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Teves JB, Gonzalez-Castillo J, Holness M, Spurney M, Bandettini PA, Handwerker DA. The art and science of using quality control to understand and improve fMRI data. Front Neurosci 2023; 17:1100544. [PMID: 37090794 PMCID: PMC10117661 DOI: 10.3389/fnins.2023.1100544] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 03/17/2023] [Indexed: 04/08/2023] Open
Abstract
Designing and executing a good quality control (QC) process is vital to robust and reproducible science and is often taught through hands on training. As FMRI research trends toward studies with larger sample sizes and highly automated processing pipelines, the people who analyze data are often distinct from those who collect and preprocess the data. While there are good reasons for this trend, it also means that important information about how data were acquired, and their quality, may be missed by those working at later stages of these workflows. Similarly, an abundance of publicly available datasets, where people (not always correctly) assume others already validated data quality, makes it easier for trainees to advance in the field without learning how to identify problematic data. This manuscript is designed as an introduction for researchers who are already familiar with fMRI, but who did not get hands on QC training or who want to think more deeply about QC. This could be someone who has analyzed fMRI data but is planning to personally acquire data for the first time, or someone who regularly uses openly shared data and wants to learn how to better assess data quality. We describe why good QC processes are important, explain key priorities and steps for fMRI QC, and as part of the FMRI Open QC Project, we demonstrate some of these steps by using AFNI software and AFNI’s QC reports on an openly shared dataset. A good QC process is context dependent and should address whether data have the potential to answer a scientific question, whether any variation in the data has the potential to skew or hide key results, and whether any problems can potentially be addressed through changes in acquisition or data processing. Automated metrics are essential and can often highlight a possible problem, but human interpretation at every stage of a study is vital for understanding causes and potential solutions.
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Affiliation(s)
- Joshua B. Teves
- Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
| | - Javier Gonzalez-Castillo
- Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
| | - Micah Holness
- Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
| | - Megan Spurney
- Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
| | - Peter A. Bandettini
- Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
- Functional MRI Core Facility, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
| | - Daniel A. Handwerker
- Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
- *Correspondence: Daniel A. Handwerker,
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Kuang X, Cheung JPY, Wong KYK, Lam WY, Lam CH, Choy RW, Cheng CP, Wu H, Yang C, Wang K, Li Y, Zhang T. Spine-GFlow: A hybrid learning framework for robust multi-tissue segmentation in lumbar MRI without manual annotation. Comput Med Imaging Graph 2022; 99:102091. [PMID: 35803034 DOI: 10.1016/j.compmedimag.2022.102091] [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: 02/14/2022] [Revised: 05/30/2022] [Accepted: 06/13/2022] [Indexed: 10/18/2022]
Abstract
Most learning-based magnetic resonance image (MRI) segmentation methods rely on the manual annotation to provide supervision, which is extremely tedious, especially when multiple anatomical structures are required. In this work, we aim to develop a hybrid framework named Spine-GFlow that combines the image features learned by a CNN model and anatomical priors for multi-tissue segmentation in a sagittal lumbar MRI. Our framework does not require any manual annotation and is robust against image feature variation caused by different image settings and/or underlying pathology. Our contributions include: 1) a rule-based method that automatically generates the weak annotation (initial seed area), 2) a novel proposal generation method that integrates the multi-scale image features and anatomical prior, 3) a comprehensive loss for CNN training that optimizes the pixel classification and feature distribution simultaneously. Our Spine-GFlow has been validated on 2 independent datasets: HKDDC (containing images obtained from 3 different machines) and IVDM3Seg. The segmentation results of vertebral bodies (VB), intervertebral discs (IVD), and spinal canal (SC) are evaluated quantitatively using intersection over union (IoU) and the Dice coefficient. Results show that our method, without requiring manual annotation, has achieved a segmentation performance comparable to a model trained with full supervision (mean Dice 0.914 vs 0.916).
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Affiliation(s)
- Xihe Kuang
- Department of Orthopaedics and Traumatology, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, China
| | - Jason Pui Yin Cheung
- Department of Orthopaedics and Traumatology, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, China
| | - Kwan-Yee K Wong
- Department of Computer Science, Faculty of Engineering, University of Hong Kong, Hong Kong, China
| | - Wai Yi Lam
- Department of Orthopaedics and Traumatology, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, China
| | - Chak Hei Lam
- Department of Orthopaedics and Traumatology, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, China
| | - Richard W Choy
- Department of Orthopaedics and Traumatology, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, China
| | | | - Honghan Wu
- Department of Orthopaedics and Traumatology, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, China
| | - Cao Yang
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
| | - Kun Wang
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
| | - Yang Li
- School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China.
| | - Teng Zhang
- Department of Orthopaedics and Traumatology, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, China.
