Current Medical Imaging Reviews最新文献

Objective: The purpose of this study was to compare the image quality of different MRI sequences regarding the presentation of Dermatofibrosarcoma Protuberans (DFSP).

Materials and methods: We retrospectively collected MRI images of 40 patients who had been pathologically diagnosed with DFSP, including 21 primary tumors and 19 recurrent tumors. The image quality of different MRI sequences was assessed subjectively by two radiologists, taking into account the display of the lesions, artifacts, and distortions, as well as the overall impact of the image quality.

Results: Among the 40 cases, 22 cases involved the trunk, 14 cases involved the shoulders and limbs, 2 cases involved the head and neck, 1 case involved the breast, and 1 case involved the groin. In terms of image quality, fat suppression T2-weighted images were superior to T1-weighted images and T2-weighted images (P<0.05). The difference between fat suppression T2-weighted images and contrast-enhanced images was not significant (P>0.05). As far as lesion contrast is concerned, diffusion-weighted images, fat suppression T2-weighted images, and contrast-enhanced images did not differ significantly (P>0.05). On the DWI images, there were severe magnetic artifacts and deformations.

Conclusions: Fat suppression T2-weighted images and enhanced sequences produce the highest quality images, while diffusion-weighted images provide the best lesion contrast.

Introduction: A recently developed deep-learning-based automatic evaluation model provides reliable and efficient Cobb angle measurements for scoliosis diagnosis. However, few studies have explored its clinical application, and external validation is lacking. Therefore, this study aimed to explore the value of automated assessment models in clinical practice by comparing deep-learning models with manual measurement methods.

Methods: The 481 spine radiographs from an open-source dataset were divided into training and validation sets, and 119 spine radiographs from a private dataset were used as the test set. The mean Cobb angle values assessed by three physicians in the hospital's PACS system served as the reference standard. The results of Seg4Reg, VFLDN, and manual measurement were statistically analyzed. The intra-class correlation coefficients (ICC) and the Pearson correlation coefficient (PCC) were used to compare their reliability and correlation. The Bland-Altman method was used to compare their agreement. The Kappa statistic was used to compare the consistency of Cobb angles at different severity levels.

Results: The mean Cobb angle values measured were 35.89° ± 9.33° with Seg4Reg, 31.54° ± 9.78° with VFLDN, and 32.23° ± 9.28° with manual measurement. The ICCs for the reliability of Seg4Reg and VFLDN were 0.809 and 0.974, respectively. The PCC and MAD between Seg4Reg and manual measurements were 0.731 (p<0.001) and 6.51°, while those between VFLDN and manual measurements were 0.952 (p<0.001) and 2.36°. The Kappa statistic indicated VFLDN (k= 0.686, p< 0.001) was superior to Seg4Reg and manual measurements for Cobb angle severity classification.

Conclusion: The deep-learning-based automatic scoliosis Cobb angle assessment model is feasible in clinical practice. Specifically, the keypoint-based VFLDN is more valuable in actual clinical work with higher accuracy, transparency, and interpretability.

Background: Interstitial lung diseases (ILD) encompass various disorders characterized by inflammation and/or fibrosis in the lung interstitium. These conditions produce distinct patterns in High-Resolution Computed Tomography (HRCT).

Objective: We employ a deep learning method to diagnose the most commonly encountered patterns in ILD differentially.

Materials and methods: Patients were categorized into usual interstitial pneumonia (UIP), nonspecific interstitial pneumonia (NSIP), and normal lung parenchyma groups. VGG16 and VGG19 deep learning architectures were utilized. 85% of each pattern was used as training data for the artificial intelligence model. The models were then tasked with diagnosing the patterns in the test dataset without human intervention. Accuracy rates were calculated for both models.

Results: 1 The success of the VGG16 model in the test phase was 95.02% accuracy. 2 Using the same data, 98.05% accuracy results were obtained in the test phase of the VGG19 model.

Conclusion: Deep Learning models showed high accuracy in distinguishing the two most common patterns of ILD.

Background: 68Ga-labeled prostate-specific membrane antigen positron emission tomography-computed tomography (68Ga-PSMA PET/CT) has led to altered treatment plans for prostate cancer (PCa) patients.

Objective: This study aimed to investigate the impact of 68Ga-PSMA PET/CT on overall survival (OS) and management in PCa.

Methods: Consecutive 100 patients who had 68Ga-PSMA PET/CT and conventional imaging (CI) were included in this retrospective study. Disease stages and treatment plans according to both CI and 68Ga-PSMA PET/CT were compared. The effect of 68Ga-PSMA PET/CT on OS was assessed.

