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Functional stability of clinical research neurological scanners
Antonio Napolitano1, Martina Lucignani1, Francesca Bottino1, Anna Nigri2, Domenico Aquino2, Fulvia Palesi3, Maria Grazia Bruzzone2, Michela Tosetti4,5, Claudia A. M. Gandini Wheeler-Kingshott6,7,8, and the Italian IRCCS advanced neuroimaging network9

1Medical Physics Department, Bambino Gesù Children's Hospital, Rome, Italy, 2Neuroradiology Department, Carlo Besta Neurological Institute, Milan, Italy, 3Brain MRI 3T Center, Mondino National Neurological Institute, Pavia, Italy, 4IRCCS Stella Maris Foundation, Pisa, Italy, 5Imago7 Foundation, Pisa, Italy, 6Brain MRI 3T Research Center, Mondino National Neurological Institute, Pavia, Italy, 7Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy, 8Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom, 9Advanced Neuroimaging Network of Italian Research Neurological Institutes (IRCCS), Italy, Italy

Synopsis

A multicenter network of Italian Neurological Research Hospital was recently started. Its first aim was to establish quality assessment protocols for future quantitative imaging studies. In this context, we report the outcome of the first 6 months of data collection using a dedicated phantom and processing pipeline. Scanner performance was assessed in terms of several stability indicators, grouped by manufacturer. The preliminary results represent the initial step towards a large-scale scanner stability assessment in clinical settings. We expected to gather regular data from all the sites in order to broaden data analysis and assess the multi-site temporal stability.

INTRODUCTION.

An advanced Neuroimaging Network of Italian Research Neurological Institutes (IRCCS) was recently approved. It comprises 22 sites (20 sites with clinical scanners) with different scanner manufacturers (Philips, Siemens and GE). The first aim of the network was to establish quality assessment protocols for future quantitative imaging studies. In this context, monitoring temporal signal stability during fMRI acquisition was considered an important aspect for scanner performance assessment. The aim of the current work is to report the outcome of the first 6 months of data collection using a dedicated phantom and processing pipeline.

MATERIALS AND METHODS.

The study included 20 italian Research Neurological Institutes (IRCCS), equipped with different MR hardware (Table 1). The network decided to acquire the Functional Stability Reference (FUNSTAR) Phantom (Gold Standard Phantoms ltd., https://www.goldstandardphantoms.com) at each centres for its homogeneity characteristics (Fig 2). The FBIRN protocol1 for temporal stability was acquired over the past 6 months at 9 sites (4 Philips 3T, 3 Siemens 3T and 2 GE (1.5T and 3T)) with the following unified parameters : TR = 2000 msec, TE = 30 msec, FA = 90 degree, FOV = 220 mm, matrix = 64x64, slices = 32 interleaved with 4-mm thickness and 5-mm spacing, volumes = 200, readout direction = RL, phase-encoding direction = PA, bandwidth = 1595 Hz/px @ Siemens, 1857 Hz/px @ Philips, 7812 Hz/px @ GE. The FUNSTAR phantom is made of two semi-spheres with a plastic septum at the joining plane. The acquisition protocol was optimised by defining the position of the FUNSTAR phantom in the receiver coil that reduced artefacts from the plastic septum (Fig 2A). The analysis was performed by automatically placing Region of Interest (ROI) in the central section of the phantom center (Central ROI), at the periphery of the phantom (Peripheral ROIs) and in the background (Fig 2D). Since artefacts occurred in proximity of the central slice, we set the Central slice analysis 4 slices away from the center. The following parameters were computed: Signal-to-noise ratio (SNR), signal-to-fluctuation-noise ratio (SFNR), Static Spatial Noise (SSN), drift, presence of spikes, Weisskoff analysis and mean intensity fluctuation in Peripheral ROIs. All the QA analyses were automated and performed in Python 2.7.

RESULTS.

