Dataset Information

A Rotational Cylindrical fMRI Phantom for Image Quality Control.

ABSTRACT:

Purpose

A novel phantom for image quality testing for functional magnetic resonance imaging (fMRI) scans is described.

Methods

The cylindrical, rotatable, ~4.5L phantom, with eight wedge-shaped compartments, is used to simulate rest and activated states. The compartments contain NiCl2 doped agar gel with alternating concentrations of agar (1.4%, 1.6%) to produce T1 and T2 values approximating brain grey matter. The Jacard index was used to compare the image distortions for echo planar imaging (EPI) and gradient recalled echo (GRE) scans. Contrast to noise ratio (CNR) was compared across the imaging volume for GRE and EPI.

Results

The mean T2 for the two agar concentrations were found to be 106.5±4.8, 94.5±4.7 ms, and T1 of 1500±40 and 1485±30 ms, respectively. The Jacard index for GRE was generally found to be higher than for EPI (0.95 versus 0.8). The CNR varied from 20 to 50 across the slices and echo times used for EPI scans, and from 20 to 40 across the slices for the GRE scans. The phantom provided a reproducible CNR over 25 days.

Conclusions

The phantom provides a quantifiable signal change over a head-size imaging volume with EPI and GRE sequences, which was used for image quality assessment.

SUBMITTER: Tovar DA

PROVIDER: S-EPMC4666484 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A Rotational Cylindrical fMRI Phantom for Image Quality Control.

Tovar David A DA Zhan Wang W Rajan Sunder S SS

PloS one 20151201 12

<h4>Purpose</h4>A novel phantom for image quality testing for functional magnetic resonance imaging (fMRI) scans is described.<h4>Methods</h4>The cylindrical, rotatable, ~4.5L phantom, with eight wedge-shaped compartments, is used to simulate rest and activated states. The compartments contain NiCl2 doped agar gel with alternating concentrations of agar (1.4%, 1.6%) to produce T1 and T2 values approximating brain grey matter. The Jacard index was used to compare the image distortions for echo pl ...[more]

PMID: 26625264

Similar Datasets

Project description:PurposeInvestigate the impact of acquisition time and reconstruction parameters on single-photon emission computed tomography/computed tomography (SPECT/CT) image quality with the ultimate aim of finding the shortest possible acquisition time for clinical whole-body SPECT/CT (WB-SPECT/CT) while maintaining image quality METHODS: The National Electrical Manufacturers Association (NEMA) image quality measurements were performed on a SPECT/CT imaging system using a NEMA International Electrotechnical Commission (IEC) phantom with spherical inserts of varying diameter (10-37 mm), filled with 99m Tc in activity sphere-to-background concentration ratio of 8.5:1. A gated acquisition was acquired and binned data were summed to simulate acquisitions of 15, 8, and 3 s per projection angle. Images were reconstructed on a Hermes (HERMES Medical Solutions AB, Stockholm, Sweden) workstation using eight subsets and between 4 and 24 iterations of the three-dimensional (3D) ordered subset expectation maximization (OSEM) algorithm. Reconstructed images were post-smoothed with 3D Gaussian filter ranging from 0 to 12 mm full-width at half maximum (FWHM). Contrast recovery, background variability, and contrast-to-noise ratio were evaluated RESULTS: As expected, the spheres were more clearly defined as acquisition time and count statistics improved. The optimal iteration number and Gaussian filter were determined from the contrast recovery convergence and level of noise. Convergence of contrast recovery was observed at eight iterations while 12 iterations yielded stabilized values at all acquisition times. In addition, it was observed that applying 3D Gaussian filter of 8-12 mm FWHM suppressed the noise and mitigated Gibbs artifacts. Background variability was larger for small spheres than larger spheres and the noise decreased when acquisition time became longer. A contrast-to-noise ratio >5 was reached for the two smallest spheres of 10 and 13 mm at acquisition times of 8 s CONCLUSION: Optimized reconstruction parameters preserved image quality with reduce acquisition time in present study. This study suggests an optimal protocol for clinical 99m Tc SPECT/CT can be reached at 8 s per projection angle, with data reconstructed using 12 iterations and eight subset of the 3D OSEM algorithm and 8 mm Gaussian post-filter.

Project description:PurposeRadiomics utilizes a large number of image-derived features for quantifying tumor characteristics that can in turn be correlated with response and prognosis. Unfortunately, extraction and analysis of such image-based features is subject to measurement variability and bias. The challenge for radiomics is particularly acute in Positron Emission Tomography (PET) where limited resolution, a high noise component related to the limited stochastic nature of the raw data, and the wide variety of reconstruction options confound quantitative feature metrics. Extracted feature quality is also affected by tumor segmentation methods used to define regions over which to calculate features, making it challenging to produce consistent radiomics analysis results across multiple institutions that use different segmentation algorithms in their PET image analysis. Understanding each element contributing to these inconsistencies in quantitative image feature and metric generation is paramount for ultimate utilization of these methods in multi-institutional trials and clinical oncology decision making.MethodsTo assess segmentation quality and consistency at the multi-institutional level, we conducted a study of seven institutional members of the National Cancer Institute Quantitative Imaging Network. For the study, members were asked to segment a common set of phantom PET scans acquired over a range of imaging conditions as well as a second set of head and neck cancer (HNC) PET scans. Segmentations were generated at each institution using their preferred approach. In addition, participants were asked to repeat segmentations with a time interval between initial and repeat segmentation. This procedure resulted in overall 806 phantom insert and 641 lesion segmentations. Subsequently, the volume was computed from the segmentations and compared to the corresponding reference volume by means of statistical analysis.ResultsOn the two test sets (phantom and HNC PET scans), the performance of the seven segmentation approaches was as follows. On the phantom test set, the mean relative volume errors ranged from 29.9 to 87.8% of the ground truth reference volumes, and the repeat difference for each institution ranged between -36.4 to 39.9%. On the HNC test set, the mean relative volume error ranged between -50.5 to 701.5%, and the repeat difference for each institution ranged between -37.7 to 31.5%. In addition, performance measures per phantom insert/lesion size categories are given in the paper. On phantom data, regression analysis resulted in coefficient of variation (CV) components of 42.5% for scanners, 26.8% for institutional approaches, 21.1% for repeated segmentations, 14.3% for relative contrasts, 5.3% for count statistics (acquisition times), and 0.0% for repeated scans. Analysis showed that the CV components for approaches and repeated segmentations were significantly larger on the HNC test set with increases by 112.7% and 102.4%, respectively.ConclusionAnalysis results underline the importance of PET scanner reconstruction harmonization and imaging protocol standardization for quantification of lesion volumes. In addition, to enable a distributed multi-site analysis of FDG PET images, harmonization of analysis approaches and operator training in combination with highly automated segmentation methods seems to be advisable. Future work will focus on quantifying the impact of segmentation variation on radiomics system performance.

Dataset Information

A Rotational Cylindrical fMRI Phantom for Image Quality Control.

Purpose

Methods

Results

Conclusions

Publications

A Rotational Cylindrical fMRI Phantom for Image Quality Control.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets