Skip to content
2000
  1. Home
  2. A-Z Publications
  3. Current Medical Imaging
  4. Volume 20, Issue 1
  5. Article
Volume 20, Issue 1
  • ISSN: 1573-4056
  • E-ISSN: 1875-6603
file format pdf download PDF
side by side viewer icon HTML

Abstract

Background

A prosthetic device is designed based on the quantitative analysis of muscle MRI which will improve the muscle control achieved with functional electrical stimulation/ guided robotic exoskeletons. Electromyography (EMG) provides muscle functionality information while MRI provides the physiological and functionality of muscles. The sensor feedbacks were used for the bionic prosthesis, but the length of muscle using image processing was not correlated .

Objective

To investigate and perform qualitative and quantitative assessment of thigh muscle using MRI. The objective of the work is to improve the existing VAG signal classification method to diagnose abnormality using MRI for patients undergoing Total knee replacement (TKR).

Methods

A deep learning method for qualitative and quantitative assessment of thigh muscle is done using MRI. In existing prosthetic devices, electrical measurements of a person’s muscles are obtained using surface or implantable electrodes. Several methods were used for the classification and diagnostic processes. The existing methods have drawbacks in feature extraction and require experts to design the system. This work combines medical image processing and orthopaedic prosthetics to develop a therapeutic method.

Results & Discussion

This design provides much more precise control of prosthetic limbs using the image processing technique. The hybrid CNN swarm-based method measures the muscle structure and functions. Along with the sensor readings, these details are combined for prosthetic control. The implementation was carried out in MATLAB, Sketchuppro, and Arduino IDE.

Conclusion

A combined swarm intelligence and deep learning method were proposed for qualitative and quantitative assessment of thigh muscle. The prosthetic device choice was done from the scanned MRI image like Humerus-T prosthetics, segmental prosthesis and arthrodesis prosthesis. The investigation was done for the Total knee replacement (TKR) approach

© 2024 Arunachalam and N
This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Loading

Article metrics loading...

/content/journals/cmir/10.2174/0115734056284002240318055326
2024-01-01
2025-05-12

  • From This Site

    /content/journals/cmir/10.2174/0115734056284002240318055326
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
The full text of this item is not currently available.

