Automating the multimodal analysis of musculoskeletal imaging in the presence of hip implants

In patients treated with hip arthroplasty, the muscular condition and presence of inflammatory reactions are assessed using magnetic resonance imaging (MRI). As MRI lacks contrast for bony structures, computed tomography (CT) is preferred for clinical evaluation of bone tissue and orthopaedic surgical planning. Combining the complementary information of MRI and CT could improve current clinical practice for diagnosis, monitoring and treatment planning. In particular, the different contrast of these modalities could help better quantify the presence of fatty infiltration to characterise muscular condition after hip replacement. In this thesis, I developed automated processing tools for the joint analysis of CT and MR images of patients with hip implants. In order to combine the multimodal information, a novel nonlinear registration algorithm was introduced, which imposes rigidity constraints on bony structures to ensure realistic deformation. I implemented and thoroughly validated a fully automated framework for the multimodal segmentation of healthy and pathological musculoskeletal structures, as well as implants. This framework combines the proposed registration algorithm with tailored image quality enhancement techniques and a multi-atlas-based segmentation approach, providing robustness against the large population anatomical variability and the presence of noise and artefacts in the images. The automation of muscle segmentation enabled the derivation of a measure of fatty infiltration, the Intramuscular Fat Fraction, useful to characterise the presence of muscle atrophy. The proposed imaging biomarker was shown to strongly correlate with the atrophy radiological score currently used in clinical practice. Finally, a preliminary work on multimodal metal artefact reduction, using an unsupervised deep learning strategy, showed promise for improving the postprocessing of CT and MR images heavily corrupted by metal artefact. This work represents a step forward towards the automation of image analysis in hip arthroplasty, supporting and quantitatively informing the decision-making process about patient’s management

Automated measurement of fat infiltration in the hip abductors from Dixon magnetic resonance imaging

PURPOSE: Intramuscular fat infiltration is a dynamic process, in response to exercise and muscle health, which can be quantified by estimating fat fraction (FF) from Dixon MRI. Healthy hip abductor muscles are a good indicator of a healthy hip and an active lifestyle as they have a fundamental role in walking. The automated measurement of the abductors' FF requires the challenging task of segmenting them. We aimed to design, develop and evaluate a multi-atlas based method for automated measurement of fat fraction in the main hip abductor muscles: gluteus maximus (GMAX), gluteus medius (GMED), gluteus minimus (GMIN) and tensor fasciae latae (TFL). METHOD: We collected and manually segmented Dixon MR images of 10 healthy individuals and 7 patients who underwent MRI for hip problems. Twelve of them were selected to build an atlas library used to implement the automated multi-atlas segmentation method. We compared the FF in the hip abductor muscles for the automated and manual segmentations for both healthy and patients groups. Measures of average and spread were reported for FF for both methods. We used the root mean square error (RMSE) to quantify the method accuracy. A linear regression model was used to explain the relationship between FF for automated and manual segmentations. RESULTS: The automated median (IQR) FF was 20.0(16.0-26.4) %, 14.3(10.9-16.5) %, 15.5(13.9-18.6) % and 16.2(13.5-25.6) % for GMAX, GMED, GMIN and TFL respectively, with a FF RMSE of 1.6%, 0.8%, 2.1%, 2.7%. A strong linear correlation (R2 = 0.93, p < .001, m = 0.99) was found between the FF from automated and manual segmentations. The mean FF was higher in patients than in healthy subjects. CONCLUSION: The automated measurement of FF of hip abductor muscles from Dixon MRI had good agreement with FF measurements from manually segmented images. The method was accurate for both healthy and patients groups

Reference values for volume, fat content and shape of the hip abductor muscles in healthy individuals from Dixon MRI