Results: After 68Ga-PSMA PET/CT, the stage changed in 64 patients (64%). By the reason of 68Ga-PSMA PET/CT findings, treatment plans based on CI were changed in 73 patients (73%). According to the ROC analysis, patients with a PSA value below 8 had higher rates of change in staging (p<0.0001) and treatment (p=0.034). Both a PSA below 8 (OR 8.79 95% CI (2.72-28.43), p<0.001), and having a hormone-sensitive disease at the time of imaging (OR 5.6 95% CI (1.35-23.08), p=0.017) were significant independent factors predicting change in staging with 68Ga-PSMA PET/CT. The results of a phi correlation coefficient analysis showed a significant relationship between therapy and changes in staging (ϕ=0.638, p<0.0001). Two-year OS was statistically different in hormone-sensitive patients with and without treatment change (95% vs 81%, p=0.006).

Conclusion: 68Ga-PSMA PET/CT has the effect of changing the treatment in 73% of PCa patients. There is a positive correlation between the changes in staging and treatment. Survival of hormone sensitive patients has improved due to treatment changes based on PET/CT findings.

Background: A prosthetic device is designed based on the quantitative analysis of muscle MRI which will improve the muscle control achieved with functional electrical stimulation/ guided robotic exoskeletons. Electromyography (EMG) provides muscle functionality information while MRI provides the physiological and functionality of muscles. The sensor feedbacks were used for the bionic prosthesis, but the length of muscle using image processing was not correlated.

Objective: To investigate and perform qualitative and quantitative assessment of thigh muscle using MRI. The objective of the work is to improve the existing VAG signal classification method to diagnose abnormality using MRI for patients undergoing Total knee replacement (TKR).

Methods: A deep learning method for qualitative and quantitative assessment of thigh muscle is done using MRI. In existing prosthetic devices, electrical measurements of a person's muscles are obtained using surface or implantable electrodes. Several methods were used for the classification and diagnostic processes. The existing methods have drawbacks in feature extraction and require experts to design the system. This work combines medical image processing and orthopaedic prosthetics to develop a therapeutic method.

Results & discussion: This design provides much more precise control of prosthetic limbs using the image processing technique. The hybrid CNN swarm-based method measures the muscle structure and functions. Along with the sensor readings, these details are combined for prosthetic control. The implementation was carried out in MATLAB, Sketchuppro, and Arduino IDE.

Conclusion: A combined swarm intelligence and deep learning method were proposed for qualitative and quantitative assessment of thigh muscle. The prosthetic device choice was done from the scanned MRI image like Humerus-T prosthetics, segmental prosthesis and arthrodesis prosthesis. The investigation was done for the Total knee replacement (TKR) approach.

Background: Dermatofibrosarcoma Protuberans (DFSP) is a rare soft tissue sarcoma, accounting for approximately 1% of all tumors; however, DFSP of the breast is extremely rare. Moreover, DFSP generally has a low malignant potential and is characterized by a high rate of local recurrence along with a small but definite risk of metastasis. The risk of metastasis is higher in fibrosarcomatous transformation in DFSP than in ordinary DFSP.

Case report: We have, herein, reported a case of a 61-year-old male patient with fibrosarcomatous transformation in DFSP. Preoperative Dynamic Contrastenhanced Magnetic Resonance Imaging (DCE-MRI) of the breast revealed an oval-shaped mass with heterogeneous internal enhancement, a large vessel embedded within, and a washout curve pattern on kinetic curve analysis. The mass exhibited a hyperintense signal on Diffusion-weighted Imaging (DWI), with a low apparent diffusion coefficient value. Histologically, the bland spindle tumor cells were arranged in a storiform pattern. Areas with the highest histological grade demonstrated increased cellularity, cytological atypia, and mitotic activity. Immunohistochemically, Ki-67 and p53 were highly expressed.

Conclusion: Recognizing the risk and accurately diagnosing fibrosarcomatous transformation in male breast DFSP are critical for improving prognosis and establishing appropriate treatment and follow-up plans. This emphasizes the significance of combining immunohistopathological features with DCE-MRI and DWI to assist clinicians in the early and accurate diagnosis of sarcomas arising from male breast DFSP.

Background: Currently, it is difficult to find a solution to the inverse inappropriate problem, which involves restoring a high-resolution image from a lowresolution image contained within a single image. In nature photography, one can capture a wide variety of objects and textures, each with its own characteristics, most notably the high-frequency component. These qualities can be distinguished from each other by looking at the pictures.

Objective: The goal is to develop an automated approach to identify thyroid nodules on ultrasound images. The aim of this research is to accurately differentiate thyroid nodules using Deep Learning Technique and to evaluate the effectiveness of different localization techniques.

Methods: The method used in this research is to reconstruct a single super-resolution image based on segmentation and classification. The poor-quality ultrasound image is divided into several parts, and the best applicable classification is chosen for each component. Pairs of high- and lowresolution images belonging to the same class are found and used to figure out which image is high-resolution for each segment. Deep learning technology, specifically the Adam classifier, is used to identify carcinoid tumors within thyroid nodules. Measures, such as localization accuracy, sensitivity, specificity, dice loss, ROC, and area under the curve (AUC), are used to evaluate the effectiveness of the techniques.

Results: The results of the proposed method are superior, both statistically and qualitatively, compared to other methods that are considered one of the latest and best technologies. The developed automated approach shows promising results in accurately identifying thyroid nodules on ultrasound images.