References

  1. NiklassonE. BorgaM. Dahlqvist LeinhardO. WidholmP. AnderssonD.P. WiikA. HolmbergM. BrismarT.B. GustafssonT. LundbergT.R. Assessment of anterior thigh muscle size and fat infiltration using single-slice CT imaging versus automated MRI analysis in adults.Br. J. Radiol.20229511332021109410.1259/bjr.2021109435195445
     [Google Scholar]
  2. YamauchiK. YoshikoA. SuzukiS. KatoC. AkimaH. KatoT. IshidaK. Estimation of individual thigh muscle volumes from a single-slice muscle cross-sectional area and muscle thickness using magnetic resonance imaging in patients with knee osteoarthritis.J. Orthop. Surg.201725310.1177/230949901774310129212436
     [Google Scholar]
  3. VanmechelenI.M. ShortlandA.P. NobleJ.J. Lower limb muscle volume estimation from maximum cross-sectional area and muscle length in cerebral palsy and typically developing individuals.Clin. Biomech.201851404410.1016/j.clinbiomech.2017.11.00429179032
     [Google Scholar]
  4. HandsfieldG.G. MeyerC.H. AbelM.F. BlemkerS.S. Heterogeneity of muscle sizes in the lower limbs of children with cerebral palsy.Muscle Nerve201653693394510.1002/mus.2497226565390
     [Google Scholar]
  5. WilliamsS.A. StottN.S. ValentineJ. ElliottC. ReidS.L. Measuring skeletal muscle morphology and architecture with imaging modalities in children with cerebral palsy: A scoping review.Dev. Med. Child Neurol.202163326327310.1111/dmcn.1471433107594
     [Google Scholar]
  6. PonsC. BorotikarB. GaretierM. BurdinV. Ben SalemD. LempereurM. BrochardS. Quantifying skeletal muscle volume and shape in humans using MRI: A systematic review of validity and reliability.PLoS One20181311e020784710.1371/journal.pone.020784730496308
     [Google Scholar]
  7. WhittaG. LiangJ. StottN.S. MirjaliliS.A. BattinM. WilliamsS.A. The reliability of the measurement of muscle volume using magnetic resonance imaging in typically developing infants by two raters.Sci. Rep.20221211819110.1038/s41598‑022‑23087‑y36307532
     [Google Scholar]
  8. NaruseM TrappeS TrappeTA Human skeletal muscle size with ultrasound imaging: A comprehensive review.J. Appl. Physiol.1985132512671279
     [Google Scholar]
  9. AmabileC. MoalB. ChtaraO.A. PilletH. RayaJ.G. IannessiA. SkalliW. LafageV. BronsardN. Estimation of spinopelvic muscles’ volumes in young asymptomatic subjects: a quantitative analysis.Surg. Radiol. Anat.201739439340310.1007/s00276‑016‑1742‑627637762
     [Google Scholar]
  10. MersmannF BohmS SchrollA BoethH DudaG ArampatzisA Muscle shape consistency and muscle volume prediction of thigh muscles.Scand J. Med. Sci. Sports.201525220821310.1111/sms.12285
     [Google Scholar]
  11. De BeukelaerN. VandekerckhoveI. HuygheE. MolenberghsG. PeetersN. HanssenB. OrtibusE. Van CampenhoutA. DesloovereK. Morphological medial gastrocnemius muscle growth in ambulant children with spastic cerebral palsy: A prospective longitudinal study.J. Clin. Med.2023124156410.3390/jcm1204156436836099
     [Google Scholar]
  12. HanssenB. PeetersN. De BeukelaerN. VanneromA. PeetersL. MolenaersG. Van CampenhoutA. DeschepperE. Van den BroeckC. DesloovereK. Progressive resistance training for children with cerebral palsy: A randomized controlled trial evaluating the effects on muscle strength and morphology.Front. Physiol.20221391116210.3389/fphys.2022.91116236267577
     [Google Scholar]
  13. Bin GhouthS.G. WilliamsS.A. ReidS.L. BesierT.F. HandsfieldG.G. A statistical shape model of soleus muscle morphology in spastic cerebral palsy.Sci. Rep.2022121771110.1038/s41598‑022‑11611‑z35546597
     [Google Scholar]
  14. TschiedelM. RussoldM.F. KaniusasE. Relying on more sense for enhancing lower limb prostheses control: A review.J. Neuroeng. Rehabil.20201719910.1186/s12984‑020‑00726‑x32680530
     [Google Scholar]
  15. FluitR. PrinsenE.C. WangS. van der KooijH. A comparison of control strategies in commercial and research knee prostheses.IEEE Trans. Biomed. Eng.202067127729010.1109/TBME.2019.291246631021749
     [Google Scholar]
  16. Bernal-TorresM Medellín-CastilloH GonzálezA. Development of an active biomimetic-controlled transfemoral knee prosthesis.ASME 2016 International Mechanical Engineering Congress and Exposition201611010.1115/IMECE2016‑67211
     [Google Scholar]
  17. ParriA MartiniE GeeromsJ Whole body awareness for controlling a robotic transfemoral prosthesis.Front Neurorobot.2017112510.3389/fnbot.2017.00025
     [Google Scholar]
  18. LiuM. WangD. HuangH. Development of an environment-aware locomotion mode recognition system for powered lower limb prostheses.IEEE Trans. Neural Syst. Rehabil. Eng.201624443444310.1109/TNSRE.2015.242053925879962
     [Google Scholar]
  19. HaqA. LiJ.P. KhanS. AlsharaM.A. AlotaibiR.M. MawuliC. DACBT: Deep learning approach for classification of brain tumors using MRI data in IoT healthcare environment.Sci. Rep.20221211533110.1038/s41598‑022‑19465‑136097024
     [Google Scholar]
  20. AurnaN.F. YousufM.A. TaherK.A. AzadA.K.M. MoniM.A. A classification of MRI brain tumor based on two stage feature level ensemble of deep CNN models.Comput. Biol. Med.202214610553910.1016/j.compbiomed.2022.10553935483227
     [Google Scholar]
  21. ZainEldinH. GamelS.A. El-KenawyE.S.M. AlharbiA.H. KhafagaD.S. IbrahimA. TalaatF.M. Brain tumor detection and classification using deep learning and sine-cosine fitness grey wolf optimization.Bioengineering20221011810.3390/bioengineering1001001836671591
