Quantitative Imaging in Medicine and Surgery最新文献

Background: There are different types of vena cava filter (VCF) available in clinical practice. However, limited data exist to determine whether one type is superior to another, and no single VCF is universally recommended in clinical guidelines. The objective of this study was to investigate the safety and efficacy of a novel VCF, Octoparms, for the prevention of pulmonary embolism (PE) and to compare it with the Celect filter.

Methods: This multicenter, randomized, open-label, parallel, positive-controlled, noninferiority trial was conducted in 10 centers across 6 provinces in China from October 2017 to March 2019. Patients who had confirmed lower extremity deep vein thrombosis or PE or who were at risk of PE with a clinical indication for VCF placement due to contraindication to or failure of anticoagulant therapy were included in the trial. The sample size for this trial was based on the assumption that the clinical success rate would be 95% and the noninferiority margin would be 10% for both filters. Each patient underwent baseline testing and was randomized using a web-based central system. Any additional interventions or standard treatments patients received along with the VCF placement were recorded. The primary endpoint was the overall clinical success rate, including technical and clinical success of filter placement and retrieval. The secondary endpoint was the safety of filter placement and retrieval, encompassing procedure-related and filter-related complications.

Results: A total of 188 patients were included and were divided into two groups: the Octoparms group (n=94) and the Celect group (n=94). Baseline characteristics and demographics were comparable between the two groups (P>0.05). Technical and clinical success rates for filter placement were achieved in 100% (188/188) of patients. The median dwelling time was 12.0 days (range, 4-190 days). Ten VCFs were left in situ as permanent devices. Of the remaining 178 patients, technical success and clinical success rates for filter retrieval were both achieved in 100% of cases (178/178). Clinical success rates were 92.6% (87/94) for the Octoparms group and 96.8% (91/94) for the Celect group, with a difference of -4.2% (hazard ratio 2.441, 95% confidence interval 0.612-9.741; P=0.206). The lower limit was greater than the noninferiority margin of -10%. Eight patients experienced a total of eight procedure-related complications. No filter-related complications, such as migration, deformation, inferior vena cava (IVC) penetration, peripheral organ damage, or IVC stenosis/occlusion, were observed (P>0.05).

Conclusions: The Octoparms filter exhibited a high rate of clinical success and a low rate of complications during placement and retrieval, demonstrating noninferiority to the Celect filter.

Background: The effectiveness of coronary computed tomography (CT) angiography in assessing stent restenosis is hindered by heavy metal artifacts. This study aimed to evaluate the image quality of monoenergetic reconstructions and iodine density map for coronary stent imaging using an 8-cm dual-layer detector spectral CT.

Methods: In this study, 8 stents with a diameter <3 mm (group A) and 10 with a diameter ≥3 mm (group B) were placed in plastic tubes filled with iodinated contrast media and scanned. The internal diameter of the prepared stents was then measured by intravascular ultrasound. The reconstructed images included iodine density maps, conventional images, and different energy levels. The visualization of the stent lumen and stent structure was subjectively assessed using a 4-point Likert scale. The objective evaluation was performed using the in-stent lumen signal-to-noise ratio (SNRis), non-stent lumen SNR (SNRns), internal diameter difference (IDD), and blooming artifact index (BAI). The Friedman test and analysis of variance were used for multiple comparisons.

Results: For lumen visualization, the optimal monoenergetic images received the highest score for both group A (2.56±0.51) and group B (3.1±0.55). Multiple comparisons showed that there were significant differences between the optimal monoenergetic images and iodine density maps. However, for stent structure, iodine density maps received the highest score for group A (3.0±0.52) and group B (3.8±0.41). For quantitative assessment, the optimal monoenergetic images had the highest SNRis and SNRns, while the iodine density maps had the lowest SNRis and SNRns. For IDD and BAI, the iodine density maps yielded the smallest value.

Conclusions: The monoenergetic images on the second-generation dual-layer detector CT provide better visualization of the lumen and higher SNR. However, iodine density maps are superior for evaluating stent structure and IDD and BAI compared to monoenergetic and conventional reconstructions.

