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Peters S, Kellermann G, Watkinson J, Gärtner F, Huhndorf M, Stürner K, Jansen O, Larsen N. AI supported detection of cerebral multiple sclerosis lesions decreases radiologic reporting times. Eur J Radiol 2024; 178:111638. [PMID: 39067268 DOI: 10.1016/j.ejrad.2024.111638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2024] [Revised: 06/10/2024] [Accepted: 07/16/2024] [Indexed: 07/30/2024]
Abstract
PURPOSE Multiple Sclerosis (MS) is a common autoimmune disease of the central nervous system. MRI plays a crucial role in diagnosing as well as in disease and treatment monitoring. Therefore, evaluation of cerebral MRI of MS patients is part of daily clinical routine. A growing number of companies offer commercial software to support the reporting with automated lesion detection. Aim of this study was to evaluate the effect of such a software with AI supported lesion detection to the radiologic reporting. METHOD Four radiologist each counted MS-lesions in MRI examinations of 50 patients separated by the locations periventricular, cortical/juxtacortical, infrantentorial and unspecific white matter. After at least six weeks they repeated the evaluation, this time using the AI based software mdbrain for lesion detection. In both settings the required time was documented. Further the radiologists evaluated follow-up MRI of 50 MS-patients concerning new and enlarging lesions in the same manner. RESULTS To determine the lesion-load the average reporting time decreased from 286.85 sec to 196.34 sec (p > 0.001). For the evaluation of the follow-up images the reporting time dropped from 196.17 sec to 120.87 sec (p < 0.001). The interrater reliabilities showed no significant differences for the determination of lesion-load (0.83 without vs. 0.8 with software support) and for the detection of new/enlarged lesions (0.92 without vs. 0.82 with software support). CONCLUSION For the evaluation of MR images of MS patients, an AI-based support for image-interpretation can significantly decreases reporting times.
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Affiliation(s)
- Sönke Peters
- Department of Radiology and Neuroradiology, University Hospital of Schleswig-Holstein, Campus Kiel, Germany.
| | - Gesa Kellermann
- Department of Radiology, Bundeswehr Hospital Hamburg, Germany
| | - Joe Watkinson
- Department of Radiology and Neuroradiology, University Hospital of Schleswig-Holstein, Campus Kiel, Germany
| | - Friederike Gärtner
- Department of Radiology and Neuroradiology, University Hospital of Schleswig-Holstein, Campus Kiel, Germany
| | - Monika Huhndorf
- Department of Radiology and Neuroradiology, University Hospital of Schleswig-Holstein, Campus Kiel, Germany
| | - Klarissa Stürner
- Department of Neurology, University Hospital of Schleswig-Holstein, Campus Kiel, Germany
| | - Olav Jansen
- Department of Radiology and Neuroradiology, University Hospital of Schleswig-Holstein, Campus Kiel, Germany
| | - Naomi Larsen
- Department of Radiology and Neuroradiology, University Hospital of Schleswig-Holstein, Campus Kiel, Germany
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Spagnolo F, Depeursinge A, Schädelin S, Akbulut A, Müller H, Barakovic M, Melie-Garcia L, Bach Cuadra M, Granziera C. How far MS lesion detection and segmentation are integrated into the clinical workflow? A systematic review. Neuroimage Clin 2023; 39:103491. [PMID: 37659189 PMCID: PMC10480555 DOI: 10.1016/j.nicl.2023.103491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Accepted: 08/04/2023] [Indexed: 09/04/2023]
Abstract
INTRODUCTION Over the past few years, the deep learning community has developed and validated a plethora of tools for lesion detection and segmentation in Multiple Sclerosis (MS). However, there is an important gap between validating models technically and clinically. To this end, a six-step framework necessary for the development, validation, and integration of quantitative tools in the clinic was recently proposed under the name of the Quantitative Neuroradiology Initiative (QNI). AIMS Investigate to what extent automatic tools in MS fulfill the QNI framework necessary to integrate automated detection and segmentation into the clinical neuroradiology workflow. METHODS Adopting the systematic Cochrane literature review methodology, we screened and summarised published scientific articles that perform automatic MS lesions detection and segmentation. We categorised the retrieved studies based on their degree of fulfillment of QNI's six-steps, which include a tool's technical assessment, clinical validation, and integration. RESULTS We found 156 studies; 146/156 (94%) fullfilled the first QNI step, 155/156 (99%) the second, 8/156 (5%) the third, 3/156 (2%) the fourth, 5/156 (3%) the fifth and only one the sixth. CONCLUSIONS To date, little has been done to evaluate the clinical performance and the integration in the clinical workflow of available methods for MS lesion detection/segmentation. In addition, the socio-economic effects and the impact on patients' management of such tools remain almost unexplored.
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Affiliation(s)
- Federico Spagnolo
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Department of Neurology, University Hospital Basel, Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland; MedGIFT, Institute of Informatics, School of Management, HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland
| | - Adrien Depeursinge
- MedGIFT, Institute of Informatics, School of Management, HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland; Nuclear Medicine and Molecular Imaging Department, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
| | - Sabine Schädelin
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Clinical Trial Unit, Department of Clinical Research, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Aysenur Akbulut
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Ankara University School of Medicine, Ankara, Turkey
| | - Henning Müller
- MedGIFT, Institute of Informatics, School of Management, HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland; The Sense Research and Innovation Center, Lausanne and Sion, Switzerland
| | - Muhamed Barakovic
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Department of Neurology, University Hospital Basel, Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Lester Melie-Garcia
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Department of Neurology, University Hospital Basel, Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Meritxell Bach Cuadra
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland; Radiology Department, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
| | - Cristina Granziera
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Department of Neurology, University Hospital Basel, Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland.
