Two-Dimensional and 3-Dimensional MRI Assessment of Progressive Collapsing Foot Deformity-Adult Acquired Flat Foot Deformity.

This article is meant to serve as a reference for radiologists, orthopedic surgeons, and other physicians to enhance their understanding of progressive collapsing foot deformity, also known as adult acquired flat foot deformity. Pathophysiology, imaging findings, especially on MRI and 3-dimensional MRI are discussed with relevant illustrations so that the readers can apply these principles in their practice for better patient managements.

Harmonizing three-dimensional MRI using pseudo-warping field guided GAN.

In pursuit of cultivating automated models for magnetic resonance imaging (MRI) to aid in diagnostics, an escalating demand for extensive, multisite, and heterogeneous brain imaging datasets has emerged. This potentially introduces biased outcomes when directly applied for subsequent analysis. Researchers have endeavored to address this issue by pursuing the harmonization of MRIs. However, most existing image-based harmonization methods for MRI are tailored for 2D slices, which may introduce inter-slice variations when they are combined into a 3D volume. In this study, we aim to resolve inconsistencies between slices by introducing a pseudo-warping field. This field is created randomly and utilized to transform a slice into an artificially warped subsequent slice. The objective of this pseudo-warping field is to ensure that generators can consistently harmonize adjacent slices to another domain, without being affected by the varying content present in different slices. Furthermore, we construct unsupervised spatial and recycle loss to enhance the spatial accuracy and slice-wise consistency across the 3D images. The results demonstrate that our model effectively mitigates inter-slice variations and successfully preserves the anatomical details of the images during the harmonization process. Compared to generative harmonization models that employ 3D operators, our model exhibits greater computational efficiency and flexibility.

A Novel Evaluation for Vertebral Artery Course Using 3D Magnetic Resonance Imaging with Computed Tomography -like Bone Contrast and Magnetic Resonance Angiography: A Proof of Concept Study.

OBJECTIVE: Vertebral artery (VA) injury poses a significant risk in cervical spine surgery, necessitating accurate preoperative assessment. This study aims to introduce and validate a novel approach that combines the Fast field echo that resembles a computed tomography using restricted echo spacing (FRACTURE) sequence with Time of Flight (TOF) Magnetic Resonance Angiography (MRA) for comprehensive evaluation of VA courses in the cervical spine. MATERIALS AND METHODS: A total of eight healthy volunteers and two patients participated in this study. The FRACTURE sequence provided high-resolution bone images of the cervical spine, while TOF MRA offered non-invasive vascular imaging. Fusion images were created by merging FRACTURE and MRA modalities to simultaneously visualize cervical spine structures and VA courses. Board-certified orthopedic spine surgeons independently evaluated images to assess the visibility of anatomical characteristics of the VA course by Likert-scale. RESULTS: The FRACTURE-MRA fusion images effectively depicted the extraosseous course of the VA at the craniovertebral junction, the intraosseous course of the VA at the craniovertebral junction, the VA entrance level to the transverse foramen, and the side-to-side asymmetry of bilateral VAs. Additionally, clinical cases demonstrated the utility of the proposed technique in identifying anomalies and guiding surgical interventions. CONCLUSIONS: The integration of the FRACTURE sequence and TOF MRA presents a promising methodology for the precise evaluation of VA courses in the cervical spine. This approach improves preoperative planning for cervical spine surgery with detailed anatomy and is a valuable alternative to conventional methods without contrast agents.

Novel 3D MRI-based volumetric assessment of rotator cuff musculature demonstrates stronger correlation with preoperative functional status when compared to the Goutallier grading scheme.

