Automated anomaly-aware 3D segmentation of bones and cartilages in knee MR images from the Osteoarthritis Initiative.

In medical image analysis, automated segmentation of multi-component anatomical entities, with the possible presence of variable anomalies or pathologies, is a challenging task. In this work, we develop a multi-step approach using U-Net-based models to initially detect anomalies (bone marrow lesions, bone cysts) in the distal femur, proximal tibia and patella from 3D magnetic resonance (MR) images in individuals with varying grades of knee osteoarthritis. Subsequently, the extracted data are used for downstream tasks involving semantic segmentation of individual bone and cartilage volumes as well as bone anomalies.

Analysis of cam location characteristics in FAI syndrome patients from 3D MR images demonstrates sex-specific differences.

Cam femoroacetabular impingement (FAI) syndrome is associated with hip osteoarthritis (OA) development. Hip shape features, derived from statistical shape modeling (SSM), are predictive for OA incidence, progression, and arthroplasty. Currently, no three-dimensional (3D) SSM studies have investigated whether there are cam shape differences between male and female patients, which may be of potential clinical relevance for FAI syndrome assessments.

Automated volumetric and statistical shape assessment of cam-type morphology of the femoral head-neck region from clinical 3D magnetic resonance images.

Background: Femoroacetabular impingement (FAI) cam morphology is routinely assessed using manual measurements of two-dimensional (2D) alpha angles which are prone to high rater variability and do not provide direct three-dimensional (3D) data on these osseous formations. We present CamMorph, a fully automated 3D pipeline for segmentation, statistical shape assessment and measurement of cam volume, surface area and height from clinical magnetic resonance (MR) images of the hip in FAI patients.

Methods: The novel CamMorph pipeline involves two components: (I) accurate proximal femur segmentation generated by combining the 3D U-net to identify both global (region) and local (edge) features in clinical MR images and focused shape modelling to generate a 3D anatomical model for creating patient-specific proximal femur models; (II) patient-specific anatomical information from 3D focused shape modelling to simulate 'healthy' femoral bone models with cam-affected region constraints applied to the anterosuperior femoral head-neck region to quantify cam morphology in FAI patients.

CAN3D: Fast 3D medical image segmentation via compact context aggregation.

Direct automatic segmentation of objects in 3D medical imaging, such as magnetic resonance (MR) imaging, is challenging as it often involves accurately identifying multiple individual structures with complex geometries within a large volume under investigation. Most deep learning approaches address these challenges by enhancing their learning capability through a substantial increase in trainable parameters within their models. An increased model complexity will incur high computational costs and large memory requirements unsuitable for real-time implementation on standard clinical workstations, as clinical imaging systems typically have low-end computer hardware with limited memory and CPU resources only.

Automated 3D Analysis of Clinical Magnetic Resonance Images Demonstrates Significant Reductions in Cam Morphology Following Arthroscopic Intervention in Contrast to Physiotherapy.

Purpose: To obtain automated measurements of cam volume, surface area, and height from baseline (preintervention) and 12-month magnetic resonance (MR) images acquired from male and female patients allocated to physiotherapy (PT) or arthroscopic surgery (AS) management for femoroacetabular impingement (FAI) in the Australian FASHIoN trial.

Methods: An automated segmentation pipeline (CamMorph) was used to obtain cam morphology data from three-dimensional (3D) MR hip examinations in FAI patients classified with mild, moderate, or major cam volumes. Pairwise comparisons between baseline and 12-month cam volume, surface area, and height data were performed within the PT and AS patient groups using paired -tests or Wilcoxon signed-rank tests.

Integrated Multi-Modal Antenna With Coupled Radiating Structures (I-MARS) for 7T pTx Body MRI.

One of the main challenges in ultra-high field whole body MRI relates to the uniformity and efficiency of the radiofrequency field. Although recent advances in the design of RF coils have demonstrated that dipole antennas have a current distribution ideally suited to 7T MRI, they are limited by low isolation and poor robustness to loading changes. Multi-layered and self-decoupled loop coils have demonstrated improved RF performance in these areas at lower field MRI but have not been adapted to dipole designs.

Automated analysis of immediate reliability of T2 and T2* relaxation times of hip joint cartilage from 3 T MR examinations.

Background: Magnetic resonance (MR) T2 and T2* mapping sequences allow in vivo quantification of biochemical characteristics within joint cartilage of relevance to clinical assessment of conditions such as hip osteoarthritis (OA).