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3
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Solomon C, Shmueli O, Shrot S, Blumenfeld-Katzir T, Radunsky D, Omer N, Stern N, Reichman DBA, Hoffmann C, Salti M, Greenspan H, Ben-Eliezer N. Psychophysical Evaluation of Visual vs. Computer-Aided Detection of Brain Lesions on Magnetic Resonance Images. J Magn Reson Imaging 2023; 58:642-649. [PMID: 36495014 DOI: 10.1002/jmri.28559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 11/25/2022] [Accepted: 11/28/2022] [Indexed: 07/20/2023] Open
Abstract
BACKGROUND Magnetic resonance imaging (MRI) diagnosis is usually performed by analyzing contrast-weighted images, where pathology is detected once it reached a certain visual threshold. Computer-aided diagnosis (CAD) has been proposed as a way for achieving higher sensitivity to early pathology. PURPOSE To compare conventional (i.e., visual) MRI assessment of artificially generated multiple sclerosis (MS) lesions in the brain's white matter to CAD based on a deep neural network. STUDY TYPE Prospective. POPULATION A total of 25 neuroradiologists (15 males, age 39 ± 9, 9 ± 9.8 years of experience) independently assessed all synthetic lesions. FIELD STRENGTH/SEQUENCE A 3.0 T, T2 -weighted multi-echo spin-echo (MESE) sequence. ASSESSMENT MS lesions of varying severity levels were artificially generated in healthy volunteer MRI scans by manipulating T2 values. Radiologists and a neural network were tasked with detecting these lesions in a series of 48 MR images. Sixteen images presented healthy anatomy and the rest contained a single lesion at eight increasing severity levels (6%, 9%, 12%, 15%, 18%, 21%, 25%, and 30% elevation in T2 ). True positive (TP) rates, false positive (FP) rates, and odds ratios (ORs) were compared between radiological diagnosis and CAD across the range lesion severity levels. STATISTICAL TESTS Diagnostic performance of the two approaches was compared using z-tests on TP rates, FP rates, and the logarithm of ORs across severity levels. A P-value <0.05 was considered statistically significant. RESULTS ORs of identifying pathology were significantly higher for CAD vis-à-vis visual inspection for all lesions' severity levels. For a 6% change in T2 value (lowest severity), radiologists' TP and FP rates were not significantly different (P = 0.12), while the corresponding CAD results remained statistically significant. DATA CONCLUSION CAD is capable of detecting the presence or absence of more subtle lesions with greater precision than the representative group of 25 radiologists chosen in this study. LEVEL OF EVIDENCE 1 TECHNICAL EFFICACY: Stage 3.
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Affiliation(s)
- Chen Solomon
- Department of Biomedical Engineering, Tel-Aviv University, Tel-Aviv, Israel
| | - Omer Shmueli
- Department of Biomedical Engineering, Tel-Aviv University, Tel-Aviv, Israel
| | - Shai Shrot
- Sackler School of Medicine, Tel-Aviv University, Tel-Aviv, Israel
- Department of Diagnostic Imaging, Sheba Medical Center, Ramat-Gan, Israel
| | | | - Dvir Radunsky
- Department of Biomedical Engineering, Tel-Aviv University, Tel-Aviv, Israel
| | - Noam Omer
- Department of Biomedical Engineering, Tel-Aviv University, Tel-Aviv, Israel
| | - Neta Stern
- Department of Biomedical Engineering, Tel-Aviv University, Tel-Aviv, Israel
| | | | - Chen Hoffmann
- Sackler School of Medicine, Tel-Aviv University, Tel-Aviv, Israel
- Department of Diagnostic Imaging, Sheba Medical Center, Ramat-Gan, Israel
| | - Moti Salti
- Brain Imaging Research Center (BIRC), Ben-Gurion University, Beer-Sheva, Israel
- Zlotowski Center for Neuroscience, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Hayit Greenspan
- Department of Biomedical Engineering, Tel-Aviv University, Tel-Aviv, Israel
| | - Noam Ben-Eliezer
- Department of Biomedical Engineering, Tel-Aviv University, Tel-Aviv, Israel
- Zlotowski Center for Neuroscience, Ben-Gurion University of the Negev, Beer-Sheva, Israel
- Center for Advanced Imaging Innovation and Research (CAI2R), New York University, New York, New York, USA
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
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Park CC, Brummer ME, Sadigh G, Saindane AM, Mullins ME, Allen JW, Hu R. Automated Registration and Color Labeling of Serial 3D Double Inversion Recovery MR Imaging for Detection of Lesion Progression in Multiple Sclerosis. J Digit Imaging 2023; 36:450-457. [PMID: 36352165 PMCID: PMC10039147 DOI: 10.1007/s10278-022-00737-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 11/01/2022] [Accepted: 11/02/2022] [Indexed: 11/11/2022] Open
Abstract
Automated co-registration and subtraction techniques have been shown to be useful in the assessment of longitudinal changes in multiple sclerosis (MS) lesion burden, but the majority depend on T2-fluid-attenuated inversion recovery sequences. We aimed to investigate the use of a novel automated temporal color complement imaging (CCI) map overlapped on 3D double inversion recovery (DIR), and to assess its diagnostic performance for detecting disease progression in patients with multiple sclerosis (MS) as compared to standard review of serial 3D DIR images. We developed a fully automated system that co-registers and compares baseline to follow-up 3D DIR images and outputs a pseudo-color RGB map in which red pixels indicate increased intensity values in the follow-up image (i.e., progression; new/enlarging lesion), blue-green pixels represent decreased intensity values (i.e., disappearing/shrinking lesion), and gray-scale pixels reflect unchanged intensity values. Three neuroradiologists blinded to clinical information independently reviewed each patient using standard DIR images alone and using CCI maps based on DIR images at two separate exams. Seventy-six follow-up examinations from 60 consecutive MS patients who underwent standard 3 T MR brain MS protocol that included 3D DIR were included. Median cohort age was 38.5 years, with 46 women, 59 relapsing-remitting type MS, and median follow-up interval of 250 days (interquartile range: 196-394 days). Lesion progression was detected in 67.1% of cases using CCI review versus 22.4% using standard review, with a total of 182 new or enlarged lesions using CCI review versus 28 using standard review. There was a statistically significant difference between the two methods in the rate of all progressive lesions (P < 0.001, McNemar's test) as well as cortical progressive lesions (P < 0.001). Automated CCI maps using co-registered serial 3D DIR, compared to standard review of 3D DIR alone, increased detection rate of MS lesion progression in patients undergoing clinical brain MRI exam.