     [Google Scholar]
  22. TurkO. OzhanD. AcarE. AkinciT.C. YilmazM. Automatic detection of brain tumors with the aid of ensemble deep learning architectures and class activation map indicators by employing magnetic resonance images.Z. Med. Phys.2022S0939-3889(22)00131-310.1016/j.zemedi.2022.11.01036593139
     [Google Scholar]
  23. HollonT.C. PandianB. AdapaA.R. UriasE. SaveA.V. KhalsaS.S.S. EichbergD.G. D’AmicoR.S. FarooqZ.U. LewisS. PetridisP.D. MarieT. ShahA.H. GartonH.J.L. MaherC.O. HethJ.A. McKeanE.L. SullivanS.E. Hervey-JumperS.L. PatilP.G. ThompsonB.G. SagherO. McKhannG.M.II KomotarR.J. IvanM.E. SnuderlM. OttenM.L. JohnsonT.D. SistiM.B. BruceJ.N. MuraszkoK.M. TrautmanJ. FreudigerC.W. CanollP. LeeH. Camelo-PiraguaS. OrringerD.A. Near real-time intraoperative brain tumor diagnosis using stimulated Raman histology and deep neural networks.Nat. Med.2020261525810.1038/s41591‑019‑0715‑931907460
     [Google Scholar]
  24. AlmadhounH.R. Abu-NaserS.S. Detection of brain tumor using deep learning.Intern. J. Acad. Eng. Res.2022632947
     [Google Scholar]
  25. HoseiniF. ShahbahramiA. BayatP. An efficient implementation of deep convolutional neural networks for MRI segmentation.J. Digit. Imaging201831573874710.1007/s10278‑018‑0062‑229488179
     [Google Scholar]
  26. IqbalS. GhaniM.U. SabaT. RehmanA. Brain tumor segmentation in multi‐spectral MRI using convolutional neural networks (CNN).Microsc. Res. Tech.201881441942710.1002/jemt.2299429356229
     [Google Scholar]
  27. LouresF.B. CorreiaW. ReisJ.H. Pires e AlbuquerqueR.S. de Paula MozelaA. de SouzaE.B. MaiaP.V. BarrettoJ.M. Outcomes after knee arthroplasty in extra-articular deformity.Int. Orthop.20194392065207010.1007/s00264‑018‑4147‑930215100
     [Google Scholar]
  28. WalkerL.C. ClementN.D. DeehanD.J. Predicting the outcome of total knee arthroplasty using the WOMAC score: A review of the literature.J. Knee Surg.201932873674110.1055/s‑0038‑166686629991079
     [Google Scholar]
  29. SongY. ZhuF. LinF. ZhangF. ZhangS. Bone quality, and the combination and penetration of cement–bone interface.Medicine20189735e1198710.1097/MD.000000000001198730170401
     [Google Scholar]
  30. Mohd MukhtarN.Q. ShuibS. AnuarM.A. Mohd MiswanM.F. Mohd AnuarM.A. Design optimisation of bi-cruciate retaining total knee arthroplasty (TKA) prosthesis via taguchi methods.Mathematics202311231210.3390/math11020312
     [Google Scholar]
  31. Dall’OcaC. RicciM. VecchiniE. GianniniN. LambertiD. TromponiC. MagnanB. Evolution of TKA design.Acta Biomed.2017882S173128657559
     [Google Scholar]
  32. DongY. ZhangZ. DongW. HuG. WangB. MouZ. An optimization method for implantation parameters of individualized TKA tibial prosthesis based on finite element analysis and orthogonal experimental design.BMC Musculoskelet. Disord.202021116510.1186/s12891‑020‑3189‑532164625
     [Google Scholar]
  33. KohY.G. ParkK.M. KangK.T. The biomechanical effect of tibiofemoral conformity design for patient-specific cruciate retainging total knee arthroplasty using computational simulation.J. Exp. Orthop.2019612310.1186/s40634‑019‑0192‑631161463
     [Google Scholar]
  34. MouZ. DongW. ZhangZ. WangA. HuG. WangB. DongY. Optimization of parameters for femoral component implantation during TKA using finite element analysis and orthogonal array testing.J. Orthop. Surg. Res.201813117910.1186/s13018‑018‑0891‑130029670
     [Google Scholar]
  35. LiuF ZhouZ JangH SamsonovA ZhaoG KijowskiR Deep convolutional neural network and 3D deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging.Magn. Reson. Med.201779423792391
     [Google Scholar]
  36. SuB.Y. WangJ. LiuS.Q. ShengM. JiangJ. XiangK. A CNN-based method for intent recognition using inertial measurement units and intelligent lower limb prosthesis.IEEE Trans. Neural Syst. Rehabil. Eng.20192751032104210.1109/TNSRE.2019.290958530969928
     [Google Scholar]
  37. LeijendekkersR.A. MarraM.A. PloegmakersM.J.M. Van HinteG. FrölkeJ.P. Van De MeentH. StaalJ.B. HoogeboomT.J. VerdonschotN. Magnetic-resonance-imaging-based three-dimensional muscle reconstruction of hip abductor muscle volume in a person with a transfemoral bone-anchored prosthesis: A feasibility study.Physiother. Theory Pract.201935549550410.1080/09593985.2018.145390229589767
     [Google Scholar]
  38. RahmanM.L. RezaA.W. ShabujS.I. An internet of things-based automatic brain tumor detection system.Ind. J. Elec. Eng. Comput. Sci.202225121422210.11591/ijeecs.v25.i1.pp214‑222
     [Google Scholar]
  39. KarpińskiR. KrakowskiP. JonakJ. MachrowskaA. MaciejewskiM. NogalskiA. Diagnostics of articular cartilage damage based on generated acoustic signals using ANN—Part I: Femoral-tibial joint.Sensors2022226217610.3390/s2206217635336346
     [Google Scholar]
/content/journals/cmir/10.2174/0115734056284002240318055326
Loading
Deep Learning-based Thigh Muscle Investigation Using MRI For Prosthetic Development for Patients Undergoing Total Knee Replacement (TKR)
Curr. Med. Imaging 20, e15734056284002 (2024); https://doi.org/10.2174/0115734056284002240318055326
/content/journals/cmir/10.2174/0115734056284002240318055326
/content/journals/cmir/10.2174/0115734056284002240318055326
Loading

Data & Media loading...


  • Article Type:     Research Article
Keyword(s): CNN; Deep learning; MRI; Prosthetic; thigh muscle; total knee replacement

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test