Search Results (40)

Objectives: This study aimed to evaluate the usefulness of a short magnetic resonance (MR) protocol for knee evaluation, using 3D PDW SPAIR sequences compared with traditional 2D ones. Methods: A prospective analysis included 76 patients with knee pain. MR was performed using a 1.5 T scanner. The standard protocol consisted of multiplanar 2D proton density-weighted (PDW) SPectral Attenuated Inversion Recovery (SPAIR) and additional T1-weighted (T1W) and T2-weighted (T2W) sequences, with a total acquisition time of 17 min. The simulated short protocol included a 3D PDW SPAIR sequence with isotropic voxels and a slice thickness of 0.6 mm, coronal T1W, and gradient echo (GRE) axial sequences, with a total acquisition time of 9 min. Two radiologists independently reviewed images and collected pathological processes. Results: The 3D PDW SPAIR sequence demonstrated a significantly higher subjective image quality compared to standard 2D sequences [κ = 0.712 (p < 0.001) vs. κ = 0.144 (p = 0.63); p < 0.001]. Artifacts were not significantly different between the two protocols (p = 0.201). Qualitative assessments showed superior ratings for 3D images (excellent quality: 72.4% vs. 26.3% for 3D and 2D, respectively; p < 0.001). Diagnostic performance was comparable between the two protocols for ACL injuries, medial and lateral collateral ligament injuries, and chondropathies. Three-dimensional sequences were more effective in detecting medial meniscal injuries (p < 0.001), particularly radial and complex tears, likely due to higher spatial resolution and multiplanar reconstruction capability. Conclusions: The 3D PDW SPAIR sequence offers advantages in knee MRI study, including improved image quality, reduced acquisition time, and superior detection of meniscal injuries. Full article

Accurate diagnosis of knee joint injuries from magnetic resonance (MR) images is critical for patient care. Background/Objectives: While deep learning has advanced 3D MR image analysis, its reliance on extensive labeled datasets is a major hurdle for diverse knee pathologies. Few-shot learning (FSL) addresses this by enabling models to classify new conditions from minimal annotated examples, often leveraging knowledge from related tasks. However, creating robust 3D FSL frameworks for varied knee injuries remains challenging. Methods: We introduce MedNet-FS, a 3D FSL framework that effectively classifies knee injuries by utilizing domain-specific pre-trained weights and generalized end-to-end (GE2E) loss for discriminative embeddings. Results: MedNet-FS, with knee-MRI-specific pre-training, significantly outperformed models using generic or other medical pre-trained weights and approached supervised learning performance on internal datasets with limited samples (e.g., achieving an area under the curve (AUC) of 0.76 for ACL tear classification with k = 40 support samples on the MRNet dataset). External validation on the KneeMRI dataset revealed challenges in classifying partially torn ACL (AUC up to 0.58) but demonstrated promising performance for distinguishing intact versus fully ruptured ACLs (AUC 0.62 with k = 40). Conclusions: These findings demonstrate that tailored FSL strategies can substantially reduce data dependency in developing specialized medical imaging tools. This approach fosters rapid AI tool development for knee injuries and offers a scalable solution for data scarcity in other medical imaging domains, potentially democratizing AI-assisted diagnostics, particularly for rare conditions or in resource-limited settings. Full article

Background/Objectives: The correct angular placement of the tibial tunnel is crucial to ensure graft tension, maintain knee stability, and ensure optimal clinical outcomes after anterior cruciate ligament (ACL) reconstruction. While 3D imaging methods such as MRI and CT are the gold standard for evaluating tunnel positioning, their routine use is limited by cost, availability, and time constraints. In clinical practice, 2D radiographs are more accessible but lack established reliability in accurately estimating tunnel angles. The aim of this study was to convert 2D radiographic angular measurements used in the evaluation of patients undergoing anterior cruciate ligament reconstruction into 3D values with a simple method and to compare these measurements with three-dimensional angles calculated using conventional MRI and CT. Methods: This retrospective study included 38 patients who underwent anatomic anterior cruciate ligament reconstruction. Postoperative radiographs and MR images were analyzed to determine the tibial tunnel angles. The angles calculated from 2D radiographs were statistically analyzed for their correlation with the actual 3D angles measured by MRI. Results: The analysis showed a strong correlation between tibial tunnel angles from radiographs and MRI, with minimal, non-significant differences. This suggests that radiographs can provide a reliable estimate of tibial tunnel angles. Conclusions: These findings suggest that radiographs can predict tibial tunnel angles in ACL reconstruction as accurately as MRI. This method can guide the correct tunnel angle and facilitate postoperative evaluation. Further studies are needed to confirm these results across various populations and techniques. Full article