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Affiliation(s)
- Charlie C Park
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, 1364 Clifton Road NE, Suite BG20, Atlanta, GA, 30322, USA
| | - Marijn E Brummer
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, 1364 Clifton Road NE, Suite BG20, Atlanta, GA, 30322, USA
| | - Gelareh Sadigh
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, 1364 Clifton Road NE, Suite BG20, Atlanta, GA, 30322, USA
| | - Amit M Saindane
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, 1364 Clifton Road NE, Suite BG20, Atlanta, GA, 30322, USA
| | - Mark E Mullins
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, 1364 Clifton Road NE, Suite BG20, Atlanta, GA, 30322, USA
| | - Jason W Allen
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, 1364 Clifton Road NE, Suite BG20, Atlanta, GA, 30322, USA
| | - Ranliang Hu
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, 1364 Clifton Road NE, Suite BG20, Atlanta, GA, 30322, USA.
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5
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Mattay RR, Davtyan K, Rudie JD, Mattay GS, Jacobs DA, Schindler M, Loevner LA, Schnall MD, Bilello M, Mamourian AC, Cook TS. Economic impact of selective use of contrast for routine follow-up MRI of patients with multiple sclerosis. J Neuroimaging 2022; 32:656-666. [PMID: 35294074 DOI: 10.1111/jon.12984] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 02/19/2022] [Accepted: 02/22/2022] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND AND PURPOSE Imaging and autopsy studies show intracranial gadolinium deposition in patients who have undergone serial contrast-enhanced MRIs. This observation has raised concerns when using contrast administration in patients who receive frequent MRIs. To address this, we implemented a contrast-conditional protocol wherein gadolinium is administered only for multiple sclerosis (MS) patients with imaging evidence of new disease activity on precontrast imaging. In this study, we explore the economic impact of our new MRI protocol. METHODS We compared scanner time and Medicare reimbursement using our contrast-conditional methodology versus that of prior protocols where all patients received gadolinium. RESULTS For 422 patients over 4 months, the contrast-conditional protocol amounted to 60% decrease in contrast injection and savings of approximately 20% of MRI scanner time. If the extra scanner time was used for performing MS follow-up MRIs in additional patients, the contrast-conditional protocol would amount to net revenue loss of $21,707 (∼3.7%). CONCLUSIONS Implementation of a new protocol to limit contrast in MS follow-up MRIs led to a minimal decrease in revenue when controlled for scanner time utilized and is outweighed by other benefits, including substantial decreased gadolinium administration, increased patient comfort, and increased availability of scanner time, which depending on type of studies performed could result in additional financial benefit.
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Affiliation(s)
- Raghav R Mattay
- Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Karapet Davtyan
- Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Jeffrey D Rudie
- Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California, USA
| | - Govind S Mattay
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Dina A Jacobs
- Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Matthew Schindler
- Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Laurie A Loevner
- Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Mitchell D Schnall
- Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Michel Bilello
- Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Alexander C Mamourian
- Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Radiology, Penn State Milton S. Hershey Medical Center, Hershey, Pennsylvania, USA
| | - Tessa S Cook
- Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania, USA
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6
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Combès B, Kerbrat A, Pasquier G, Commowick O, Le Bon B, Galassi F, L'Hostis P, El Graoui N, Chouteau R, Cordonnier E, Edan G, Ferré JC. A Clinically-Compatible Workflow for Computer-Aided Assessment of Brain Disease Activity in Multiple Sclerosis Patients. Front Med (Lausanne) 2021; 8:740248. [PMID: 34805206 PMCID: PMC8595265 DOI: 10.3389/fmed.2021.740248] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Accepted: 09/30/2021] [Indexed: 11/21/2022] Open
Abstract
Over the last 10 years, the number of approved disease modifying drugs acting on the focal inflammatory process in Multiple Sclerosis (MS) has increased from 3 to 10. This wide choice offers the opportunity of a personalized medicine with the objective of no clinical and radiological activity for each patient. This new paradigm requires the optimization of the detection of new FLAIR lesions on longitudinal MRI. In this paper, we describe a complete workflow—that we developed, implemented, deployed, and evaluated—to facilitate the monitoring of new FLAIR lesions on longitudinal MRI of MS patients. This workflow has been designed to be usable by both hospital and private neurologists and radiologists in France. It consists of three main components: (i) a software component that allows for automated and secured anonymization and transfer of MRI data from the clinical Picture Archive and Communication System (PACS) to a processing server (and vice-versa); (ii) a fully automated segmentation core that enables detection of focal longitudinal changes in patients from T1-weighted, T2-weighted and FLAIR brain MRI scans, and (iii) a dedicated web viewer that provides an intuitive visualization of new lesions to radiologists and neurologists. We first present these different components. Then, we evaluate the workflow on 54 pairs of longitudinal MRI scans that were analyzed by 3 experts (1 neuroradiologist, 1 radiologist, and 1 neurologist) with and without the proposed workflow. We show that our workflow provided a valuable aid to clinicians in detecting new MS lesions both in terms of accuracy (mean number of detected lesions per patient and per expert 1.8 without the workflow vs. 2.3 with the workflow, p = 5.10−4) and of time dedicated by the experts (mean time difference 2′45″, p = 10−4). This increase in the number of detected lesions has implications in the classification of MS patients as stable or active, even for the most experienced neuroradiologist (mean sensitivity was 0.74 without the workflow and 0.90 with the workflow, p-value for no difference = 0.003). It therefore has potential consequences on the therapeutic management of MS patients.
