Salvatore Nigro1, Gennarina Arabia2, Angelo Antonini3, Luca Weis3, Andrea Marcante3, Alessandro Tessitore4,5, Mario Cirillo4,5, Gioacchino Tedeschi4,5, Stefano Zanigni6,7, Giovanna Calandra-Buonaura7,8, Caterina Tonon6,7, Gianni Pezzoli9, Roberto Cilia9, Mario Zappia10, Alessandra Nicoletti10, Calogero Edoardo Cicero10, Michele Tinazzi11, Pierluigi Tocco11, Nicolò Cardobi12, Aldo Quattrone13,14. 1. Institute of Bioimaging and Molecular Physiology, National Research Council, 88100, Catanzaro, Italy. 2. Institute of Neurology, Department of Medical and Surgical Sciences, University 'Magna Graecia', 88100, Catanzaro, Italy. 3. Parkinson's Disease and Movement Disorders Unit, 'Fondazione Ospedale San Camillo' - I.R.C.C.S, Venice-Lido, Italy. 4. Department of Medical, Surgical, Neurological, Metabolic and Aging Sciences, Second University of Naples, Naples, 80138, Italy. 5. MRI Research Center SUN-FISM, Second University of Naples, Naples, Italy. 6. Functional MR Unit, Policlinico S. Orsola - Malpighi, Bologna, Italy. 7. Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy. 8. IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy. 9. Parkinson Institute, ASST G.Pini - CTO, ex ICP, Milano, Italy. 10. Department 'G.F. Ingrassia', Section of Neurosciences, University of Catania, Catania, Italy. 11. Department of Neurological and Movement Sciences, University Hospital of Verona, Piazzale L.A. Scuro 10, 37134, Verona, Italy. 12. Institute of Radiology, University Hospital of Verona, Piazzale L.A. Scuro 10, 37134, Verona, Italy. 13. Institute of Bioimaging and Molecular Physiology, National Research Council, 88100, Catanzaro, Italy. quattrone@unicz.it. 14. Institute of Neurology, Department of Medical and Surgical Sciences, University 'Magna Graecia', 88100, Catanzaro, Italy. quattrone@unicz.it.
Abstract
OBJECTIVES: To investigate the reliability of a new in-house automatic algorithm for calculating the Magnetic Resonance Parkinsonism Index (MRPI), in a large multicentre study population of patients affected by progressive supranuclear palsy (PSP) or Parkinson's disease (PD), and healthy controls (HC), and to compare the diagnostic accuracy of the automatic and manual MRPI values. METHODS: The study included 88 PSP patients, 234 PD patients and 117 controls. MRI was performed using both 3T and 1.5T scanners. Automatic and manual MRPI values were evaluated, and accuracy of both methods in distinguishing PSP from PD and controls was calculated. RESULTS: No statistical differences were found between automated and manual MRPI values in all groups. The automatic MRPI values differentiated PSP from PD with an accuracy of 95 % (manual MRPI accuracy 96 %) and 97 % (manual MRPI accuracy 100 %) for 1.5T and 3T scanners, respectively. CONCLUSION: Our study showed that the new in-house automated method for MRPI calculation was highly accurate in distinguishing PSP from PD. Our automatic approach allows a widespread use of MRPI in clinical practice and in longitudinal research studies. KEY POINTS: • A new automatic method for calculating the MRPI is presented. • Automatic MRPI values are in good agreement with manual values. • Automatic MRPI can distinguish patients with PSP from patients with PD. • The automatic method overcomes MRPI application limitations in routine practice. • The automatic method may allow a more widespread use of MRPI.
OBJECTIVES: To investigate the reliability of a new in-house automatic algorithm for calculating the Magnetic Resonance Parkinsonism Index (MRPI), in a large multicentre study population of patients affected by progressive supranuclear palsy (PSP) or Parkinson's disease (PD), and healthy controls (HC), and to compare the diagnostic accuracy of the automatic and manual MRPI values. METHODS: The study included 88 PSPpatients, 234 PDpatients and 117 controls. MRI was performed using both 3T and 1.5T scanners. Automatic and manual MRPI values were evaluated, and accuracy of both methods in distinguishing PSP from PD and controls was calculated. RESULTS: No statistical differences were found between automated and manual MRPI values in all groups. The automatic MRPI values differentiated PSP from PD with an accuracy of 95 % (manual MRPI accuracy 96 %) and 97 % (manual MRPI accuracy 100 %) for 1.5T and 3T scanners, respectively. CONCLUSION: Our study showed that the new in-house automated method for MRPI calculation was highly accurate in distinguishing PSP from PD. Our automatic approach allows a widespread use of MRPI in clinical practice and in longitudinal research studies. KEY POINTS: • A new automatic method for calculating the MRPI is presented. • Automatic MRPI values are in good agreement with manual values. • Automatic MRPI can distinguish patients with PSP from patients with PD. • The automatic method overcomes MRPI application limitations in routine practice. • The automatic method may allow a more widespread use of MRPI.
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