In the field of knee imaging, the incorporation of MR-based pseudo-CT synthesis holds the potential to mitigate the need for separate CT scans, simplifying workflows, enhancing patient comfort, and reducing radiation exposure. In this work, we present a novel DL framework, grounded in the development of the Cycle-Consistent Generative Adversarial Network (CycleGAN) method, tailored specifically for the synthesis of pseudo-CT images in knee imaging to surmount the limitations of current methods. Upon visually examining the outcomes, it is evident that the synthesized pseudo-CTs show an excellent quality and high robustness. Despite the limited dataset employed, the method is able to capture the particularities of the bone contours in the resulting image. The experimental Mean Absolute Error (MAE), Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), Zero-Normalized Cross Correlation (ZNCC), Mutual Information (MI), Relative Change (RC), and absolute Relative Change (|RC|) report values of 30.4638 ± 7.4770, 28.1168 ± 1.5245, 0.9230 ± 0.0217, 0.9807 ± 0.0071, 0.8548 ± 0.1019, 0.0055 ± 0.0265, and 0.0302 ± 0.0218 (median ± median absolute deviation), respectively. The voxel-by-voxel correlation plot shows an excellent correlation between pseudo-CT and ground-truth CT Hounsfield units (m = 0.9785; adjusted $R^2$ = 0.9988; $\rho$ = 0.9849; p < 0.001). The Bland–Altman plot shows that the average of the differences is low (($H{U}_{CT}-H{U}_{pseudo-CT}$ = 0.7199 ± 35.2490; 95% confidence interval [−68.3681, 69.8079]). This study represents the first reported effort in the field of MR-based knee pseudo-CT synthesis, shedding light to significantly advance the field of knee imaging. Full article

Objectives: This study delves into the cutting-edge field of deep learning techniques, particularly deep convolutional neural networks (DCNNs), which have demonstrated unprecedented potential in assisting radiologists and orthopedic surgeons in precisely identifying meniscal tears. This research aims to evaluate the effectiveness of deep learning models in recognizing, localizing, describing, and categorizing meniscal tears in magnetic resonance images (MRIs). Materials and methods: This systematic review was rigorously conducted, strictly following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Extensive searches were conducted on MEDLINE (PubMed), Web of Science, Cochrane Library, and Google Scholar. All identified articles underwent a comprehensive risk of bias analysis. Predictive performance values were either extracted or calculated for quantitative analysis, including sensitivity and specificity. The meta-analysis was performed for all prediction models that identified the presence and location of meniscus tears. Results: This study’s findings underscore that a range of deep learning models exhibit robust performance in detecting and classifying meniscal tears, in one case surpassing the expertise of musculoskeletal radiologists. Most studies in this review concentrated on identifying tears in the medial or lateral meniscus and even precisely locating tears—whether in the anterior or posterior horn—with exceptional accuracy, as demonstrated by AUC values ranging from 0.83 to 0.94. Conclusions: Based on these findings, deep learning models have showcased significant potential in analyzing knee MR images by learning intricate details within images. They offer precise outcomes across diverse tasks, including segmenting specific anatomical structures and identifying pathological regions. Contributions: This study focused exclusively on DL models for identifying and localizing meniscus tears. It presents a meta-analysis that includes eight studies for detecting the presence of a torn meniscus and a meta-analysis of three studies with low heterogeneity that localize and classify the menisci. Another novelty is the analysis of arthroscopic surgery as ground truth. The quality of the studies was assessed against the CLAIM checklist, and the risk of bias was determined using the QUADAS-2 tool. Full article

Bone marrow lesion (BML) volume is a potential biomarker of knee osteoarthritis (KOA) as it is associated with cartilage degeneration and pain. However, segmenting and quantifying the BML volume is challenging due to the small size, low contrast, and various positions where the BML may occur. It is also time-consuming to delineate BMLs manually. In this paper, we proposed a fully automatic segmentation method for BMLs without requiring human intervention. The model takes intermediate weighted fat-suppressed (IWFS) magnetic resonance (MR) images as input, and the output BML masks are evaluated using both regular 2D Dice similarity coefficient (DSC) of the slice-level area metric and 3D DSC of the subject-level volume metric. On a dataset with 300 subjects, each subject has a sequence of 36 IWFS MR images approximately. We randomly separated the dataset into training, validation, and testing sets with a 70%/15%/15% split at the subject level. Since not every subject or image has a BML, we excluded the images without a BML in each subset. The ground truth of the BML was labeled by trained medical staff using a semi-automatic tool. Compared with the ground truth, the proposed segmentation method achieved a Pearson’s correlation coefficient of 0.98 between the manually measured volumes and automatically segmented volumes, a 2D DSC of 0.68, and a 3D DSC of 0.60 on the testing set. Although the DSC result is not high, the high correlation of 0.98 indicates that the automatically measured BML volume is strongly correlated with the manually measured BML volume, which shows the potential to use the proposed method as an automatic measurement tool for the BML biomarker to facilitate the assessment of knee OA progression. Full article