Purpose: To evaluate an automated immediate reliability analysis of T2 and T2* mapping from MR examinations of hip joint cartilage using a bone and cartilage segmentation pipeline based around focused shape modelling.

Study Type: Technical validation.

Discrete element and finite element methods provide similar estimations for hip joint contact mechanics during walking gait.

Finite element analysis (FEA) provides a powerful approach for estimating the in-vivo loading characteristics of the hip joint during various locomotory and functional activities. However, time-consuming procedures, such as the generation of high-quality FE meshes and setup of FE simulation, typically make the method impractical for rapid applications which could be used in clinical routine. Alternatively, discrete element analysis (DEA) has been developed to quantify mechanical conditions of the hip joint in a fraction of time compared to FEA.

Simultaneous super-resolution and contrast synthesis of routine clinical magnetic resonance images of the knee for improving automatic segmentation of joint cartilage: data from the Osteoarthritis Initiative.

Purpose: High resolution three-dimensional (3D) magnetic resonance (MR) images are well suited for automated cartilage segmentation in the human knee joint. However, volumetric scans such as 3D Double-Echo Steady-State (DESS) images are not routinely acquired in clinical practice which limits opportunities for reliable cartilage segmentation using (fully) automated algorithms. In this work, a method for generating synthetic 3D MR (syn3D-DESS) images with better contrast and higher spatial resolution from routine, low resolution, two-dimensional (2D) Turbo-Spin Echo (TSE) clinical knee scans is proposed.

High-grade intramuscular tendon disruption in acute hamstring injury and return to play in Australian Football players.

Background: Recent literature has reported intramuscular tendon (IT) disruption is associated with longer return to play (RTP) following acute hamstring injury.

Objectives: Investigate whether an increase in hamstring injury severity involving high-grade IT disruption and proximal injury location is associated with longer RTP times in elite Australian Rules Football (AFL) players.

Methods: Hamstring injury records and RTP times from one professional AFL club were obtained over six seasons.

Quantitative mapping of acute and chronic PCL pathology with 3 T MRI: a prospectively enrolled patient cohort.

Background: The diagnosis of incomplete acute and chronic posterior cruciate ligament (PCL) tears can be challenging with conventional magnetic resonance (MR) imaging, particularly for injuries in which the ligament appears continuous as occurs with chronic PCL tears that have scarred in continuity. Quantitative mapping from MR imaging may provide additional useful diagnostic information in these cases. The purpose of this study was to assess the feasibility of quantifying transverse relaxation time (T2) mapping values at 3 Tesla (T) in a prospectively enrolled patient cohort with chronic PCL tears.

A lightweight rapid application development framework for biomedical image analysis.

Biomedical imaging analysis typically comprises a variety of complex tasks requiring sophisticated algorithms and visualising high dimensional data. The successful integration and deployment of the enabling software to clinical (research) partners, for rigorous evaluation and testing, is a crucial step to facilitate adoption of research innovations within medical settings. In this paper, we introduce the Simple Medical Imaging Library Interface (SMILI), an object oriented open-source framework with a compact suite of objects geared for rapid biomedical imaging (cross-platform) application development and deployment.

An open 8-channel parallel transmission coil for static and dynamic 7T MRI of the knee and ankle joints at multiple postures.

Purpose: We present the initial in vivo imaging results of an open architecture eight-channel parallel transmission (pTx) transceive radiofrequency (RF) coil array that was designed and constructed for static and dynamic 7T MRI of the knee and ankle joints.

Methods: The pTx coil has a U-shaped dual-row configuration (200 mm overall length longitudinally) that allows static and dynamic imaging of the knee and ankle joints at various postures and during active movements. This coil structure, in combination with B shimming, allows flexible configuration of B transmit profiles, with good homogeneity over 120-mm regions of interest.

Automated T2-mapping of the Menisci From Magnetic Resonance Images in Patients with Acute Knee Injury.

Rationale And Objectives: This study aimed to evaluate the accuracy of an automated method for segmentation and T2 mapping of the medial meniscus (MM) and lateral meniscus (LM) in clinical magnetic resonance images from patients with acute knee injury.

Materials And Methods: Eighty patients scheduled for surgery of an anterior cruciate ligament or meniscal injury underwent magnetic resonance imaging of the knee (multiplanar two-dimensional [2D] turbo spin echo [TSE] or three-dimensional [3D]-TSE examinations, T2 mapping). Each meniscus was automatically segmented from the 2D-TSE (composite volume) or 3D-TSE images, auto-partitioned into anterior, mid, and posterior regions, and co-registered onto the T2 maps.