BACKGROUND: The Goutallier classification (GC) is used to assess fatty atrophy in rotator cuff (RC) tears, yet limitations exist. A battery of 3-dimensional (3D) magnetic resonance imaging (MRI) volumetric scores (VSs) was developed to provide comprehensive characterization of RC pathology. The purposes of this study were to (1) describe the correlation between GC and VSs for supraspinatus changes in RC tears, (2) characterize the chronicity of RC tears using a battery of 12 VS measurements, and (3) compare GC and VSs to determine which method most closely corresponds with preoperative patient-reported outcome measures (PROMs). METHODS: Preoperative shoulder MRIs were reviewed after arthroscopic RC repair. Preoperative GC stage and Patient-Reported Outcomes Measurement Information System (PROMIS) physical function (PF) and pain interference (PI) scores were collected. The battery of VSs included fat infiltration (FIS), muscle size (MSS), and relative volume contribution (RCS) for each RC muscle. Backward linear regression was performed to compare GC stage with preoperative PROMIS PF and PI to determine which VS measurement most closely correlated with preoperative PROMs. RESULTS: Eighty-two patients underwent RC repair (mean age 55 ± 8.2 years, 63% male, 68% GC stage ≤1). In evaluation of the supraspinatus, there was a moderate positive correlation between GC and FIS (r = 0.459, P < .001); strong negative correlations were observed between MSS (r = -0.800, P < .001) and RCS (r = -0.745, P < .001) when compared to GC. A negligible linear correlation was observed between GC and preoperative PROMIS PF (r = -0.106, P = .343) and PI (r = -0.071, P = .528). On multivariate analysis, subscapularis MSS (ß >0, P = .064) was a positive predictor and subscapularis FIS (ß <0, P = .137), teres minor MSS (ß <0, P = .141), and FIS (ß <0, P = .070) were negative predictors of preoperative PF (r = 0.343, P = .044); in contrast, supraspinatus MSS (ß >0, P = .009) and FIS (ß >0, P = .073), teres minor FIS (ß >0, P = .072), and subscapularis FIS (ß >0, P = .065) were positive predictors of preoperative PI (r = 0.410, P = .006). CONCLUSION: Although the criterion standard in evaluation of RC pathology, GC demonstrated negligible correlation with preoperative functional disability. Alternatively, a battery of 3D VSs showed strong correlation with GC through a quantitative, comprehensive evaluation of the RC unit including several moderate predictors of preoperative functional disability.

Deep-learning reconstructed lumbar spine 3D MRI for surgical planning: pedicle screw placement and geometric measurements compared to CT.

PURPOSE: To test equivalency of deep-learning 3D lumbar spine MRI with "CT-like" contrast to CT for virtual pedicle screw planning and geometric measurements in robotic-navigated spinal surgery. METHODS: Between December 2021 and June 2022, 16 patients referred for spinal fusion and decompression surgery with pre-operative CT and 3D MRI were retrospectively assessed. Pedicle screws were virtually placed on lumbar (L1-L5) and sacral (S1) vertebrae by three spine surgeons, and metrics (lateral deviation, axial/sagittal angles) were collected. Vertebral body length/width (VL/VW) and pedicle height/width (PH/PW) were measured at L1-L5 by three radiologists. Analysis included equivalency testing using the 95% confidence interval (CI), a margin of ± 1 mm (± 2.08° for angles), and intra-class correlation coefficients (ICCs). RESULTS: Across all vertebral levels, both combined and separately, equivalency between CT and MRI was proven for all pedicle screw metrics and geometric measurements, except for VL at L1 (mean difference: - 0.64 mm; [95%CI - 1.05, - 0.24]), L2 (- 0.65 mm; [95%CI - 1.11, - 0.20]), and L4 (- 0.78 mm; [95%CI - 1.11, - 0.46]). Inter- and intra-rater ICC for screw metrics across all vertebral levels combined ranged from 0.68 to 0.91 and 0.89-0.98 for CT, and from 0.62 to 0.92 and 0.81-0.97 for MRI, respectively. Inter- and intra-rater ICC for geometric measurements ranged from 0.60 to 0.95 and 0.84-0.97 for CT, and 0.61-0.95 and 0.93-0.98 for MRI, respectively. CONCLUSION: Deep-learning 3D MRI facilitates equivalent virtual pedicle screw placements and geometric assessments for most lumbar vertebrae, with the exception of vertebral body length at L1, L2, and L4, compared to CT for pre-operative planning in patients considered for robotic-navigated spine surgery.

Brain tumor detection based on a novel and high-quality prediction of the tumor pixel distributions.

