Can the measured angle ABC on the lateral projection of the knee be used to determine the tube angulation for an optimum skyline projection?

Seah, L.J.Y.; Seow, D.; Mahmood, D.; Chua, Eric Chern Pin; Sng, Li-Hwei

doi:10.1016/j.radi.2021.11.005

Cited by 3 publications

(4 citation statements)

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“…OA lutut memiliki insidensi 34% pada pasien berusia di atas 65 tahun (Sa'id Jamalulail Bin Ali., 2017). Protokol standar pemeriksaan diagnosa OA adalah pemeriksaan radiografi proyeksi antero-posterior (AP) dan proyeksi lateral (Seah et al, 2022). Pemeriksaan khusus proyeksi skyline perlu ditambahkan untuk mendeteksi OA pada sendi patello femoral (PFJ).…”

Section: Pendahuluanunclassified

“…Menurut (Bhattacharya et al, 2007) bahwa proyeksi skyline dapat memvisualisasikan OA pada patella femoral joint dengan nilai sensitifitas 89%, spesifitas 83% dan memprediksi positif OA sebesar 92%. (Seah et al, 2022) menyatakan bahwa proyeksi skyline memberikan informasi berharga tentang morfologi dan anatomi PFJ, memungkinkan lokasi yang tepat dari setiap perubahan degeneratife. Menurut (Martadiani, 2013) proyeksi Skyline untuk mendeteksi subluksasi patella yang ringan atau fraktur osteokondral, stress valgus untuk menilai adanya trauma pada ligamentum kolateral medialis (MCL), dan stress varus untuk menilai adanya trauma pada ligamentum kolateral lateralis (LCL).…”

Section: Pendahuluanunclassified

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Analisis Kriteria Radiografi Os Patella Dan Patelofemoral Joint Dengan Proyeksi Skyline Metode Hungston Dan Settegast

Mahfud

2023

j. teras kesehatan.

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Background: the problem in radiographic examination of Os Patella and Patellofemoral joint is the limitation in showing the picture of Os Patella from the infero-superior aspect which is free from superposition with other surrounding organs and also to show the distance between Os Patella and Condylus Femoralis Joint. Objective: to analyze the criteria for the results of the Os Patella and Patellofemoral joint examination in providing optimal and informative results according to diagnostic needs. Methods: This study used a descriptive qualitative design, with a sample of 10 patients. Research Results: radiographic images of the Hungston method in the prone patient position and knee flexed to form 550 with 450 cranially ray direction can provide informative results in the examination of Os Patella and Patellofemoral Joint because it can provide optimal anatomical image criteria and open Patellofemoral Joint Interspace than the Settegast method. Conclusion: the Hungston method is able to show optimal and informative image results for use in examining the Os Patella and Patellofemoral Joint Skyline projection.

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Section: Pendahuluanunclassified

Analisis Kriteria Radiografi Os Patella Dan Patelofemoral Joint Dengan Proyeksi Skyline Metode Hungston Dan Settegast

Mahfud

2023

j. teras kesehatan.

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“…The image accuracy may be further compromised by soft tissue that obscures bone positioning, potentially leading to BPA errors and resulting in images with double lines and blurred contours of the patella and femur. Consequently, the precision of the skyline view largely depends on the radiographer’s expertise, often leading to inconsistent outcomes due to human error [ 6 ]. Moreover, concerns about radiation exposure and time constraints in high-volume clinical settings become pronounced when repetition of X-rays is necessary to achieve an accurate patellofemoral joint view.…”

Section: Introductionmentioning

confidence: 99%

Minimal impact of beam projection angle deviations in skyline (Laurin) view and the efficacy of the anterior border of proximal tibia as a guiding landmark