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Affiliation(s)
- Benoit Combès
- Univ Rennes, Inria, CNRS, Inserm IRISA UMR 6074, Empenn ERL U 1228, Rennes, France
| | - Anne Kerbrat
- Univ Rennes, Inria, CNRS, Inserm IRISA UMR 6074, Empenn ERL U 1228, Rennes, France.,Neurology Department, Rennes University Hospital, Rennes, France
| | | | - Olivier Commowick
- Univ Rennes, Inria, CNRS, Inserm IRISA UMR 6074, Empenn ERL U 1228, Rennes, France
| | - Brandon Le Bon
- Univ Rennes, Inria, CNRS, Inserm IRISA UMR 6074, Empenn ERL U 1228, Rennes, France
| | - Francesca Galassi
- Univ Rennes, Inria, CNRS, Inserm IRISA UMR 6074, Empenn ERL U 1228, Rennes, France
| | | | - Nora El Graoui
- Univ Rennes, Inria, CNRS, Inserm IRISA UMR 6074, Empenn ERL U 1228, Rennes, France.,CHU Rennes, Department of Neuroradiology, Rennes, France
| | - Raphael Chouteau
- Neurology Department, Rennes University Hospital, Rennes, France
| | | | - Gilles Edan
- Univ Rennes, Inria, CNRS, Inserm IRISA UMR 6074, Empenn ERL U 1228, Rennes, France.,Neurology Department, Rennes University Hospital, Rennes, France
| | - Jean-Christophe Ferré
- Univ Rennes, Inria, CNRS, Inserm IRISA UMR 6074, Empenn ERL U 1228, Rennes, France.,CHU Rennes, Department of Neuroradiology, Rennes, France
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7
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Bonanno L, Mammone N, De Salvo S, Bramanti A, Rifici C, Sessa E, Bramanti P, Marino S, Ciurleo R. Multiple Sclerosis lesions detection by a hybrid Watershed-Clustering algorithm. Clin Imaging 2020; 72:162-167. [PMID: 33278790 DOI: 10.1016/j.clinimag.2020.11.006] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2020] [Revised: 09/21/2020] [Accepted: 11/02/2020] [Indexed: 11/18/2022]
Abstract
BACKGROUND Computer Aided Diagnosis (CAD) systems have been developing in the last years with the aim of helping the diagnosis and monitoring of several diseases. We present a novel CAD system based on a hybrid Watershed-Clustering algorithm for the detection of lesions in Multiple Sclerosis. METHODS Magnetic Resonance Imaging scans (FLAIR sequences without gadolinium) of 20 patients affected by Multiple Sclerosis with hyperintense lesions were studied. The CAD system consisted of the following automated processing steps: images recording, automated segmentation based on the Watershed algorithm, detection of lesions, extraction of both dynamic and morphological features, and classification of lesions by Cluster Analysis. RESULTS The investigation was performed on 316 suspect regions including 255 lesion and 61 non-lesion cases. The Receiver Operating Characteristic analysis revealed a highly significant difference between lesions and non-lesions; the diagnostic accuracy was 87% (95% CI: 0.83-0.90), with an appropriate cut-off of 192.8; the sensitivity was 77% and the specificity was 87%. CONCLUSIONS In conclusion, we developed a CAD system by using a modified algorithm for automated image segmentation which may discriminate MS lesions from non-lesions. The proposed method generates a detection out-put that may be support the clinical evaluation.
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Affiliation(s)
- Lilla Bonanno
- IRCCS Centro Neurolesi "Bonino-Pulejo", Messina, Italy
| | - Nadia Mammone
- IRCCS Centro Neurolesi "Bonino-Pulejo", Messina, Italy
| | | | | | | | - Edoardo Sessa
- IRCCS Centro Neurolesi "Bonino-Pulejo", Messina, Italy
| | | | - Silvia Marino
- IRCCS Centro Neurolesi "Bonino-Pulejo", Messina, Italy
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8
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Simon AF, Holmes JH, Schwartz ES. Decreasing radiologist burnout through informatics-based solutions. Clin Imaging 2019; 59:167-171. [PMID: 31821974 DOI: 10.1016/j.clinimag.2019.10.014] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2019] [Revised: 10/21/2019] [Accepted: 10/28/2019] [Indexed: 01/06/2023]
Abstract
Increased performance demands have interacted with suboptimal use of technology and contributed to burnout among radiologists. Although the problem of radiologist burnout has been well documented, there is a gap in the literature in terms of how technology can be better utilized to lessen the problem. Informatics-based modifications to existing technology hold the potential to reduce the amount of time radiologists spend on noninterpretive tasks, decrease interruptions, facilitate connections with colleagues, and improve patient care. Examples of successful modifications to technology are presented and discussed in relation to how they contribute to improving workplace engagement among radiologists.
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Affiliation(s)
- Andrew F Simon
- Department of Psychology, Seton Hall University, South Orange, NJ, United States of America
| | - John H Holmes
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States of America
| | - Erin Simon Schwartz
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, The Children's Hospital of Philadelphia, Philadelphia, PA, United States of America.
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9
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Duong MT, Rauschecker AM, Rudie JD, Chen PH, Cook TS, Bryan RN, Mohan S. Artificial intelligence for precision education in radiology. Br J Radiol 2019; 92:20190389. [PMID: 31322909 DOI: 10.1259/bjr.20190389] [Citation(s) in RCA: 80] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
In the era of personalized medicine, the emphasis of health care is shifting from populations to individuals. Artificial intelligence (AI) is capable of learning without explicit instruction and has emerging applications in medicine, particularly radiology. Whereas much attention has focused on teaching radiology trainees about AI, here our goal is to instead focus on how AI might be developed to better teach radiology trainees. While the idea of using AI to improve education is not new, the application of AI to medical and radiological education remains very limited. Based on the current educational foundation, we highlight an AI-integrated framework to augment radiology education and provide use case examples informed by our own institution's practice. The coming age of "AI-augmented radiology" may enable not only "precision medicine" but also what we describe as "precision medical education," where instruction is tailored to individual trainees based on their learning styles and needs.