To assess the impact of a deep learning (DL) denoising reconstruction algorithm applied to identical patient scans acquired with two different voxel dimensions, representing distinct spatial resolutions, this IRB-approved prospective study was conducted at a tertiary pediatric center in compliance with the Health Insurance Portability and Accountability Act. A General Electric Signa Premier unit (GE Medical Systems, Milwaukee, WI) was employed to acquire two DTI (diffusion tensor imaging) sequences of the left knee on each child at 3T: an in-plane 2.0 × 2.0 ${\mathrm{m}\mathrm{m}}^2$ with section thickness of 3.0 mm and a 2 mm³ isovolumetric voxel; neither had an intersection gap. For image acquisition, a multi-band DTI with a fat-suppressed single-shot spin-echo echo-planar sequence (20 non-collinear directions; b-values of 0 and 600 s/mm²) was utilized. The MR vendor-provided a commercially available DL model which was applied with 75% noise reduction settings to the same subject DTI sequences at different spatial resolutions. We compared DTI tract metrics from both DL-reconstructed scans and non-denoised scans for the femur and tibia at each spatial resolution. Differences were evaluated using Wilcoxon-signed ranked test and Bland–Altman plots. When comparing DL versus non-denoised diffusion metrics in femur and tibia using the 2 mm × 2 mm × 3 mm voxel dimension, there were no significant differences between tract count (p = 0.1, p = 0.14) tract volume (p = 0.1, p = 0.29) or tibial tract length (p = 0.16); femur tract length exhibited a significant difference (p < 0.01). All diffusion metrics (tract count, volume, length, and fractional anisotropy (FA)) derived from the DL-reconstructed scans, were significantly different from the non-denoised scan DTI metrics in both the femur and tibial physes using the 2 mm³ voxel size (p < 0.001). DL reconstruction resulted in a significant decrease in femorotibial FA for both voxel dimensions (p < 0.01). Leveraging denoising algorithms could address the drawbacks of lower signal-to-noise ratios (SNRs) associated with smaller voxel volumes and capitalize on their better spatial resolutions, allowing for more accurate quantification of diffusion metrics. Full article

In this paper, we propose a dense multi-scale adaptive graph convolutional network (DMA-GCN) method for automatic segmentation of the knee joint cartilage from MR images. Under the multi-atlas setting, the suggested approach exhibits several novelties, as described in the following. First, our models integrate both local-level and global-level learning simultaneously. The local learning task aggregates spatial contextual information from aligned spatial neighborhoods of nodes, at multiple scales, while global learning explores pairwise affinities between nodes, located globally at different positions in the image. We propose two different structures of building models, whereby the local and global convolutional units are combined by following an alternating or a sequential manner. Secondly, based on the previous models, we develop the DMA-GCN network, by utilizing a densely connected architecture with residual skip connections. This is a deeper GCN structure, expanded over different block layers, thus being capable of providing more expressive node feature representations. Third, all units pertaining to the overall network are equipped with their individual adaptive graph learning mechanism, which allows the graph structures to be automatically learned during training. The proposed cartilage segmentation method is evaluated on the entire publicly available Osteoarthritis Initiative (OAI) cohort. To this end, we have devised a thorough experimental setup, with the goal of investigating the effect of several factors of our approach on the classification rates. Furthermore, we present exhaustive comparative results, considering traditional existing methods, six deep learning segmentation methods, and seven graph-based convolution methods, including the currently most representative models from this field. The obtained results demonstrate that the DMA-GCN outperforms all competing methods across all evaluation measures, providing $DSC=95.71\%$ and $DSC=94.02\%$ for the segmentation of femoral and tibial cartilage, respectively. Full article