Healthy hip abductor muscles are a good indicator of a healthy hip and an active lifestyle, as they are greatly involved in human daily activities. Fatty infiltration and muscle atrophy are associated with loss of strength, loss of mobility and hip disease. However, these variables have not been widely studied in this muscle group. We aimed to characterize the hip abductor muscles in a group of healthy individuals to establish reference values for volume, intramuscular fat content and shape of this muscle group. To achieve this, we executed a cross-sectional study using Dixon MRI scans of 51 healthy subjects. We used an automated segmentation method to label GMAX, GMED, GMIN and TFL muscles, measured normalized volume (NV) using lean body mass, fat fraction (FF) and lean muscle volume for each subject and computed non-parametric statistics for each variable grouped by sex and age. We measured these variables for each axial slice and created cross-sectional area and FF axial profiles for each muscle. Finally, we generated sex-specific atlases with FF statistical images. We measured median (IQR) NV values of 12.6 (10.8-13.8), 6.3 (5.6-6.7), 1.6 (1.4-1.7) and 0.8 (0.6-1.0) cm3/kg for GMAX, GMED, GMIN and TFL, and median (IQR) FF values of 12.3 (10.1-15.9)%, 9.8 (8.6-11.2)%, 10.0 (9.0-12.0)% and 10.2 (7.8-13.5)% respectively. FF values were significantly higher for females for the four muscles (p < 0.01), but there were no significant differences between the two age groups. When comparing individual muscles, we observed a significantly higher FF in GMAX than in the other muscles. The reported novel reference values and axial profiles for volume and FF of the hip abductors, together with male and female atlases, are tools that could potentially help to quantify and detect early the deteriorating effects of hip disease or sarcopenia

Patient-Specific Implants in Musculoskeletal (Orthopedic) Surgery

Most of the treatments in medicine are patient specific, aren’t they? So why should we bother with individualizing implants if we adapt our therapy to patients anyway? Looking at the neighboring field of oncologic treatment, you would not question the fact that individualization of tumor therapy with personalized antibodies has led to the thriving of this field in terms of success in patient survival and positive responses to alternatives for conventional treatments. Regarding the latest cutting-edge developments in orthopedic surgery and biotechnology, including new imaging techniques and 3D-printing of bone substitutes as well as implants, we do have an armamentarium available to stimulate the race for innovation in medicine. This Special Issue of Journal of Personalized Medicine will gather all relevant new and developed techniques already in clinical practice. Examples include the developments in revision arthroplasty and tumor (pelvic replacement) surgery to recreate individual defects, individualized implants for primary arthroplasty to establish physiological joint kinematics, and personalized implants in fracture treatment, to name but a few

The Effect of Robotic Technology on Perioperative Outcomes in Total Knee Arthroplasty

Introduction Robotic technology has recently regained momentum in total knee arthroplasty (TKA) but the effects of this technology on accuracy of implant positioning, intraoperative soft tissue injury and postoperative functional rehabilitation remain unknown. The objectives of this research thesis were to compare a comprehensive range of radiological objectives and perioperative outcomes in conventional jig-based TKA versus robotic-arm assisted TKA, and use optical motion capture technology to quantify the effects of anterior cruciate ligament (ACL) and posterior cruciate ligament (PCL) resection on knee biomechanics. Methods A series of prospective cohort studies were undertaken in patients with established knee osteoarthritis undergoing primary conventional jig-based TKA versus robotic-arm assisted TKA. Predefined radiological and perioperative study outcomes were recorded by independent observers. Optical motion capture technology during robotic TKA was used to quantify the effects of ACL and PCL resection on knee biomechanics. Results Robotic-arm assisted TKA was associated with improved accuracy of implant positioning, reduced periarticular soft tissue injury, decreased bone trauma, improved postoperative functional rehabilitation, and reduced early systemic inflammatory response compared to conventional jig-based TKA. The Macroscopic Soft Tissue Injury (MASTI) classification system was developed and validated for grading intraoperative periarticular soft tissue injury and bone trauma during TKA. ACL resection created flexion-extension mismatch by increasing the extension gap more than the flexion gap, whilst PCL resection increased the flexion gap proportionally more than the extension gap and created mediolateral laxity in knee flexion but not in extension. Conclusion Robotic-arm assisted TKA was associated with increased accuracy of implant positioning, reduced iatrogenic soft tissue injury, and improved functional rehabilitation compared to conventional jig-based TKA. ACL and PCL resections created unique changes in knee biomechanics that affected flexion-extension gaps and mediolateral soft tissue tension during TKA. On the basis of this thesis, further clinical trials have been established to determine the long-term clinical significance of these findings

Contribution to the clinical validation of a generic method for the classification of osteoarthritic and non-pathological knee function

