3D-MRI combined with signal-to-noise ratio measurement can improve the diagnostic accuracy and sensitivity in evaluating meniscal healing status after meniscal repair

Song, Bin; Tan, Wei; Xu, Yongteng; Yu, Taihui; Li, Weiping; Zhong, Chen

doi:10.1007/s00167-018-5018-z

Cited by 9 publications

(16 citation statements)

References 44 publications

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“…A single 3T 3-dimensional (3D) Spectra MRI unit (Siemens Healthineers) was used, which provided improved information, spatial resolution, and diagnostic accuracy compared with 2-dimensional (2D) MRI for assessing the menisci and surrounding anatomy. 29,30,40 The following sequences were performed for each patient in 3 planes: 2D-T2 turbo spin-echo fat-saturated (FS); diffusion-prepared (DP) 3D-T2 space FS; and 3D-T1 space sagittal (Table 1). For each of the 2D sequences, 30 slices with a 3-mm slice thickness and a 0.3-mm interval between each slice were performed.…”

Section: Mri Evaluationmentioning

confidence: 99%

MRI Criteria for Healing at 1 Year After Repair of a Traumatic Meniscal Tear

Schwach,

Grange,

Klasan

et al. 2023

Am J Sports Med

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Background: Meniscal repair for a traumatic meniscal tear is increasingly used to preserve the meniscus. Interpreting postoperative magnetic resonance imaging (MRI) scans remains challenging, especially in symptomatic patients. There is a lack of reliable MRI criteria to affirm the healed character of a traumatic meniscal injury repair. Purpose: To identify relevant MRI criteria for meniscal healing after meniscal repair. Study Design: Case series; Level of evidence, 4. Methods: We prospectively included all patients with a traumatic meniscal injury who underwent either an isolated meniscal repair or a repair during a concomitant anterior cruciate ligament reconstruction. A standardized preoperative and postoperative clinical evaluation was performed, along with collection of functional scores—Knee injury and Osteoarthritis Outcome Score, International Knee Documentation Committee, Lysholm Score, and 36-Item Short Form Health Survey. An MRI scan was performed 1 year postoperatively and compared with the preoperative MRI scan. The following MRI aspects were analyzed: variation of morphology and fat-saturated (FS) T2 intensity signal and pre- and postoperative tear diastasis measurement. Results: Fifty patients (age, mean ± SD, 28.7 ± 8.5 years [range, 16-45 years]) who were 1 year postoperative were included. All patients were considered clinically healed had the same MRI characteristics. A signal change (FS T2) was observed from a high signal intensity fluid to a nonfluid moderate signal intensity. The morphology of the lesion was more complex: from the initial lesion, line ramifications appeared, creating the appearance of tree branches. The tear diastasis decreased (from 2.3 ± 0.5 mm [range, 1.3-3.5] to 1.1 ± 0.28 mm [range, 0.5-1.5]). Conclusion: MRI criteria confirming meniscal healing after traumatic meniscal repair at 1 year were identified: a change in the intrameniscal signal becoming nonfluid and moderate in intensity; a reduction in tear diastasis to <1.5 mm; and a change in the signal morphology of the repaired meniscus.

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Section: Mri Evaluationmentioning

confidence: 99%

MRI Criteria for Healing at 1 Year After Repair of a Traumatic Meniscal Tear

Schwach,

Grange,

Klasan

et al. 2023

Am J Sports Med

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“…Such a failure would typically lead to reduced signal to noise and substandard image quality 1 . Consequently, the diagnostic accuracy and sensitivity can be reduced, potentially leading to missed, delayed or incorrect diagnoses 4–7 . Therefore, it is vital that machine faults are discovered early, allowing for corrective action to be taken.…”

Section: Introductionmentioning

confidence: 99%

Application of synthetic data in the training of artificial intelligence for automated quality assurance in magnetic resonance imaging