Conclusion: The research demonstrates the development of an automated approach to identify thyroid nodules within ultrasound images using super-resolution single-image reconstruction and deep learning technology. The results indicate that the proposed method is superior to the latest and best techniques in terms of accuracy and quality. This research contributes to the advancement of medical imaging and holds the potential to improve the diagnosis and treatment of thyroid nodules.

Background: Accurate identification of vascular lumen region founded the base of bubble detection and bubble grading, which played a significant role in the detection of vascular gas emboli for the diagnosis of decompression sickness.

Objectives: To assist in the detection of vascular bubbles, it is crucial to develop an automatic algorithm that could identify vascular lumen areas in ultrasound videos with the interference of bubble presence.

Methods: This article proposed an automated vascular lumen region recognition (VLRR) algorithm that could sketch the accurate boundary between vessel lumen and tissues from dynamic 2D ultrasound videos. It adopts 2D ultrasound videos of the lumen area as input and outputs the frames with circled vascular lumen boundary of the videos. Normalized cross-correlation method, distance transform technique, and region growing technique were adopted in this algorithm. Results A double-blind test was carried out to test the recognition accuracy of the algorithm on 180 samples in the images of 6 different grades of bubble videos, during which, intersection over union and pixel accuracy were adopted as evaluation metrics. The average IOU on the images of different bubble grades reached 0.76. The mean PA on 6 of the images of bubble grades reached 0.82.

Conclusion: It is concluded that the proposed method could identify the vascular lumen with high accuracy, potentially applicable to assist clinicians in the measurement of the severity of vascular gas emboli in clinics.

Background: Patients with diffuse large B-cell lymphoma (DLBCL) often experience a poor prognosis due to cardiac damage induced by anthracycline chemotherapy, with left ventricular diastolic dysfunction manifesting early. Vector Flow Mapping (VFM) is a novel technology, and its effectiveness in detecting left ventricular diastolic dysfunction following anthracycline chemotherapy remains unverified.

Objects: This study evaluates left ventricular diastolic function in DLBCL patients after anthracycline chemotherapy using vector flow mapping (VFM).

Materials and methods: We prospectively enrolled 54 DLBCL patients who had undergone anthracycline chemotherapy (receiving a minimum of 4 cycles) as the case group and 54 age- and sex-matched individuals as controls. VFM assessments were conducted in the case group pre-chemotherapy (T0), post-4 chemotherapy cycles (T4), and in the control group. Measurements included basal, middle, and apical segment energy loss (ELb, ELm, ELa) and intraventricular pressure differences (IVPDb, IVPDm, IVPDa) across four diastolic phases: isovolumic relaxation (D1), rapid filling (D2), slow filling (D3), and atrial contraction (D4).

Results: When comparing parameters between the control and case groups at T0, no significant differences were observed in general data, conventional ultrasound parameters, and VFM parameters (all P > 0.05). From T0 to T4, ELa significantly increased throughout the diastole cycle (all P < 0.05); ELm increased only during D4 (all P < 0.05); and ELb increased during D1, D2, and D4 (all P < 0.05). All IVPD measurements (IVPDa, IVPDm, IVPDb) increased during D1 and D4 (all P < 0.05) but decreased during D2 and D3 (all P < 0.05). Significant positive correlations were identified between ELa-D4, IVPDa-D4, and parameters A, e', E/e,' and LAVI (all r > 0.5, all P < 0.001). Negative correlations were noted with E/A for ELa- D4 IVPDa-D4 (all r < -0.5, all P < 0.001). Positive correlations were observed for IVPDa-D1, IVPDa-D2 with E, E/e', and LAVI (0.3

Conclusion: VFM parameters demonstrate a certain correlation with conventional diastolic function parameters and show promise in assessing left ventricular diastolic function. Furthermore, VFM parameters exhibit greater sensitivity to early diastolic function changes, suggesting that VFM could be a novel method for evaluating differences in left ventricular diastolic function in DLBCL patients before and after chemotherapy.

Objective: The aim of this study was to investigate the feasibility of muscle CT radiomics in identifying gout.

Materials and methods: A total of 30 gout patients and 20 non-gout cases with CT examinations of ankles were analyzed by using the methods of CT radiomics. CT radiomics features of the soleus muscle were extracted using the software of a 3D slicer, and then gout cases and non-gout cases were compared. The radiomics features that were significantly different between the two groups were then processed with machine learning methods. Receiver operating characteristic curve analysis was used to evaluate the diagnostic performance.

Results: Five CT radiomics features were significantly different between gout cases and non-gout cases (P < 0.05). In the logic regression, the AUC, sensitivity, specificity, and accuracy were 0.738, 77% (46/60), 70% (28/40), and 74% (74/100), respectively. In the Random forest, Xgboost, and support vector machine analysis, the accuracy was 0.901, 0.833, and 0.875, respectively.

Conclusion: From this study, it can be concluded that muscle CT radiomics is feasible in identifying gout.