Patient selection
This study was approved by our institutional review board (Ethics Commission of the Medical Faculty, Technical University of Munich, Germany; Ethics proposal number 258/15 S). Informed consent was obtained from all study participants prior to inclusion. Participants were recruited from February 2019 until March 2020. In total, 25 consecutive patients (mean age 52.4 ± 18 years, 13 women) with shoulder pain diagnosed by one board-certified orthopedic surgeon, were included. All patients had undergone conventional radiography of the shoulder including AP and Y views as part of clinical routine and were scheduled for MR imaging of the shoulder for further evaluation of the ligaments and soft tissue structures of the shoulder joint. In five patients additional CT scans of the shoulder were available. Patients with previous surgical interventions were excluded prior to this study.
MR imaging
MR imaging was performed using one 3 Tesla MR scanner (Ingenia; Philips Healthcare, Best, The Netherlands) with a dedicated 8-channel shoulder coil (Medical Advances). Standard shoulder imaging protocols were used including triplanar intermediate weighted MR images with fat suppression, a sagittal T2-weighted sequence and a coronal T1-weighted sequence. Additionally, a 3D T1-weighted spoiled gradient echo sequence was acquired in transverse acquisition plane; in-plane resolution, 0.3 mm × 0.3 mm; TE, 2.586 ms; TR, 10 ms; flip angle, 8°; slice thickness, 0.4 mm; standard field of view (FOV) 150x150x80mm3. The FOV could be changed slightly depending on the patient size leading to an acquisition time between 4 min 25 s to 5 min 03 s.
Post-processing
A novel software tool built in-house based on MATLAB (MathWorks, Natick, Massachusetts) was used to process the T1-weighted spoiled gradient echo images, as previously reported [4]. On the T1-weighted GRE images the surrounding background was segmented to achieve a binary mask. Subsequently, the intensities of the images were inverted. Further, the contrast of the inverted images was enhanced using an adaptive histogram equalization. In a next step, the 15-th power of the images was computed to further increase the contrast of bone. The processed images were multiplied pointwise with the background mask to avoid disturbing artifacts in the forward projection. In order to generate 2D simulated radiographs, the 3D MR images were processed using a cone-beam forward projection resembling a standard cone-beam CT. In a final step, a diagnostically ideal projection (anterior-posterior-(AP) and Y- projections) of the processed images was chosen for further image analysis (Fig. 1 and Fig. 2).
Image analysis
Conventional radiographs were used as standard of reference and read beforehand by two board-certified radiologists (B.J.S and A.S.G., both with 9 years of experience in musculoskeletal radiology). After a period of at least 4 weeks, the MR-derived simulated radiographs were analyzed in random order by the same radiologists, which were blinded to clinical information and any other imaging data.
Semi-quantitative measurements
Overall image quality for each projection (true AP and Y-projection) as well as the visibility of the anatomical landmarks (acromion, glenohumeral joint space, glenoid and acromioclavicular joint) were graded using a four-point Likert scale (1 = excellent visibility/detectability, 2 = good visibility/detectability, 3 = moderate visibility/detectability and 4 = poor visibility/detectability). The overall certainty of imaging features was also graded using a four-point Likert scale (1 = absolute certain, 2 = very certain, 3 = moderate certainty, 4 = not certain due to poor image quality).
For the evaluation of degenerative changes of the glenohumeral joint, the Samilson-Prieto classification was used to grade the stages of osteoarthritis on the basis of the osteophytes sizes located at the inferior humeral head measured in a cranial to caudal direction (Grade I = < 3 mm, mild osteoarthritis, Grade II = 3-6 mm, moderate osteoarthritis, Grad III = ≥7 mm, severe osteoarthritis) [9, 10]. The acromion configuration was assessed according to the Bigliani classification, which categorizes the acromion into three types according to the angle of the lateral notch (type I: flat acromion, type II: curved acromion, type III: hook shaped acromion) [11]. The acromiohumeral distance (AHD) was assessed by measuring the distance from the inferior edge of the acromion to the humeral head in the Y-projection [12, 13]. The critical shoulder angle (CSA; Fig. 3) was measured by drawing a line parallel to the glenoid in an anterior-posterior radiograph and a line through the inferior-lateral edge of the glenoid and the inferior-lateral edge of the acromion.
The angle was measured in order to evaluate the humeral coverage of the acromion and the inclination of the glenoid which has been shown to correlate with the risk for rotator cuff tears or glenohumeral osteoarthritis [14, 15]. The CT-like MR images were evaluated by both readers for calcifications of the rotator cuff, osseous glenoid defects, subcortical cysts and tears of the rotator cuff (Fig. 4, 5, 6, 7 and 8). For intra-rater reproducibility, the MR-derived simulated radiographs of the 23 subjects were reassessed after 4 weeks by one radiologist.
Statistical analysis
The data were analyzed using IBM SPSS Statistics for Windows, version 27.0 (IBM Corp., Armonk, N.Y., USA). All statistical tests were performed two-sided and a level of significance (α) of 0.05 was used for all tests. Paired t-test (for numeric variables), McNemar’s test (for binary categorical variables) and Wilcoxon signed-rank test (for the categorical variables) were used to evaluate differences in gradings between the MR-derived images and the conventional radiographs. Agreement between the MR-derived simulated radiographs and conventional radiographs was calculated using Cohen’s Kappa. The inter-observer and intra-observer reliability of the assessment of the MR-derived simulated radiographs and the assessment of the conventional radiographs were also calculated using Cohen’s Kappa [16]. Agreement was interpreted as poor (0), slight (0.0–0.2), fair (0.21–0.40), moderate (0.41–0.60), substantial (0.61–0.80), and perfect (0.81–1.00) [17].