Automated 3D quantitative assessment and measurement of alpha angles from the femoral head-neck junction using MR imaging.

To develop an automated approach for 3D quantitative assessment and measurement of alpha angles from the femoral head-neck (FHN) junction using bone models derived from magnetic resonance (MR) images of the hip joint.Bilateral MR images of the hip joints were acquired from 30 male volunteers (healthy active individuals and high-performance athletes, aged 18–49 years) using a water-excited 3D dual echo steady state (DESS) sequence. In a subset of these subjects (18 water-polo players), additional True Fast Imaging with Steady-state Precession (TrueFISP) images were acquired from the right hip joint.

Statistical shape model reconstruction with sparse anomalous deformations: Application to intervertebral disc herniation.

Many medical image processing techniques rely on accurate shape modeling of anatomical features. The presence of shape abnormalities challenges traditional processing algorithms based on strong morphological priors. In this work, a sparse shape reconstruction from a statistical shape model is presented.

Automated analysis of hip joint cartilage combining MR T2 and three-dimensional fast-spin-echo images.

Purpose: To validate a fully automated scheme to extract biochemical information from the hip joint cartilages using MR T2 mapping images incorporating segmentation of co-registered three-dimensional Fast-Spin-Echo (3D-SPACE) images.

Methods: Manual analyses of unilateral hip (3 Tesla) MR images of 24 asymptomatic volunteers were used to validate a 3D deformable model method for automated cartilage segmentation of SPACE scans, partitioning of the individual femoral and acetabular cartilage plates into clinically defined sub-regions and propagating these results to T2 maps to calculate region-wise T2 value statistics. Analyses were completed on a desktop computer (∼ 10 min per case).

Automatic bone segmentation and bone-cartilage interface extraction for the shoulder joint from magnetic resonance images.

We present a statistical shape model approach for automated segmentation of the proximal humerus and scapula with subsequent bone-cartilage interface (BCI) extraction from 3D magnetic resonance (MR) images of the shoulder region. Manual and automated bone segmentations from shoulder MR examinations from 25 healthy subjects acquired using steady-state free precession sequences were compared with the Dice similarity coefficient (DSC). The mean DSC scores between the manual and automated segmentations of the humerus and scapula bone volumes surrounding the BCI region were 0.

Automatic hip cartilage segmentation from 3D MR images using arc-weighted graph searching.

Accurate segmentation of hip joint cartilage from magnetic resonance (MR) images offers opportunities for quantitative investigations of pathoanatomical conditions such as osteoarthritis. In this paper, we present a fully automatic scheme for the segmentation of the individual femoral and acetabular cartilage plates in the human hip joint from high-resolution 3D MR images. The developed scheme uses an improved optimal multi-object multi-surface graph search framework with an arc-weighted graph representation that incorporates prior morphological knowledge as a basis for segmentation of the individual femoral and acetabular cartilage plates despite weak or incomplete boundary interfaces.

Validity and reliability of computerized measurement of lumbar intervertebral disc height and volume from magnetic resonance images.

Background Context: Magnetic resonance (MR) examinations of morphologic characteristics of intervertebral discs (IVDs) have been used extensively for biomechanical studies and clinical investigations of the lumbar spine. Traditionally, the morphologic measurements have been performed using time- and expertise-intensive manual segmentation techniques not well suited for analyses of large-scale studies..

Focused shape models for hip joint segmentation in 3D magnetic resonance images.

Deformable models incorporating shape priors have proved to be a successful approach in segmenting anatomical regions and specific structures in medical images. This paper introduces weighted shape priors for deformable models in the context of 3D magnetic resonance (MR) image segmentation of the bony elements of the human hip joint. The fully automated approach allows the focusing of the shape model energy to a priori selected anatomical structures or regions of clinical interest by preferentially ordering the shape representation (or eigen-modes) within this type of model to the highly weighted areas.

Automated bone segmentation from large field of view 3D MR images of the hip joint.

Accurate bone segmentation in the hip joint region from magnetic resonance (MR) images can provide quantitative data for examining pathoanatomical conditions such as femoroacetabular impingement through to varying stages of osteoarthritis to monitor bone and associated cartilage morphometry. We evaluate two state-of-the-art methods (multi-atlas and active shape model (ASM) approaches) on bilateral MR images for automatic 3D bone segmentation in the hip region (proximal femur and innominate bone). Bilateral MR images of the hip joints were acquired at 3T from 30 volunteers.