Kim,

Hwang,

Lee

et al. 2024

Skeletal Radiol

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Objective Obtaining an optimal knee skyline view is challenging due to inaccuracies in beam projection angles (BPAs) and soft tissue obscuring bony landmarks. This study aimed to assess the impact of BPA deviations on patellofemoral index measurements and assessed the anterior border of the proximal tibia as an anatomic landmark for guiding BPAs. Materials and methods This retrospective study consisted of three parts. The first was a simulation study using 52 CT scans of knees with a 20° flexion contracture to replicate the skyline (Laurin) view. Digitally reconstructed radiographs simulated neutral, 5° downward, and 5° upward tilt BPAs. Five patellofemoral indices (sulcus angle, congruence angle, patellar tilt angle, lateral facet angle, and bisect ratio) were measured and compared. The second part was a proof of concept study on 162 knees to examine patellar indices differences across these BPAs. Lastly, the alignment of the anterior border of the proximal tibia with the BPA tangential to the patellar articular surface was tested from the CT scans. Results No significant differences in patellofemoral indices were found across various BPAs in both the simulation and proof of concept studies (all p > 0.05). The angle between the anterior border of the proximal tibia and the patellar articular surface was 1.5 ± 5.3°, a statistically significant (p = 0.037) yet clinically acceptable deviation. Conclusion Patellofemoral indices in skyline view remained consistent regardless of BPA deviations. The anterior border of the proximal tibia proved to be an effective landmark for accurate beam projection.

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“…In the field of face recognition, some methods recognize areas of high symmetry, such as faces [6,7], and some use facial feature points and estimate face angles based on them. On the other hand, in radiography, Seah et al extracted feature points from a radiograph of the lateral knee joint to estimate tube angles for another positioning [8]. In addition, Wu et al estimated the rotation angle of the chest from asymmetric projection and validated it using a phantom [9].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Based Method for the Estimation of Patient’s Angles from Lateral Skull Radiographs

Nakazeko

Kojima

Watanabe

et al. 2022

Frontiers in Artificial Intelligence and Applications

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Radiography is used for initial diagnosis and postoperative follow-up. If a radiograph is deemed unsuitable for diagnosis, it is rejected. Retaking a radiograph is disadvantageous for the patient because it prolongs the examination time and increases the radiation dose. Skull radiography is the position in which retaking occurs most frequently. In skull radiography, the patient’s rotational direction is estimated from minute changes in the inner ear’s structure in the lateral skull radiograph. When retaking, the amount of correction for patient positioning is generally estimated from the errors in the rejected image through empirical evidence. Therefore, considerable expertise is needed to correct the positioning appropriately, and inexperienced radiologic technologists take considerable time to estimate this error. This study aimed to estimate the patient’s angle from lateral skull radiographs to compensate for radiologic technologists’ lack of experience and reduce the burden on patients. The radiograph is a simulated 2-D line-integral projection of a 3-D CT image, and we developed an estimation method using deep learning with supervised training. The network is based on a re-scaled ResNet. The patient’s angle was estimated in the lateral and superior-inferior directions. We evaluated the accuracy of estimation with the projected images of 256 simulated cases. The estimation errors were 0.48 ± 0.41° and 0.55 ± 0.50° in the lateral and superior-inferior angles, respectively. These findings suggest that a patient’s angle can be accurately estimated from a radiograph using deep learning, compensating for the lack of experience and reducing retaking time.

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Can the measured angle ABC on the lateral projection of the knee be used to determine the tube angulation for an optimum skyline projection?

Cited by 3 publications

References 6 publications

Analisis Kriteria Radiografi Os Patella Dan Patelofemoral Joint Dengan Proyeksi Skyline Metode Hungston Dan Settegast

Analisis Kriteria Radiografi Os Patella Dan Patelofemoral Joint Dengan Proyeksi Skyline Metode Hungston Dan Settegast

Minimal impact of beam projection angle deviations in skyline (Laurin) view and the efficacy of the anterior border of proximal tibia as a guiding landmark

Deep Learning Based Method for the Estimation of Patient’s Angles from Lateral Skull Radiographs

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