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Affiliation(s)
- Michael Tran Duong
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Andreas M Rauschecker
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA.,Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Jeffrey D Rudie
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA.,Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Po-Hao Chen
- Imaging Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Tessa S Cook
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - R Nick Bryan
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA.,Department of Diagnostic Medicine, Dell Medical School, University of Texas at Austin, Austin, TX, USA
| | - Suyash Mohan
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA.,Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA, USA
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10
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Rudie JD, Mattay RR, Schindler M, Steingall S, Cook TS, Loevner LA, Schnall MD, Mamourian AC, Bilello M. An Initiative to Reduce Unnecessary Gadolinium-Based Contrast in Multiple Sclerosis Patients. J Am Coll Radiol 2019; 16:1158-1164. [PMID: 31092348 DOI: 10.1016/j.jacr.2019.04.005] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2019] [Revised: 04/08/2019] [Accepted: 04/09/2019] [Indexed: 02/04/2023]
Abstract
OBJECTIVE Patients with multiple sclerosis (MS) routinely undergo serial contrast-enhanced MRIs. Given concerns regarding tissue deposition of gadolinium-based contrast agents (GBCAs) and evidence that enhancement of lesions is only seen in patients with new disease activity on noncontrast imaging, we set out to implement a prospective quality improvement project whereby intravenous contrast would be reserved only for patients with evidence of new disease activity on noncontrast images. METHODS To prospectively implement such a protocol, we leveraged our in-house computer-assisted detection (CAD) software and 3-D laboratory radiology technologists to perform real-time preliminary assessments of the CAD-processed T2 fluid attenuated inversion recovery (FLAIR) noncontrast images as a basis for deciding whether to inject contrast. Before implementation, we held multidisciplinary meetings with neurology, neuroradiology, and MR technologists and distributed surveys to objectively assess opinions and obstacles to clinical implementation. We evaluated reduction in GBCA utilization and technologist performance relative to final neuroradiologist interpretations. RESULTS During a 2-month trial period, 153 patients were imaged under the new protocol. Technologists using the CAD software were able to identify patients with new or enlarging lesions on FLAIR images with 95% accuracy and 97% negative predictive value relative to final neuroradiologist interpretations, which allowed us to avoid the use of contrast and additional imaging sequences in 87% of patients. DISCUSSION A multidisciplinary effort to implement a quality improvement project to limit contrast in MS patients receiving follow-up MRIs allowed for improved safety and cost by targeting patients that would benefit from the use of intravenous contrast in real-time.
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Affiliation(s)
- Jeffrey D Rudie
- Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania
| | - Raghav R Mattay
- Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania
| | - Matthew Schindler
- Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania
| | - Samantha Steingall
- Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania
| | - Tessa S Cook
- Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania
| | - Laurie A Loevner
- Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania
| | - Mitchell D Schnall
- Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania
| | - Alexander C Mamourian
- Department of Radiology, Penn State Milton S. Hershey Medical Center, Hershey, Pennsylvania
| | - Michel Bilello
- Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania.
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11
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Eichinger P, Schön S, Pongratz V, Wiestler H, Zhang H, Bussas M, Hoshi MM, Kirschke J, Berthele A, Zimmer C, Hemmer B, Mühlau M, Wiestler B. Accuracy of Unenhanced MRI in the Detection of New Brain Lesions in Multiple Sclerosis. Radiology 2019; 291:429-435. [PMID: 30860448 DOI: 10.1148/radiol.2019181568] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Background Administration of a gadolinium-based contrast material is widely considered obligatory for follow-up imaging of patients with multiple sclerosis (MS). However, advances in MRI have substantially improved the sensitivity for detecting new or enlarged lesions in MS. Purpose To investigate whether the use of contrast material has an effect on the detection of new or enlarged MS lesions and, consequently, the assessment of interval progression. Materials and Methods In this retrospective study based on a local prospective observational cohort, 507 follow-up MR images obtained in 359 patients with MS (mean age, 38.2 years ± 10.3; 246 women, 113 men) were evaluated. With use of subtraction maps, nonenhanced images (double inversion recovery [DIR], fluid-attenuated inversion recovery [FLAIR]) and contrast material-enhanced (gadoterate meglumine, 0.1 mmol/kg) T1-weighted images were separately assessed for new or enlarged lesions in independent readings by two readers blinded to each other's findings and to clinical information. Primary outcome was the percentage of new or enlarged lesions detected only on contrast-enhanced T1-weighted images and the assessment of interval progression. Interval progression was defined as at least one new or unequivocally enlarged lesion on follow-up MR images. Results Of 507 follow-up images, 264 showed interval progression, with a total of 1992 new or enlarged and 207 contrast-enhancing lesions. Four of these lesions (on three MR images) were retrospectively detected on only the nonenhanced images, corresponding to 1.9% (four of 207) of the enhancing and 0.2% (four of 1992) of all new or enlarged lesions. Nine enhancing lesions were not detected on FLAIR-based subtraction maps (nine of 1442, 0.6%). In none of the 507 images did the contrast-enhanced sequences reveal interval progression that was missed in the readouts of the nonenhanced sequences, with use of either DIR- or FLAIR-based subtraction maps. Interrater agreement was high for all three measures, with intraclass correlation coefficients of 0.91 with FLAIR, 0.94 with DIR, and 0.99 with contrast-enhanced T1-weighted imaging. Conclusion At 3.0 T, use of a gadolinium-based contrast agent at follow-up MRI did not change the diagnosis of interval disease progression in patients with multiple sclerosis. © RSNA, 2019 See also the editorial by Saindane in this issue.