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Assessing radiomics feature stability with simulated CT acquisitions. Sci Rep 2022; 12:4732. [PMID: 35304508 PMCID: PMC8933485 DOI: 10.1038/s41598-022-08301-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2021] [Accepted: 03/03/2022] [Indexed: 11/29/2022] Open
Abstract
Medical imaging quantitative features had once disputable usefulness in clinical studies. Nowadays, advancements in analysis techniques, for instance through machine learning, have enabled quantitative features to be progressively useful in diagnosis and research. Tissue characterisation is improved via the “radiomics” features, whose extraction can be automated. Despite the advances, stability of quantitative features remains an important open problem. As features can be highly sensitive to variations of acquisition details, it is not trivial to quantify stability and efficiently select stable features. In this work, we develop and validate a Computed Tomography (CT) simulator environment based on the publicly available ASTRA toolbox (www.astra-toolbox.com). We show that the variability, stability and discriminative power of the radiomics features extracted from the virtual phantom images generated by the simulator are similar to those observed in a tandem phantom study. Additionally, we show that the variability is matched between a multi-center phantom study and simulated results. Consequently, we demonstrate that the simulator can be utilised to assess radiomics features’ stability and discriminative power.
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Nazarian B, Caron-Guyon J, Anton JL, Sein J, Baurberg J, Catz N, Kavounoudias A. A new patterned air-flow device to reveal the network for tactile motion coding using fMRI. J Neurosci Methods 2022; 365:109397. [PMID: 34695454 DOI: 10.1016/j.jneumeth.2021.109397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Revised: 10/04/2021] [Accepted: 10/19/2021] [Indexed: 11/25/2022]
Abstract
BACKGROUND Studying brain processes underlying tactile perception induced by natural-like stimulation is challenging yet crucial to closely match real-world situations. NEW METHOD We developed a computer-controlled pneumatic device that allows the delivery of complex airflow patterns on subject's body, through a MR-compatible system fixed on an independent clippable mounting device. The intensity of stimulation as well as the timing of each of the four air channels are completely programmable and independent, allowing the precise control and modularity of the airflow delivery. RESULTS An analysis of signal-to-noise ratio (SNR) measurements did not show any impact of the PAF device on anatomical or functional scan acquisitions. A psychophysical experiment was also performed on 24 volunteers to evaluate the perception of different airflow patterns delivered over the lower part of their face. It revealed that all participants were able to finely discriminate the direction of these leftward to rightward flow motions. The fMRI experiment, which consisted in presenting to 20 participants four different airflow patterns, shed light on the brain network associated with tactile motion perception. A multivariate analysis further showed a specific coding of the different patterns inside this tactile brain network including the primary and secondary somatosensory cortex COMPARISON WITH EXISTING METHOD(S): The Patterned Air-Flow (PAF) is an easy-to-set-up, portable, adaptable device, which can be spatially and temporally modulated to provide complex tactile stimuli. CONCLUSIONS This device will be useful to further explore complex dynamic touch exerted over various body parts and can also be combined with visual or auditory stimulation to study multisensory mechanisms.
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Affiliation(s)
- B Nazarian
- Aix Marseille Univ, CNRS, Centre IRM-INT@CERIMED (Institut des Neurosciences de la Timone - UMR 7289), Marseille, France
| | - J Caron-Guyon
- Aix Marseille Univ, CNRS, LNC (Laboratoire de Neurosciences Cognitives - UMR 7291), Marseille, France
| | - J L Anton
- Aix Marseille Univ, CNRS, Centre IRM-INT@CERIMED (Institut des Neurosciences de la Timone - UMR 7289), Marseille, France
| | - J Sein
- Aix Marseille Univ, CNRS, Centre IRM-INT@CERIMED (Institut des Neurosciences de la Timone - UMR 7289), Marseille, France
| | - J Baurberg
- Aix Marseille Univ, CNRS, Centre IRM-INT@CERIMED (Institut des Neurosciences de la Timone - UMR 7289), Marseille, France
| | - N Catz
- Aix Marseille Univ, CNRS, LNC (Laboratoire de Neurosciences Cognitives - UMR 7291), Marseille, France
| | - A Kavounoudias
- Aix Marseille Univ, CNRS, LNC (Laboratoire de Neurosciences Cognitives - UMR 7291), Marseille, France.
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