In acute-ischemic-stroke patients, penumbra assessment plays a significant role in treatment outcome. MR perfusion-weighted imaging (PWI) and diffusion-weighted imaging (DWI) mismatch ratio can provide penumbra assessment. Recently reported studies have shown the potential of susceptibility-weighted imaging (SWI) in the qualitative assessment of penumbra. We hypothesize that quantitative penumbra assessment using SWI-DWI can provide an alternative to the PWI-DWI approach and this can also reduce the overall scan-time. The purpose of the current study was to develop a framework for accurate quantitative assessment of penumbra using SWI-DWI and its validation with PWI-DWI-based quantification. In the current study, the arterial-spin-labelling (ASL) technique has been used for PWI. This retrospective study included 25 acute-ischemic-stroke patients presenting within 24 hours of the last noted baseline condition of stroke onset. Eleven patients also had follow-up MRI within 48 hours. MRI acquisition comprised DWI, SWI, pseudo-continuous-ASL (pCASL), FLAIR and non-contrast-angiography sequences. A framework was developed for the enhancement of prominent hypo-intense vein signs followed by automatic segmentation of the SWI penumbra ROI. Apparent-diffusion-coefficient (ADC) maps and cerebral-blood-flow (CBF) maps were computed. The infarct core ROI from the ADC map and the ASL penumbra ROI from CBF maps were segmented semiautomatically. The infarct core volume, SWI penumbra volume (SPV) and pCASL penumbra volume were computed and used to calculate mismatch ratios MR SWI ADC
Magnetic Resonance Imaging (MRI) offers strong soft tissue contrast but suffers from long acquisition times and requires tedious annotation from radiologists. Traditionally, these challenges have been addressed separately with reconstruction and image analysis algorithms. To see if performance could be improved by treating both as end-to-end, we hosted the K2S challenge, in which challenge participants segmented knee bones and cartilage from 8× undersampled k-space. We curated the 300-patient K2S dataset of multicoil raw k-space and radiologist quality-checked segmentations. 87 teams registered for the challenge and there were 12 submissions, varying in methodologies from serial reconstruction and segmentation to end-to-end networks to another that eschewed a reconstruction algorithm altogether. Four teams produced strong submissions, with the winner having a weighted Dice Similarity Coefficient of 0.910 ± 0.021 across knee bones and cartilage. Interestingly, there was no correlation between reconstruction and segmentation metrics. Further analysis showed the top four submissions were suitable for downstream biomarker analysis, largely preserving cartilage thicknesses and key bone shape features with respect to ground truth. K2S thus showed the value in considering reconstruction and image analysis as end-to-end tasks, as this leaves room for optimization while more realistically reflecting the long-term use case of tools being developed by the MR community.
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