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Affiliation(s)
- Paul Eichinger
- From the Department of Diagnostic and Interventional Neuroradiology (P.E., S.S., H.Z., J.K., C.Z., B.W.), Department of Neurology (V.P., M.B., M.M.H., A.B., B.H., M.M.), and TUM-NIC, NeuroImaging Center (V.P., M.B., M.M.), Klinikum rechts der Isar, Technische Universität München, Ismaninger Strasse 22, 81675 Munich, Germany; Department of Psychiatry and Psychotherapy, Isar-Amper-Klinikum München-Ost, Haar, Germany (H.W.); and Munich Cluster for Systems Neurology (SyNergy), Munich, Germany (B.H.)
| | - Simon Schön
- From the Department of Diagnostic and Interventional Neuroradiology (P.E., S.S., H.Z., J.K., C.Z., B.W.), Department of Neurology (V.P., M.B., M.M.H., A.B., B.H., M.M.), and TUM-NIC, NeuroImaging Center (V.P., M.B., M.M.), Klinikum rechts der Isar, Technische Universität München, Ismaninger Strasse 22, 81675 Munich, Germany; Department of Psychiatry and Psychotherapy, Isar-Amper-Klinikum München-Ost, Haar, Germany (H.W.); and Munich Cluster for Systems Neurology (SyNergy), Munich, Germany (B.H.)
| | - Viola Pongratz
- From the Department of Diagnostic and Interventional Neuroradiology (P.E., S.S., H.Z., J.K., C.Z., B.W.), Department of Neurology (V.P., M.B., M.M.H., A.B., B.H., M.M.), and TUM-NIC, NeuroImaging Center (V.P., M.B., M.M.), Klinikum rechts der Isar, Technische Universität München, Ismaninger Strasse 22, 81675 Munich, Germany; Department of Psychiatry and Psychotherapy, Isar-Amper-Klinikum München-Ost, Haar, Germany (H.W.); and Munich Cluster for Systems Neurology (SyNergy), Munich, Germany (B.H.)
| | - Hanni Wiestler
- From the Department of Diagnostic and Interventional Neuroradiology (P.E., S.S., H.Z., J.K., C.Z., B.W.), Department of Neurology (V.P., M.B., M.M.H., A.B., B.H., M.M.), and TUM-NIC, NeuroImaging Center (V.P., M.B., M.M.), Klinikum rechts der Isar, Technische Universität München, Ismaninger Strasse 22, 81675 Munich, Germany; Department of Psychiatry and Psychotherapy, Isar-Amper-Klinikum München-Ost, Haar, Germany (H.W.); and Munich Cluster for Systems Neurology (SyNergy), Munich, Germany (B.H.)
| | - Haike Zhang
- From the Department of Diagnostic and Interventional Neuroradiology (P.E., S.S., H.Z., J.K., C.Z., B.W.), Department of Neurology (V.P., M.B., M.M.H., A.B., B.H., M.M.), and TUM-NIC, NeuroImaging Center (V.P., M.B., M.M.), Klinikum rechts der Isar, Technische Universität München, Ismaninger Strasse 22, 81675 Munich, Germany; Department of Psychiatry and Psychotherapy, Isar-Amper-Klinikum München-Ost, Haar, Germany (H.W.); and Munich Cluster for Systems Neurology (SyNergy), Munich, Germany (B.H.)
| | - Matthias Bussas
- From the Department of Diagnostic and Interventional Neuroradiology (P.E., S.S., H.Z., J.K., C.Z., B.W.), Department of Neurology (V.P., M.B., M.M.H., A.B., B.H., M.M.), and TUM-NIC, NeuroImaging Center (V.P., M.B., M.M.), Klinikum rechts der Isar, Technische Universität München, Ismaninger Strasse 22, 81675 Munich, Germany; Department of Psychiatry and Psychotherapy, Isar-Amper-Klinikum München-Ost, Haar, Germany (H.W.); and Munich Cluster for Systems Neurology (SyNergy), Munich, Germany (B.H.)
| | - Muna-Miriam Hoshi
- From the Department of Diagnostic and Interventional Neuroradiology (P.E., S.S., H.Z., J.K., C.Z., B.W.), Department of Neurology (V.P., M.B., M.M.H., A.B., B.H., M.M.), and TUM-NIC, NeuroImaging Center (V.P., M.B., M.M.), Klinikum rechts der Isar, Technische Universität München, Ismaninger Strasse 22, 81675 Munich, Germany; Department of Psychiatry and Psychotherapy, Isar-Amper-Klinikum München-Ost, Haar, Germany (H.W.); and Munich Cluster for Systems Neurology (SyNergy), Munich, Germany (B.H.)
| | - Jan Kirschke
- From the Department of Diagnostic and Interventional Neuroradiology (P.E., S.S., H.Z., J.K., C.Z., B.W.), Department of Neurology (V.P., M.B., M.M.H., A.B., B.H., M.M.), and TUM-NIC, NeuroImaging Center (V.P., M.B., M.M.), Klinikum rechts der Isar, Technische Universität München, Ismaninger Strasse 22, 81675 Munich, Germany; Department of Psychiatry and Psychotherapy, Isar-Amper-Klinikum München-Ost, Haar, Germany (H.W.); and Munich Cluster for Systems Neurology (SyNergy), Munich, Germany (B.H.)
| | - Achim Berthele
- From the Department of Diagnostic and Interventional Neuroradiology (P.E., S.S., H.Z., J.K., C.Z., B.W.), Department of Neurology (V.P., M.B., M.M.H., A.B., B.H., M.M.), and TUM-NIC, NeuroImaging Center (V.P., M.B., M.M.), Klinikum rechts der Isar, Technische Universität München, Ismaninger Strasse 22, 81675 Munich, Germany; Department of Psychiatry and Psychotherapy, Isar-Amper-Klinikum München-Ost, Haar, Germany (H.W.); and Munich Cluster for Systems Neurology (SyNergy), Munich, Germany (B.H.)