The work presented in this paper is in the area of brain tumor detection. We propose a fast detection system with 3D MRI scans of Flair modality. It performs 2 functions, predicting the gray level distribution and location distribution of the pixels in the tumor regions and generating tumor masks with pixel-wise precision. To facilitate 3D data analysis and processing, we introduce a 2D histogram presentation encompassing the gray-level distribution and pixel-location distribution of a 3D object. In the proposed system, specific 2D histograms highlighting tumor-related features are established by exploiting the left-right asymmetry of a brain structure. A modulation function, generated from the input data of each patient case, is applied to the 2D histograms to transform them into coarsely or finely predicted distributions of tumor pixels. The prediction result helps to identify/remove tumor-free slices. The prediction and removal operations are performed to the axial, coronal and sagittal slice series of a brain image, transforming it into a 3D minimum bounding box of its tumor region. The bounding box is utilized to finalize the prediction and generate a 3D tumor mask. The proposed system has been tested extensively with the data of more than 1200 patient cases in BraTS2018∼2021 datasets. The test results demonstrate that the predicted 2D histograms resemble closely the true ones. The system delivers also very good tumor detection results, comparable to those of state-of-the-art CNN systems with mono-modality inputs. They are reproducible and obtained at an extremely low computation cost and without need for training.

A Systematic Review and Identification of the Challenges of Deep Learning Techniques for Undersampled Magnetic Resonance Image Reconstruction.

Deep learning (DL) in magnetic resonance imaging (MRI) shows excellent performance in image reconstruction from undersampled k-space data. Artifact-free and high-quality MRI reconstruction is essential for ensuring accurate diagnosis, supporting clinical decision-making, enhancing patient safety, facilitating efficient workflows, and contributing to the validity of research studies and clinical trials. Recently, deep learning has demonstrated several advantages over conventional MRI reconstruction methods. Conventional methods rely on manual feature engineering to capture complex patterns and are usually computationally demanding due to their iterative nature. Conversely, DL methods use neural networks with hundreds of thousands of parameters and automatically learn relevant features and representations directly from the data. Nevertheless, there are some limitations to DL-based techniques concerning MRI reconstruction tasks, such as the need for large, labeled datasets, the possibility of overfitting, and the complexity of model training. Researchers are striving to develop DL models that are more efficient, adaptable, and capable of providing valuable information for medical practitioners. We provide a comprehensive overview of the current developments and clinical uses by focusing on state-of-the-art DL architectures and tools used in MRI reconstruction. This study has three objectives. Our main objective is to describe how various DL designs have changed over time and talk about cutting-edge tactics, including their advantages and disadvantages. Hence, data pre- and post-processing approaches are assessed using publicly available MRI datasets and source codes. Secondly, this work aims to provide an extensive overview of the ongoing research on transformers and deep convolutional neural networks for rapid MRI reconstruction. Thirdly, we discuss several network training strategies, like supervised, unsupervised, transfer learning, and federated learning for rapid and efficient MRI reconstruction. Consequently, this article provides significant resources for future improvement of MRI data pre-processing and fast image reconstruction.

3D isotropic MRI of ankle: review of literature with comparison to 2D MRI.

The ankle joint has complex anatomy with different tissue structures and is commonly involved in traumatic injuries. Magnetic resonance imaging (MRI) is the primary imaging modality used to assess the soft tissue structures around the ankle joint including the ligaments, tendons, and articular cartilage. Two-dimensional (2D) fast spin echo/turbo spin echo (FSE/TSE) sequences are routinely used for ankle joint imaging. While the 2D sequences provide a good signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) with high spatial resolution, there are some limitations to their use owing to the thick slices, interslice gaps leading to partial volume effects, limited fluid contrast, and the need to acquire separate images in different orthogonal planes. The 3D MR imaging can overcome these limitations and recent advances have led to technical improvements that enable its widespread clinical use in acceptable time periods. The volume imaging renders the advantage of reconstructing into thin continuous slices with isotropic voxels enabling multiplanar reconstructions that helps in visualizing complex anatomy of the structure of interest throughout their course with improved sharpness, definition of anatomic variants, and fluid conspicuity of lesions and injuries. Recent advances have also reduced the acquisition time of the 3D datasets making it more efficient than 2D sequences. This article reviews the recent technical developments in the domain 3D MRI, compares imaging with 3D versus 2D sequences, and demonstrates the use-case scenarios with interesting cases, and benefits of 3D MRI in evaluating various ankle joint components and their lesions.

