Current peri-operative imaging concepts surrounding shoulder arthroplasty

Dekker, Travis J.; Steele, John R.; Vinson, Emily N.; Garrigues, Grant E.

doi:10.1007/s00256-019-03183-3

Cited by 13 publications

(23 citation statements)

References 55 publications

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“…Second, it has been demonstrated that both MRI and CT are equally effective in assessing supraspinatus atrophy 51 and in the measurement rotator cuff cross-sectional area.Third, CT imaging allowed us to segment scapular bones automatically using a validated 3D software (BluePrint, v2.1.6; Tornier, France) allowing us to calculate scapular volume in cm 3 . Finally, CT is widely available, fast, and is used routinely in preoperative planning of shoulder arthroplasty, 52 and the addition of soft tissue algorithms to the bone algorithms is simple, quick, and cheap.…”

Section: Discussionmentioning

confidence: 99%

CT-based volumetric assessment of rotator cuff muscle in shoulder arthroplasty preoperative planning

Werthel

Casson²,

Burdin

et al. 2021

Bone & Joint Open

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Aims The aim of this study was to describe a quantitative 3D CT method to measure rotator cuff muscle volume, atrophy, and balance in healthy controls and in three pathological shoulder cohorts. Methods In all, 102 CT scans were included in the analysis: 46 healthy, 21 cuff tear arthropathy (CTA), 18 irreparable rotator cuff tear (IRCT), and 17 primary osteoarthritis (OA). The four rotator cuff muscles were manually segmented and their volume, including intramuscular fat, was calculated. The normalized volume (NV) of each muscle was calculated by dividing muscle volume to the patient’s scapular bone volume. Muscle volume and percentage of muscle atrophy were compared between muscles and between cohorts. Results Rotator cuff muscle volume was significantly decreased in patients with OA, CTA, and IRCT compared to healthy patients (p < 0.0001). Atrophy was comparable for all muscles between CTA, IRCT, and OA patients, except for the supraspinatus, which was significantly more atrophied in CTA and IRCT (p = 0.002). In healthy shoulders, the anterior cuff represented 45% of the entire cuff, while the posterior cuff represented 40%. A similar partition between anterior and posterior cuff was also found in both CTA and IRCT patients. However, in OA patients, the relative volume of the anterior (42%) and posterior cuff (45%) were similar. Conclusion This study shows that rotator cuff muscle volume is significantly decreased in patients with OA, CTA, or IRCT compared to healthy patients, but that only minimal differences can be observed between the different pathological groups. This suggests that the influence of rotator cuff muscle volume and atrophy (including intramuscular fat) as an independent factor of outcome may be overestimated. Cite this article: Bone Jt Open 2021;2(7):552–561.

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Section: Discussionmentioning

confidence: 99%

CT-based volumetric assessment of rotator cuff muscle in shoulder arthroplasty preoperative planning

Werthel

Casson²,

Burdin

et al. 2021

Bone & Joint Open

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“…CT offers the further advantage of identifying nondisplaced acromial and scapular spine fractures, facilitating prompt treatment that may prevent subsequent fracture displacement [13]. For these reasons, it is recommended that patients with postoperative pain over the acromion or scapula without radiographic signs of fracture undergo CT imaging [24,25]. Single photon emission computed tomography (SPECT) has been cited as a useful adjunct for identifying acromial stress reactions in the post-RSA patient with clinical symptoms who lack radiographic evidence of fracture [13].…”

Section: Diagnosismentioning

confidence: 99%

Periprosthetic Fractures in Reverse Total Shoulder Arthroplasty: Current Concepts and Advances in Management