| | - Claus Zimmer
- From the Department of Diagnostic and Interventional Neuroradiology (P.E., S.S., H.Z., J.K., C.Z., B.W.), Department of Neurology (V.P., M.B., M.M.H., A.B., B.H., M.M.), and TUM-NIC, NeuroImaging Center (V.P., M.B., M.M.), Klinikum rechts der Isar, Technische Universität München, Ismaninger Strasse 22, 81675 Munich, Germany; Department of Psychiatry and Psychotherapy, Isar-Amper-Klinikum München-Ost, Haar, Germany (H.W.); and Munich Cluster for Systems Neurology (SyNergy), Munich, Germany (B.H.)
| | - Bernhard Hemmer
- From the Department of Diagnostic and Interventional Neuroradiology (P.E., S.S., H.Z., J.K., C.Z., B.W.), Department of Neurology (V.P., M.B., M.M.H., A.B., B.H., M.M.), and TUM-NIC, NeuroImaging Center (V.P., M.B., M.M.), Klinikum rechts der Isar, Technische Universität München, Ismaninger Strasse 22, 81675 Munich, Germany; Department of Psychiatry and Psychotherapy, Isar-Amper-Klinikum München-Ost, Haar, Germany (H.W.); and Munich Cluster for Systems Neurology (SyNergy), Munich, Germany (B.H.)
| | - Mark Mühlau
- From the Department of Diagnostic and Interventional Neuroradiology (P.E., S.S., H.Z., J.K., C.Z., B.W.), Department of Neurology (V.P., M.B., M.M.H., A.B., B.H., M.M.), and TUM-NIC, NeuroImaging Center (V.P., M.B., M.M.), Klinikum rechts der Isar, Technische Universität München, Ismaninger Strasse 22, 81675 Munich, Germany; Department of Psychiatry and Psychotherapy, Isar-Amper-Klinikum München-Ost, Haar, Germany (H.W.); and Munich Cluster for Systems Neurology (SyNergy), Munich, Germany (B.H.)
| | - Benedikt Wiestler
- From the Department of Diagnostic and Interventional Neuroradiology (P.E., S.S., H.Z., J.K., C.Z., B.W.), Department of Neurology (V.P., M.B., M.M.H., A.B., B.H., M.M.), and TUM-NIC, NeuroImaging Center (V.P., M.B., M.M.), Klinikum rechts der Isar, Technische Universität München, Ismaninger Strasse 22, 81675 Munich, Germany; Department of Psychiatry and Psychotherapy, Isar-Amper-Klinikum München-Ost, Haar, Germany (H.W.); and Munich Cluster for Systems Neurology (SyNergy), Munich, Germany (B.H.)
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12
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Mattay RR, Davtyan K, Bilello M, Mamourian AC. Do All Patients with Multiple Sclerosis Benefit from the Use of Contrast on Serial Follow-Up MR Imaging? A Retrospective Analysis. AJNR Am J Neuroradiol 2018; 39:2001-2006. [PMID: 30287455 DOI: 10.3174/ajnr.a5828] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2018] [Accepted: 07/26/2018] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE Patients with multiple sclerosis routinely have MR imaging with contrast every 6-12 months to assess response to medication. Multiple recent studies provide evidence of tissue deposition of MR imaging contrast agents, questioning the long-term safety of these agents. The goal of this retrospective image-analysis study was to determine whether contrast could be reserved for only those patients who show new MS lesions on follow-up examinations. MATERIALS AND METHODS We retrospectively reviewed brain MRIs of 138 patients. To increase our sensitivity, we used a previously described computerized image-comparison software to evaluate the stability or progression of multiple sclerosis white matter lesions in noncontrast FLAIR sequences. We correlated these findings with evidence of contrast-enhancing lesions on the enhanced T1 sequence from the same scan. RESULTS Thirty-three scans showed an increase in white matter lesion burden. Among those 33 patients, 14 examinations also demonstrated enhancing new lesions. While we found a single example of enhancement of a pre-existing white matter lesion that appeared unchanged in size, that same examination showed an overall increase in lesion burden with enhancement of other, new lesions. Thus, we found that all patients with enhancing lesions had evidence of progression on their noncontrast imaging. CONCLUSIONS Because all enhancing lesions were associated with new lesions on unenhanced imaging and progression was only evident in 24% of patients, in patients with relapsing-remitting MS, it is reasonable to consider reserving contrast for only those patients with evidence of progression on noncontrast MR images.
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Affiliation(s)
- R R Mattay
- From the Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania.
| | - K Davtyan
- From the Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania
| | - M Bilello
- From the Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania
| | - A C Mamourian
- From the Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania
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13
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Mukherjee S, Cheng I, Miller S, Guo T, Chau V, Basu A. A fast segmentation-free fully automated approach to white matter injury detection in preterm infants. Med Biol Eng Comput 2018; 57:71-87. [PMID: 29981051 DOI: 10.1007/s11517-018-1829-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2017] [Accepted: 04/04/2018] [Indexed: 11/30/2022]
Abstract
White matter injury (WMI) is the most prevalent brain injury in the preterm neonate leading to developmental deficits. However, detecting WMI in magnetic resonance (MR) images of preterm neonate brains using traditional WM segmentation-based methods is difficult mainly due to lack of reliable preterm neonate brain atlases to guide segmentation. Hence, we propose a segmentation-free, fast, unsupervised, atlas-free WMI detection method. We detect the ventricles as blobs using a fast linear maximally stable extremal regions algorithm. A reference contour equidistant from the blobs and the brain-background boundary is used to identify tissue adjacent to the blobs. Assuming normal distribution of the gray-value intensity of this tissue, the outlier intensities in the entire brain region are identified as potential WMI candidates. Thereafter, false positives are discriminated using appropriate heuristics. Experiments using an expert-annotated dataset show that the proposed method runs 20 times faster than our earlier work which relied on time-consuming segmentation of the WM region, without compromising WMI detection accuracy. Graphical Abstract Key Steps of Segmentation-free WMI Detection.