Reliability of Conventional Two-Dimensional Magnetic Resonance Imaging for Diagnosing Extraforaminal Stenosis in Lumbosacral Transition.

Introduction: Three-dimensional (3D) magnetic resonance imaging (MRI) is reportedly superior to two-dimensional (2D) MRI for diagnosing lumbar foraminal stenosis at L5-S1. In this study, we strictly distinguished the intra- and extraforaminal regions and compared the diagnostic reliability and accuracy of 2D and 3D MRI in each region. Methods: A total of 92 surgical cases of unilateral L5 radiculopathy were selected for imaging analysis, including 46 of foraminal stenosis at L5-S1 (Group F) and 46 of intraspinal canal stenosis at L4-5 (Group C) (48 men, 44 women; mean age, 66 years). The 2D and 3D MRI sets were assessed twice by two examiners. They were informed only of the laterality of the lesion in each case and asked to select among the following for each modality: "absence of foraminal stenosis," "intraforaminal stenosis," "extraforaminal stenosis," and "coincident intraforaminal and extraforaminal stenosis." The intra- and interobserver reliabilities were evaluated using kappa (κ) statistics for the intra- and extraforaminal regions and compared between 2D and 3D MRI. For each case, disagreements between examiners were resolved through discussion to obtain a diagnostic judgment for each modality. Subsequently, the final diagnosis of intra- and/or extraforaminal stenosis in Group F was made using multiple modalities and intraoperative findings. A comparison between 2D and 3D MRI in terms of diagnostic accuracy was performed for the intra- and extraforaminal regions. Results: No significant difference was observed in the κ statistics between 2D and 3D MRI for the intraforaminal region, whereas 3D MRI had significantly larger κ statistic than 2D MRI for the extraforaminal region. Ultimately, 3D MRI perfectly judged the extraforaminal region, whereas 2D MRI detected only 44.8% of the cases of extraforaminal stenosis. Conclusions: More than half of extraforaminal stenosis was overlooked by 2D MRI, suggesting that it is unreliable for diagnosing extraforaminal stenosis at L5-S1.

Anterior cruciate ligament femoral footprint is oblong-ovate, triangular, or two-tears shaped in healthy young adults: three-dimensional MRI analysis.

PURPOSE: This study aimed to evaluate the morphology of the anterior cruciate ligament (ACL) femoral footprint with three-dimensional magnetic resonance imaging (3D MRI) in healthy knees. METHODS: Fifty subjects with healthy knees were recruited, utilising 3D-SPACE sequences for ACL evaluation. The ACL was manually segmented, and the shape, size and location of the ACL femoral footprint were evaluated on a reformatted oblique-sagittal plane, which aligned closely with the ACL attachment. Statistical analysis included one-way ANOVA for continuous variables and Fisher's exact test for categorical variables, with a P value < 0.05 considered significant. RESULTS: Three types of ACL femoral footprint shape were identified, namely, oblong-ovate (OO) in 33 knees (66%), triangular (Tr) in 12 knees (24%) and two-tears (TT) in 5 knees (10%), with the mean areas being 58, 47 and 68 mm2, respectively. Within group TT, regions with similar sizes but different locations were identified: high tear (TT-H) and low tear (TT-L). Notably, group OO demonstrated a larger notch height index, whilst group TT was characterised by a larger α angle and lateral femoral condyle index. A noticeable variation was observed in the location of the femoral footprint centre across groups, with group TT-L and group Tr showing a more distal position relative to the apex of the deep cartilage. According to the Bernard and Hertel (BH) grid, the ACL femoral footprint centres in group TT-L exhibited a shallower and higher position than other groups. Furthermore, compared to group OO and TT-H, group Tr showed a significantly higher position according to the BH grid. CONCLUSION: In this study, the morphology of the ACL femoral footprint in healthy young adults was accurately evaluated using 3D MRI, revealing three distinct shapes: OO, Tr and TT. The different ACL femoral footprint types showed similar areas but markedly different locations. These findings emphasise the necessity of considering both the shape and precise location of the ACL femoral footprint during clinical assessments, which might help surgeons enhance patient-specific surgical plans before ACL reconstruction. LEVEL OF EVIDENCE: IV.