demonstrated that 3D MRI-based SSA can realistically visualize statistical results on surface models and may facilitate the understanding of complex shape features.

The utility of automatic segmentation of kidney MRI in chronic kidney disease using a 3D convolutional neural network.

We developed a 3D convolutional neural network (CNN)-based automatic kidney segmentation method for patients with chronic kidney disease (CKD) using MRI Dixon-based T1-weighted in-phase (IP)/opposed-phase (OP)/water-only (WO) images. The dataset comprised 100 participants with renal dysfunction (RD; eGFR < 45 mL/min/1.73 m2) and 70 without (non-RD; eGFR ≥ 45 mL/min/1.73 m2). The model was applied to the right, left, and both kidneys; it was first evaluated on the non-RD group data and subsequently on the combined data of the RD and non-RD groups. For bilateral kidney segmentation of the non-RD group, the best performance was obtained when using IP image, with a Dice score of 0.902 ± 0.034, average surface distance of 1.46 ± 0.75 mm, and a difference of - 27 ± 21 mL between ground-truth and automatically computed volume. Slightly worse results were obtained for the combined data of the RD and non-RD groups and for unilateral kidney segmentation, particularly when segmenting the right kidney from the OP images. Our 3D CNN-assisted automatic segmentation tools can be utilized in future studies on total kidney volume measurements and various image analyses of a large number of patients with CKD.

Utility of magnetic resonance imaging for differentiating malignant mesenchymal tumors of the uterus from T2-weighted hyperintense leiomyomas.

PURPOSE: To generate a new discrimination method to distinguish between malignant mesenchymal tumors of the uterus and T2-weighted hyperintense leiomyoma based on magnetic resonance imaging findings and clinical features. MATERIALS AND METHODS: Data from 32 tumors of 32 patients with malignant mesenchymal tumors of the uterus and from 34 tumors of 30 patients with T2-weighted hyperintense leiomyoma were analyzed. Clinical parameters, qualitative magnetic resonance imaging features, including computed diffusion-weighted imaging, and quantitative characteristics of magnetic resonance imaging of these two tumor types were compared. Predictive values for malignant mesenchymal tumors of the uterus were calculated using variant discriminant analysis. RESULTS: The T1 bright area on qualitative assessment and mean apparent diffusion coefficient value on quantitative assessment yielded the most independent magnetic resonance imaging differentiators of malignant mesenchymal tumors of the uterus and T2-weighted hyperintense leiomyoma. The classification accuracy of the variant discriminant analysis based on three selected findings, i.e., a T1 bright area, computed diffusion-weighted imaging with a b-value of 2000s/mm2 (cDWI2000), and T2-hypointense bands, was 84.8% (56/66), indicating high accuracy. CONCLUSIONS: Variant discriminant analysis using the T1 bright area, cDWI2000, and T2-hypointense bands yielded high accuracy for differentiating between malignant mesenchymal tumors of the uterus and T2-weighted hyperintense leiomyoma.