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Affiliation(s)
- Subhayan Mukherjee
- Department of Computing Science, University of Alberta, 402 Athabasca Hall, Edmonton, Alberta, T6G 2H1, Canada
| | - Irene Cheng
- Department of Computing Science, University of Alberta, 402 Athabasca Hall, Edmonton, Alberta, T6G 2H1, Canada
| | - Steven Miller
- The Hospital for Sick Children and the University of Toronto, Toronto, Ontario, Canada
| | - Ting Guo
- The Hospital for Sick Children and the University of Toronto, Toronto, Ontario, Canada
| | - Vann Chau
- The Hospital for Sick Children and the University of Toronto, Toronto, Ontario, Canada
| | - Anup Basu
- Department of Computing Science, University of Alberta, 402 Athabasca Hall, Edmonton, Alberta, T6G 2H1, Canada.
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14
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A comparison of computer-assisted detection (CAD) programs for the identification of colorectal polyps: performance and sensitivity analysis, current limitations and practical tips for radiologists. Clin Radiol 2018; 73:593.e11-593.e18. [PMID: 29602538 DOI: 10.1016/j.crad.2018.02.009] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2017] [Accepted: 02/13/2018] [Indexed: 01/27/2023]
Abstract
AIM To directly compare the accuracy and speed of analysis of two commercially available computer-assisted detection (CAD) programs in detecting colorectal polyps. MATERIALS AND METHOD In this retrospective single-centre study, patients who had colorectal polyps identified on computed tomography colonography (CTC) and subsequent lower gastrointestinal endoscopy, were analysed using two commercially available CAD programs (CAD1 and CAD2). Results were compared against endoscopy to ascertain sensitivity and positive predictive value (PPV) for colorectal polyps. Time taken for CAD analysis was also calculated. RESULTS CAD1 demonstrated a sensitivity of 89.8%, PPV of 17.6% and mean analysis time of 125.8 seconds. CAD2 demonstrated a sensitivity of 75.5%, PPV of 44.0% and mean analysis time of 84.6 seconds. CONCLUSION The sensitivity and PPV for colorectal polyps and CAD analysis times can vary widely between current commercially available CAD programs. There is still room for improvement. Generally, there is a trade-off between sensitivity and PPV, and so further developments should aim to optimise both. Information on these factors should be made routinely available, so that an informed choice on their use can be made. This information could also potentially influence the radiologist's use of CAD results.
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15
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Guo D, Fridriksson J, Fillmore P, Rorden C, Yu H, Zheng K, Wang S. Automated lesion detection on MRI scans using combined unsupervised and supervised methods. BMC Med Imaging 2015; 15:50. [PMID: 26518734 PMCID: PMC4628334 DOI: 10.1186/s12880-015-0092-x] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2015] [Accepted: 10/16/2015] [Indexed: 11/30/2022] Open
Abstract
BACKGROUND Accurate and precise detection of brain lesions on MR images (MRI) is paramount for accurately relating lesion location to impaired behavior. In this paper, we present a novel method to automatically detect brain lesions from a T1-weighted 3D MRI. The proposed method combines the advantages of both unsupervised and supervised methods. METHODS First, unsupervised methods perform a unified segmentation normalization to warp images from the native space into a standard space and to generate probability maps for different tissue types, e.g., gray matter, white matter and fluid. This allows us to construct an initial lesion probability map by comparing the normalized MRI to healthy control subjects. Then, we perform non-rigid and reversible atlas-based registration to refine the probability maps of gray matter, white matter, external CSF, ventricle, and lesions. These probability maps are combined with the normalized MRI to construct three types of features, with which we use supervised methods to train three support vector machine (SVM) classifiers for a combined classifier. Finally, the combined classifier is used to accomplish lesion detection. RESULTS We tested this method using T1-weighted MRIs from 60 in-house stroke patients. Using leave-one-out cross validation, the proposed method can achieve an average Dice coefficient of 73.1% when compared to lesion maps hand-delineated by trained neurologists. Furthermore, we tested the proposed method on the T1-weighted MRIs in the MICCAI BRATS 2012 dataset. The proposed method can achieve an average Dice coefficient of 66.5% in comparison to the expert annotated tumor maps provided in MICCAI BRATS 2012 dataset. In addition, on these two test datasets, the proposed method shows competitive performance to three state-of-the-art methods, including Stamatakis et al., Seghier et al., and Sanjuan et al. CONCLUSIONS In this paper, we introduced a novel automated procedure for lesion detection from T1-weighted MRIs by combining both an unsupervised and a supervised component. In the unsupervised component, we proposed a method to identify lesioned hemisphere to help normalize the patient MRI with lesions and initialize/refine a lesion probability map. In the supervised component, we extracted three different-order statistical features from both the tissue/lesion probability maps obtained from the unsupervised component and the original MRI intensity. Three support vector machine classifiers are then trained for the three features respectively and combined for final voxel-based lesion classification.
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Affiliation(s)
- Dazhou Guo
- Department of Computer Science & Engineering, University of South Carolina, 301 Main Street, Columbia, 29201, USA.
| | - Julius Fridriksson
- Department of Communication Science & Disorders, University of South Carolina, 915 Greene Street, Columbia, 29208, USA.
| | - Paul Fillmore
- Department of Communication Science & Disorders, University of South Carolina, 915 Greene Street, Columbia, 29208, USA.
| | - Christopher Rorden
- Department of Communication Science & Disorders, University of South Carolina, 915 Greene Street, Columbia, 29208, USA.
| | - Hongkai Yu
- Department of Computer Science & Engineering, University of South Carolina, 301 Main Street, Columbia, 29201, USA.
| | - Kang Zheng
- Department of Computer Science & Engineering, University of South Carolina, 301 Main Street, Columbia, 29201, USA.
| | - Song Wang
- Department of Computer Science & Engineering, University of South Carolina, 301 Main Street, Columbia, 29201, USA.
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Incorporation of image data from a previous examination in 3D serial MR imaging. MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE 2015; 28:413-25. [DOI: 10.1007/s10334-014-0478-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2014] [Revised: 12/09/2014] [Accepted: 12/11/2014] [Indexed: 11/26/2022]
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