This study introduces a hybrid analytical super-resolution (SR) pipeline aimed at enhancing the resolution of medical magnetic resonance imaging (MRI) scans. The primary objective is to overcome the limitations of clinical MRI resolution without the need for additional expensive hardware. The proposed pipeline involves three key steps: pre-processing to re-slice and register the image stacks; SR reconstruction to combine information from three orthogonal image stacks to generate a high-resolution image stack; and post-processing using an artefact reduction convolutional neural network (ARCNN) to reduce the block artefacts introduced during SR reconstruction. The workflow was validated on a dataset of six knee MRIs obtained at high resolution using various sequences. Quantitative analysis of the method revealed promising results, showing an average mean error of 1.40 ± 2.22% in voxel intensities between the SR denoised images and the original high-resolution images. Qualitatively, the method improved out-of-plane resolution while preserving in-plane image quality. The hybrid SR pipeline also displayed robustness across different MRI sequences, demonstrating potential for clinical application in orthopaedics and beyond. Although computationally intensive, this method offers a viable alternative to costly hardware upgrades and holds promise for improving diagnostic accuracy and generating more anatomically accurate models of the human body. Full article

Introduction: The objective of this study was to assess the bi-exponential relaxation times and fractions of the short and long components of the human patellar tendon ex vivo using three-dimensional ultrashort echo time T1ρ (3D UTE-T1ρ) imaging. Materials and Methods: Five cadaveric human knee specimens were scanned using a 3D UTE-T1ρ imaging sequence on a 3T MR scanner. A series of 3D UTE-T1ρ images were acquired and fitted using single-component and bi-component models. Single-component exponential fitting was performed to measure the UTE-T1ρ value of the patellar tendon. Bi-component analysis was performed to measure the short and long UTE-T1ρ values and fractions. Results: The single-component analysis showed a mean single-component UTE-T1ρ value of 8.4 ± 1.7 ms for the five knee patellar tendon samples. Improved fitting was achieved with bi-component analysis, which showed a mean short UTE-T1ρ value of 5.5 ± 0.8 ms with a fraction of 77.6 ± 4.8%, and a mean long UTE-T1ρ value of 27.4 ± 3.8 ms with a fraction of 22.4 ± 4.8%. Conclusion: The 3D UTE-T1ρ sequence can detect the single- and bi-exponential decay in the patellar tendon. Bi-component fitting was superior to single-component fitting. Full article

Foot drop can have a variety of causes, including the common peroneal nerve (CPN) injuries, and is often difficult to diagnose. We aimed to develop a deep learning-based algorithm that can classify foot drop with CPN injury in patients with knee MRI axial images only. In this retrospective study, we included 945 MR image data from foot drop patients confirmed with CPN injury in electrophysiologic tests (n = 42), and 1341 MR image data with non-traumatic knee pain (n = 107). Data were split into training, validation, and test datasets using a 8:1:1 ratio. We used a convolution neural network-based algorithm (EfficientNet-B5, ResNet152, VGG19) for the classification between the CPN injury group and the others. Performance of each classification algorithm used the area under the receiver operating characteristic curve (AUC). In classifying CPN MR images and non-CPN MR images, EfficientNet-B5 had the highest performance (AUC = 0.946), followed by the ResNet152 and the VGG19 algorithms. On comparison of other performance metrics including precision, recall, accuracy, and F1 score, EfficientNet-B5 had the best performance of the three algorithms. In a saliency map, the EfficientNet-B5 algorithm focused on the nerve area to detect CPN injury. In conclusion, deep learning-based analysis of knee MR images can successfully differentiate CPN injury from other etiologies in patients with foot drop. Full article

Different bone bruise patterns observed using magnetic resonance imaging (MRI) after non-contact anterior cruciate ligament (ACL) rupture and lateral patellar dislocation may indicate different knee injury mechanisms. In this study, 77 ACL ruptures and 77 patellar dislocations in knee MR images taken from patients with bone bruises at our institution between August 2020 and March 2022 were selected and analyzed. In order to determine typical bone bruising patterns following by ACL rupture and patellar dislocation, sagittal- and transverse-plane images were used to determine bone bruise locations in the directions of medial-lateral and superior-inferior with MR images. The presence, intensity, and location of the bone bruises in specific areas of the femur and tibial after ACL rupture and patellar dislocation were recorded. Relative bone bruise patterns after ACL rupture and patellar dislocation were classified. The results showed that there were four kinds of bone bruise patterns (1-, 2-, 3-, and 4- bone bruises) after ACL rupture. The most common two patterns after ACL rupture were 3- bone bruises (including the lateral femoral condyle and both the lateral-medial tibial plateau, LF + BT; both the lateral-medial femoral condyle and the lateral tibial plateau, BF + LT; and the medial femoral condyle and both the medial and lateral tibial plateau, MF + BT) followed by 4- bone bruises (both the lateral-medial femoral condyle and the tibial plateau, BF + BT), 2- bone bruises (the lateral femoral condyle and tibial plateau, LF + LT; the medial femoral condyle and the lateral tibial plateau, MF + LT; the lateral femoral condyle and the medial tibial plateau, LF + MT; the medial femoral condyle and the tibial plateau, MF + MT; both the lateral-medial tibial plateau, 0 + BT), and 1- bone bruise (only the lateral tibial plateau, 0 + LT). There was only a 1- bone bruise (the latera femoral condyle and medial patella bone bruise) for patellar dislocation, and the most common pattern of patellar dislocation was in the inferior medial patella and the lateral anterior inferior femur. The results suggested that bone bruise patterns after ACL rupture and patellar dislocation are completely different. There were four kinds of bone bruise patterns after non-contact ACL rupture, while there was only one kind of bone bruise pattern after patellar dislocation in patients, which was in the inferior medial patella and lateral anterior inferior femur. Full article