Brusalis

Taylor

2020

Curr Rev Musculoskelet Med

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Purpose of Review Expanded indications for reverse total shoulder arthroplasty (RSA) have raised awareness of associated complications, including periprosthetic fractures. The purpose of this article was to provide a comprehensive update on how, when, and why RSA-related periprosthetic fractures occur, as well as to describe the current treatment strategies. Recent Findings Periprosthetic acromial and scapular spine fractures occur in up to 4.3% of cases and periprosthetic humeral fractures occur in approximately 3.5% of RSA procedures. Fractures of the coracoid process and clavicle have also been reported. Current literature has identified several risk factors for intraoperative or postoperative fracture, including underlying osteoporosis, revision arthroplasty, use of a superiorly placed screw during metaglene fixation, and disruption of the scapular ring by transection of the coracoacromial ligament. Summary Periprosthetic fracture associated with RSA is a clinically significant event that warrants prolonged postoperative vigilance, timely diagnosis, and shared patient decision-making regarding treatment. Further research is needed to identify optimal treatment strategies and characterize long-term clinical outcomes following RSA-related periprosthetic fracture.

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“…Although magnetic resonance imaging (MRI) offers higher contrast resolution for the evaluation of soft tissues, computed tomography (CT) still allows for the detailed quantitative analysis of muscles, distinguishing between muscle, fat, and bone tissues using specific Hounsfield unit (HU) thresholds [6][7][8]. Furthermore, CT is widely available, fast, and well accepted by patients, and this examination is increasingly being used in the imaging evaluation of glenohumeral osteoarthritis and preoperative planning of shoulder arthroplasty [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Deep learning for the rapid automatic quantification and characterization of rotator cuff muscle degeneration from shoulder CT datasets

et al. 2020

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Objectives This study aimed at developing a convolutional neural network (CNN) able to automatically quantify and characterize the level of degeneration of rotator cuff (RC) muscles from shoulder CT images including muscle atrophy and fatty infiltration. Methods One hundred three shoulder CT scans from 95 patients with primary glenohumeral osteoarthritis undergoing anatomical total shoulder arthroplasty were retrospectively retrieved. Three independent radiologists manually segmented the premorbid boundaries of all four RC muscles on standardized sagittal-oblique CT sections. This premorbid muscle segmentation was further automatically predicted using a CNN. Automatically predicted premorbid segmentations were then used to quantify the ratio of muscle atrophy, fatty infiltration, secondary bone formation, and overall muscle degeneration. These muscle parameters were compared with measures obtained manually by human raters. Results Average Dice similarity coefficients for muscle segmentations obtained automatically with the CNN (88% ± 9%) and manually by human raters (89% ± 6%) were comparable. No significant differences were observed for the subscapularis, supraspinatus, and teres minor muscles (p > 0.120), whereas Dice coefficients of the automatic segmentation were significantly higher for the infraspinatus (p < 0.012). The automatic approach was able to provide good-very good estimates of muscle atrophy (R 2 = 0.87), fatty infiltration (R 2 = 0.91), and overall muscle degeneration (R 2 = 0.91). However, CNN-derived segmentations showed a higher variability in quantifying secondary bone formation (R 2 = 0.61) than human raters (R 2 = 0.87). Conclusions Deep learning provides a rapid and reliable automatic quantification of RC muscle atrophy, fatty infiltration, and overall muscle degeneration directly from preoperative shoulder CT scans of osteoarthritic patients, with an accuracy comparable with that of human raters. Key Points • Deep learning can not only segment RC muscles currently available in CT images but also learn their pre-existing locations and shapes from invariant anatomical structures visible on CT sections. • Our automatic method is able to provide a rapid and reliable quantification of RC muscle atrophy and fatty infiltration from conventional shoulder CT scans. • The accuracy of our automatic quantitative technique is comparable with that of human raters.

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Current peri-operative imaging concepts surrounding shoulder arthroplasty

Cited by 13 publications

References 55 publications

CT-based volumetric assessment of rotator cuff muscle in shoulder arthroplasty preoperative planning

CT-based volumetric assessment of rotator cuff muscle in shoulder arthroplasty preoperative planning

Periprosthetic Fractures in Reverse Total Shoulder Arthroplasty: Current Concepts and Advances in Management

Deep learning for the rapid automatic quantification and characterization of rotator cuff muscle degeneration from shoulder CT datasets

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