Dataset Information

FLAIR² post-processing: improving MS lesion detection in standard MS imaging protocols.

ABSTRACT:

Background

Technical improvements in magnetic resonance imaging (MRI) acquisition, such as higher field strength and optimized sequences, lead to better multiple sclerosis (MS) lesion detection and characterization. Multiplication of 3D-FLAIR with 3D-T2 sequences (FLAIR²) results in isovoxel images with increased contrast-to-noise ratio, increased white-gray-matter contrast, and improved MS lesion visualization without increasing MRI acquisition time. The current study aims to assess the potential of 3D-FLAIR² in detecting cortical/leucocortical (LC), juxtacortical (JC), and white matter (WM) lesions.

Objective

To compare lesion detection of 3D-FLAIR² with state-of-the-art 3D-T2-FLAIR and 3D-T2-weighted images.

Methods

We retrospectively analyzed MRI scans of thirteen MS patients, showing previously noted high cortical lesion load. Scans were acquired using a 3 T MRI scanner. WM, JC, and LC lesions were manually labeled and manually counted after randomization of 3D-T2, 3D-FLAIR, and 3D-FLAIR² scans using the ITK-SNAP tool.

Results

LC lesion visibility was significantly improved by 3D-FLAIR² in comparison to 3D-FLAIR (4 vs 1; p = 0.018) and 3D-T2 (4 vs 1; p = 0.007). Comparing LC lesion detection in 3D-FLAIR² vs. 3D-FLAIR, 3D-FLAIR² detected on average 3.2 more cortical lesions (95% CI - 9.1 to 2.8). Comparing against 3D-T2, 3D-FLAIR² detected on average 3.7 more LC lesions (95% CI 3.3-10.7).

Conclusions

3D-FLAIR² is an easily applicable time-sparing MR post-processing method to improve cortical lesion detection. Larger sampled studies are warranted to validate the sensitivity and specificity of 3D-FLAIR².

SUBMITTER: Zrzavy T

PROVIDER: S-EPMC8738502 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundThe introduction of hybrid SPECT/CT devices enables quantitative imaging in SPECT, providing a methodological setup for quantitation using SPECT tracers comparable to PET/CT. We evaluated a specific quantitative reconstruction algorithm for SPECT data using a 99mTc-filled NEMA phantom. Quantitative and qualitative image parameters were evaluated for different parametrizations of the acquisition and reconstruction protocol to identify an optimized quantitative protocol.ResultsThe reconstructed activity concentration (ACrec) and the signal-to-noise ratio (SNR) of all examined protocols (n = 16) were significantly affected by the parametrization of the weighting factor k used in scatter correction, the total number of iterations and the sphere volume (all, p < 0.0001). The two examined SPECT acquisition protocols (with 60 or 120 projections) had a minor impact on the ACrec and no significant impact on the SNR. In comparison to the known AC, the use of default scatter correction (k = 0.47) or object-specific scatter correction (k = 0.18) resulted in an underestimation of ACrec in the largest sphere volume (26.5 ml) by - 13.9 kBq/ml (- 16.3%) and - 7.1 kBq/ml (- 8.4%), respectively. An increase in total iterations leads to an increase in estimated AC and a decrease in SNR. The mean difference between ACrec and known AC decreased with an increasing number of total iterations (e.g., for 20 iterations (2 iterations/10 subsets) = - 14.6 kBq/ml (- 17.1%), 240 iterations (24i/10s) = - 8.0 kBq/ml (- 9.4%), p < 0.0001). In parallel, the mean SNR decreased significantly from 2i/10s to 24i/10s by 76% (p < 0.0001).ConclusionQuantitative SPECT imaging is feasible with the used reconstruction algorithm and hybrid SPECT/CT, and its consistent implementation in diagnostics may provide perspectives for quantification in routine clinical practice (e.g., assessment of bone metabolism). When combining quantitative analysis and diagnostic imaging, we recommend using two different reconstruction protocols with task-specific optimized setups (quantitative vs. qualitative reconstruction). Furthermore, individual scatter correction significantly improves both quantitative and qualitative results.

Dataset Information

FLAIR² post-processing: improving MS lesion detection in standard MS imaging protocols.

Background

Objective

Methods

Results

Conclusions

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

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Dataset Information

FLAIR2 post-processing: improving MS lesion detection in standard MS imaging protocols.

Background

Objective

Methods

Results

Conclusions

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

FLAIR² post-processing: improving MS lesion detection in standard MS imaging protocols.