Data were grouped by manufacturer. Boxplots of SNR, SFNR and drift percentage from Central and Peripheral ROIs are displayed in Fig 3, while SSN and Weisskoff RDC are reported in Fig 4. Mean intensity fluctuation within Peripheral ROIs produces high variability values, ranging from 20% (GE) up to 90% (Siemens). None of the centres presented data with spikes.

DISCUSSION.

These preliminary results represent the initial step towards a large-scale scanner stability assessment in clinical settings. The high variability of the peripheral intensity fluctuation measurements could be related to the head coil used by each sites and by the lack of a standardised support structure for the phantom to reduce positioning differences.

CONCLUSION.

The data presented here are the initial outcome of a network of research hospitals in its early phase. We expected to gather regular data (monthly) from all the sites in order to broaden data analysis and assess the multi-site temporal stability. We aim to be able to define parameter values that will be useful to identify the minimum stability requirements necessary for good quality multi-centre data.

Acknowledgements

The Italian IRCCS advanced neuroimaging network is constituted by the following centers: IRCCS Istituto Auxologico Italiano (Milan); IRCCS Ospedale pediatrico Bambino Gesù (Rome); Fondazione IRCCS Istituto neurologico “Carlo Besta” (Milan); IRCCS Centro Neurolesi “Bonino Pulejo” (Messina); Centro IRCCS “Santa Maria nascente” - Don Gnocchi (Milan); IRCCS Centro San Giovanni di Dio – Fatebenefratelli (Brescia); IRCCS Ospedale pediatrico “Giannina Gaslini” (Genoa); IRCCS Istituto Clinico Humanitas (Milan); Istituto di Ricerche Farmacologiche “Mario Negri” IRCCS (Milan); Istituti Clinici Scientifici Maugeri, IRCCS (Pavia); IRCCS Eugenio Medea (Bosisio Parini); Fondazione IRCCS Istituto Neurologico “Casimiro Mondino” (Pavia); IRCCS NEUROMED – Istituto Neurologico Mediterraneo (Pozzilli); IRCCS Associazione Oasi Maria SS Onlus – Troina (Enna); Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico (Milan); IRCCS Fondazione Ospedale San Camillo (Venice); IRCCS Ospedale San Raffaele (Milan); IRCCS Fondazione Santa Lucia (Rome); IRCCS Istituto di Scienze Neurologiche (Bologna); IRCCS SDN Istituto di ricerca diagnostica e nucleare (Naples); IRCCS Fondazione Stella Maris (Pisa)

References

[1] Friedman, L., Glover, G.H. (2006). Report on a Multicenter fMRI Quality Assurance Protocol. Journal of Magnetic Resonance Imaging 23:827-839

Figures

Table 1: Description of Hardware from the 22 sites participating in this project (20 sites with clinical scanner). For each site we reported the field strength, the manufacturer and model of the scanner and the head coil type used.

Figure 2: FUNSTAR phantom. (A) Spherical agar-filled phantom was selected for QA assessment protocol. (B) Phantom was acquired in lying position to reduce artefacts related to the plastic septum that surround the center. (C) Phantom acquisition overview. (D) Positioning of ROIs in the center (blue) of FUNSTAR phantom, at its periphery (red) and in the background (green).

Figure 3: Boxplot of stability parameters grouped for manufacturer type (Philips, Siemens and GE respectively represented in orange, grey and yellow). The upper row represents SNR (A), SFNR (B) and drift percentage (C) computed for ROI in the center of the FUNSTAR phantom. The bottom row represents SNR (D), SFNR (E) and drift percentage (F) computed as the mean values from ROIs located at FUNSTAR periphery.

Figure 4: Boxplots of stability parameters grouped for manufacturer type (Philips, Siemens and GE respectively represented in orange, grey and yellow). (A) Mean intensity fluctuation computed as the mean values from peripheral ROIs;. (B) Static spatial noise computed as the difference between the sum of the odd images and the sum of the even images. (C) RDC from Weisskoff analysis evaluated for each vendor.

Proc. Intl. Soc. Mag. Reson. Med. 27 (2019)
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