Numerous studies have assessed the performance of magnetic resonance imaging (MRI) in detecting tears of the knee menisci using arthroscopy results as the gold standard, but few have concentrated on the nature of discordant findings. The purpose of this study was to analyze the discordances between 3T MRI and arthroscopic evaluation of the knee meniscus. Medical records of 112 patients who underwent 3T MRI and subsequent arthroscopy of the knee were retrospectively analyzed to determine the accuracy of diagnoses of meniscal tear. Compared with arthroscopy, there were 22 false-negative and 14 false-positive MR interpretations of meniscal tear occurring in 32 patients. Images with errors in diagnosis were retrospectively reviewed by two musculoskeletal radiologists in consensus and all errors were categorized as either unavoidable, equivocal or as interpretation error. Of 36 MR diagnostic errors, there were 16 (44%) unavoidable, 5 (14%) interpretation errors and 15 (42%) equivocal for meniscal tear. The largest categories of errors were unavoidable false-positive MRI diagnoses (71%) and equivocal false-negative MRI diagnoses (50%). All meniscal tears missed by MRI were treated with partial meniscectomy (n = 14) or meniscal repair (n = 8). Discordant findings between 3T MRI and arthroscopic evaluation of the knee meniscus remain a concern and primarily occur due to unavoidable and equivocal errors. Clinicians involved in the diagnosis and treatment of patients with meniscal tears should understand why and how the findings seen on knee MRI and arthroscopy may sometimes differ. Full article

Background and objectives: Isolated tibial tuberosity avulsion fractures are exceptionally uncommon among adults, with limited instances documented in published literature. Here, we describe a case of an isolated tibial tuberosity avulsion fracture in an adult that was treated successfully with the suture bridge repair technique. Patient concerns: A 65-year-old female visited the outpatient department with left knee pain after a slip and fall. Lateral radiographs and sagittal MR images of the left knee revealed the tibial tuberosity avulsion fracture, but the fracture line did not extend into the knee joint space. Surgical intervention was performed on the patient’s knee using an anterior midline approach, involving open reduction and internal fixation. The avulsed tendon was grasped and pulled, and an appropriate suture location was identified. Using a suture hook, the suture was guided through the patellar tendon as near to its uppermost point of the fragment as achievable, and tied over tendon. A single suture limb from each anchor was fastened over the tibial tuberosity to the distally positioned foot print anchor, effectively anchoring the tibial tuberosity using the suture bridge technique. The patient started walking on crutches after one week and was able to walk independently with a brace after two weeks from the operation day. After three months, the patient had regained her mobility to the level prior to the injury and exhibited painless active range of motion from 0 to 130 degrees. Hardware positioning and bony union were maintained at the one-year follow-up. Conclusions: In our case, the open suture bridge fixation method for tibial tuberosity avulsion fractures produced satisfactory results. Open suture bridge fixation may be considered for isolated tibial tuberosity avulsion fractures in adults, especially when the avulsion tip is too small for screw fixation. Full article

Osteoarthritis (OA) is a common degenerative joint disease that affects millions of people worldwide. Magnetic resonance imaging (MRI) has emerged as a powerful tool for the evaluation and monitoring of OA due to its ability to visualize soft tissues and bone with high resolution. This review aims to provide an overview of the current state of MRI in OA, with a special focus on the knee, including protocol recommendations for clinical and research settings. Furthermore, new developments in the field of musculoskeletal MRI are highlighted in this review. These include compositional MRI techniques, such as T2 mapping and T1rho imaging, which can provide additional important information about the biochemical composition of cartilage and other joint tissues. In addition, this review discusses semiquantitative joint assessment based on MRI findings, which is a widely used method for evaluating OA severity and progression in the knee. We analyze the most common scoring methods and discuss potential benefits. Techniques to reduce acquisition times and the potential impact of deep learning in MR imaging for OA are also discussed, as these technological advances may impact clinical routine in the future. Full article