Background: Photorefractive intrastromal corneal cross-linking (PiXL) treatment corrects myopia by enhancing localized central corneal biomechanics. However, the dose-effect relationship between the changes in corneal biomechanics and alterations in corneal curvature resulting from this treatment remain unclear. We therefore developed an acoustic radiation force optical coherence elastography (ARF-OCE) technique to investigate the dose-effect relationship in PiXL.

Methods: ARF-OCE measurements and corneal topography were performed 3 days before and 1 week after PiXL treatment. Depth-resolved Young's modulus images of the in vivo corneas were obtained based on the phase velocity of the Lamb wave. PiXL treatments with five ultraviolet-A (UVA) energy doses (5.4, 15, 25, 35, and 45 J/cm²) were administered to rabbit corneas in vivo (n=15).

Results: The percentage change in Young's modulus (ΔE%) of the cornea increased from 0.26 to 1.71 as the UVA energy dose increased from group I (5.4 J/cm²) to group V (45 J/cm²). Meanwhile, the change in the mean keratometry (ΔK_m ) of the cornea increased from 0.40 to 2.10 diopters (D) as the UVA energy dose increased from group I to group IV (35 J/cm²). Furthermore, a statistically significant positive correlation was observed between ΔE% and ΔK_m in groups I to IV.

Conclusions: With increasing UVA energy dose, the corneal Young's modulus significantly increased. Given the observed correlation, ΔE% holds promise as a new quantitative biomechanical parameter for determining the dose-effect relationship in PiXL treatment. It should be emphasized that there may be an inflection point of ΔE%, at which corneal keratometry ceases to flatten and begins to increase. The ARF-OCE system has demonstrated its efficacy in quantitatively assessing changes in corneal biomechanics in vivo following PiXL treatment. This technique has great potential in facilitating the quantitative determination of the dose-effect relationship in PiXL treatment.

When treating femoral fractures with closed reduction intramedullary nail fixation, excessive residual displacement of the fracture end can affect the fixation effect and prolong the healing time, which is difficult to manage intraoperatively. This article introduces a simple steel wire cerclage fixation technique, which is percutaneous and can control the incision within 1 cm without excessive reliance on specially designed surgical instruments. It is suitable for the reduction and fixation of oblique femoral metaphysis fractures and butterfly-shaped femoral shaft fracture blocks.

Background: The precise identification of the position and form of a tumor mass can improve early diagnosis and treatment. However, due to the complicated tumor categories and varying sizes and forms, the segregation of brain gliomas and their internal sub-regions is still very challenging. This study sought to design a new deep-learning network based on three-dimensional (3D) U-Net to address its shortcomings in brain tumor segmentation (BraTS) tasks.

Methods: We developed a 3D dilated multi-scale residual attention U-Net (DMRA-U-Net) model for magnetic resonance imaging (MRI) BraTS. It used dilated convolution residual (DCR) modules to better process shallow features, multi-scale convolution residual (MCR) modules in the bottom encoding path to create richer and more comprehensive feature expression while reducing overall information loss or blurring, and a channel attention (CA) module between the encoding and decoding paths to address the problem of retrieving and preserving important features during the processing of deep feature maps.

Results: The BraTS 2018-2021 datasets served as the training and evaluation datasets for this study. Further, the proposed architecture was assessed using metrics such as the dice similarity coefficient (DSC), Hausdorff distance (HD), and sensitivity (Sens). The DMRA U-Net model segments the whole tumor (WT), and the tumor core (TC), and the enhancing tumor (ET) regions of brain tumors. Using the suggested architecture, the DSCs were 0.9012, 0.8867, and 0.8813, the HDs were 28.86, 13.34, and 10.88 mm, and the Sens was 0.9429, 0.9452, and 0.9303 for the WT, TC, and ET regions, respectively. Compared to the traditional 3D U-Net, the DSC of the DMRA U-Net increased by 4.5%, 2.5%, and 0.8%, the HD of the DMRA U-Net decreased by 21.83, 16.42, and 10.00, the Sens of the DMRA U-Net increased by 0.4%, 0.7%, and 1.4% for the WT, TC, and ET regions, respectively. Further, the results of the statistical comparison of the performance indicators revealed that our model performed well generally in the segmentation of the WT, TC, and ET regions.

Conclusions: We developed a promising tumor segmentation model. Our solution is open sourced and is available at: https://github.com/Gold3nk/dmra-unet.