The Cardiff Dempster-Shafer (DS) classifier is a generic automated technique for analysing motion analysis (MA) data. It can accurately discriminate between level gait characteristics of non-pathological (NP) and osteoarthritic (OA) knee function. It can also quantify and visualise the functional outcome of a total knee replacement (TKR). A number of studies were undertaken to explore and enhance this method. The training set for the classifier was increased by 48% by collecting additional knee function data for level gait. Knee function for nine new patients was classified pre and post-TKR surgery. At 12 months post-TKR, two patients exhibited non-dominant NP knee function. The remaining patients did not recover NP gait. This finding is similar to previous classifications of level gait. To improve the distinction between varying degrees of knee function, stair gait was introduced into the trial. A staircase was designed and validated. Adduction and flexion moments acting about the knee joint and medial component of the ground reaction force were found to be important in the classification of OA and NP knee function from stair gait. Using a combination of these variables the DS classifier was able to characterise OA and NP function for 15 subjects correctly with 100% accuracy, determined using a leave-one-out method of cross validation. The variables were tested to assess the outcome of TKR surgery. The patient assessed recovered NP stair gait post surgery. An image based study was undertaken to investigate the quality of the MA data used in the DS classifier. A step up/down activity for 5 NP and 5 TKR subjects was recorded using non-simultaneous MA and dynamic fluoroscopy. Accurate knee kinematics were computed from the fluoroscopy images using KneeTrack image registration software. MA measured significantly larger knee joint translations and non-sagittal plane rotations. The largest errors in MA derived kinematics were 9.53 for adduction-abduction range of motion (ROM) measured from the NP cohort and 2.63cm compression-distraction ROM of the tibio-femoral joint, measured from the TKR cohort. The generic nature of the DS classifier was tested by its application to distinguish hip function following a lateral (LA) and posterior (PA) approach to total hip arthroplasty. The use of different variables was investigated with the classifier. The best classifier was able to distinguish between NP and LA function with 96.7% accuracy, LA and NP with 86.2% accuracy and between LA and PA with 81.5% accuracy. The PA approach was found to lead to more characteristic NP hip function than LA. These studies show that variables from stair gait should be included in addition to level gait in the classifier. Due to errors when measuring non-sagittal plane rotations using MA, these should be interpreted with caution. The generic nature of the classifier has been proven by its application to another joint, thus answering another orthopaedic question.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Contribution to the clinical validation of a generic method for the classification of osteoarthritic and non-pathological knee function

The Cardiff Dempster-Shafer (DS) classifier is a generic automated technique for analysing motion analysis (MA) data. It can accurately discriminate between level gait characteristics of non-pathological (NP) and osteoarthritic (OA) knee function. It can also quantify and visualise the functional outcome of a total knee replacement (TKR). A number of studies were undertaken to explore and enhance this method. The training set for the classifier was increased by 48% by collecting additional knee function data for level gait. Knee function for nine new patients was classified pre and post-TKR surgery. At 12 months post-TKR, two patients exhibited non-dominant NP knee function. The remaining patients did not recover NP gait. This finding is similar to previous classifications of level gait. To improve the distinction between varying degrees of knee function, stair gait was introduced into the trial. A staircase was designed and validated. Adduction and flexion moments acting about the knee joint and medial component of the ground reaction force were found to be important in the classification of OA and NP knee function from stair gait. Using a combination of these variables the DS classifier was able to characterise OA and NP function for 15 subjects correctly with 100% accuracy, determined using a leave-one-out method of cross validation. The variables were tested to assess the outcome of TKR surgery. The patient assessed recovered NP stair gait post surgery. An image based study was undertaken to investigate the quality of the MA data used in the DS classifier. A step up/down activity for 5 NP and 5 TKR subjects was recorded using non-simultaneous MA and dynamic fluoroscopy. Accurate knee kinematics were computed from the fluoroscopy images using KneeTrack image registration software. MA measured significantly larger knee joint translations and non-sagittal plane rotations. The largest errors in MA derived kinematics were 9.53 for adduction-abduction range of motion (ROM) measured from the NP cohort and 2.63cm compression-distraction ROM of the tibio-femoral joint, measured from the TKR cohort. The generic nature of the DS classifier was tested by its application to distinguish hip function following a lateral (LA) and posterior (PA) approach to total hip arthroplasty. The use of different variables was investigated with the classifier. The best classifier was able to distinguish between NP and LA function with 96.7% accuracy, LA and NP with 86.2% accuracy and between LA and PA with 81.5% accuracy. The PA approach was found to lead to more characteristic NP hip function than LA. These studies show that variables from stair gait should be included in addition to level gait in the classifier. Due to errors when measuring non-sagittal plane rotations using MA, these should be interpreted with caution. The generic nature of the classifier has been proven by its application to another joint, thus answering another orthopaedic question

Brain Tumour Classification Using Deep Features from Structural MRI

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