2023

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BackgroundMagnetic resonance imaging scanner faults can be missed during routine quality assurance (QA) if they are subtle, intermittent, or the test being performed is insensitive to the type of fault. Coil element malfunction is a common fault within MRI scanners, which may go undetected for quite some time. Consequently, this may lead to poor image quality and the potential for misdiagnoses.PurposeDaily QA typically consists of an automated signal to noise ratio test and in some instances this test is insensitive to coil element malfunction. Instead of relying on daily QA testing, it was proposed to utilize patient images in conjunction with a trained neural network to detect coil element malfunction, even when it presents as a very subtle defect. The advantage to using patient images over phantom testing is real‐time monitoring can be achieved. This allows clinical staff to focus more on patient throughput without being burdened by daily testing.MethodsA neural network was trained using simulated coil failure in 3958 abdominal or pelvic images from 497 patients. The accuracy of the trained network was then tested on an unseen dataset of 109 images from which 44 patients which had coil element malfunction present. Five MRI radiographers were shown 249 images with and without real coil malfunction to assess their accuracy compared to the neural network in identifying the scanner fault.ResultsThe neural network achieved an accuracy of 91.74% in identifying coil element malfunction in the unseen data. Radiographers tasked with identifying coil element malfunction had an average accuracy of 59.99%. In the same test case, the neural network outperformed all radiographers with an accuracy of 91.56%.ConclusionThis work demonstrates that neural networks trained with artificial data can successfully identify MRI scanner coil element malfunction in clinical images. The method provided better accuracy than MRI radiographers (technologists) at identifying coil element malfunction and highlights the potential utility of AI methods as an alternative to support traditional QA. Further, our methodology of training neural networks with simulated data could potentially identify other faults, allowing centers to produce robust fault detection systems with minimal data.

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“…However, MRI is an expensive and timeconsuming procedure which requires bulky equipment, laboratory settings, and skilled personnel. In addition, the accuracy of MRI in postoperative assessment of the joint during rehabilitation is still limited for the orthopedic community, and in many cases may not be feasible [6,7]. Often, clinicians rely upon the self-reported symptoms, history of mechanical symptoms, and physical examination to identify the patients with symptoms that can be attributed to meniscal tear, which involves a high level of uncertainty [8].…”

Section: Introductionmentioning

confidence: 99%

Vibration Stimulation as a Non-Invasive Approach to Monitor the Severity of Meniscus Tears

Safaei

Bolus

Whittingslow

et al. 2021

IEEE Trans. Neural Syst. Rehabil. Eng.

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 Musculoskeletal disorders and injuries are one of the most prevalent medical conditions across age groups. Due to a high load-bearing function, the knee is particularly susceptible to injuries such as meniscus tears. Imaging techniques are commonly used to assess meniscus injuries, though this approach suffers from limitations including high cost, need for skilled personnel, and confinement to laboratory or clinical settings. Vibration-based structural monitoring methods in the form of acoustic emission analysis and vibration stimulation have the potential to address the limits associated with current diagnostic technologies. In this study, an active vibration measurement technique is employed to investigate the presence and severity of meniscus tear in cadaver limbs. In a highly controlled ex vivo experimental design, a series of cadaver knees (n=6) were evaluated under an external vibration, and the frequency response of the joint was analyzed to differentiate the intact and affected samples. Four stages of knee integrity were considered: baseline, sham surgery, meniscus tear, and meniscectomy. Analyzing the frequency response of injured legs showed significant changes compared to the baseline and sham stages at selected frequency bandwidths. Furthermore, a qualitative analytical model of the knee was developed based on the Euler-Bernoulli beam theory representing the meniscus tear as a change in the local stiffness of the system. Similar trends in frequency response modulation were observed in the experimental results and analytical model. These findings serve as a foundation for further development of wearable devices for detection and grading of meniscus tear and for improving our understanding of the physiological effects of injuries on the vibration characteristics of the knee. Such systems can also aid in quantifying rehabilitation progress following reconstructive surgery and / or during physical therapy.

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3D-MRI combined with signal-to-noise ratio measurement can improve the diagnostic accuracy and sensitivity in evaluating meniscal healing status after meniscal repair

Abstract: III.

Cited by 9 publications

References 44 publications

MRI Criteria for Healing at 1 Year After Repair of a Traumatic Meniscal Tear

MRI Criteria for Healing at 1 Year After Repair of a Traumatic Meniscal Tear

Application of synthetic data in the training of artificial intelligence for automated quality assurance in magnetic resonance imaging

Vibration Stimulation as a Non-Invasive Approach to Monitor the Severity of Meniscus Tears

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