ArticleMagma (New York, N.Y.)2016
A novel alternative to classify tissues from T 1 and T 2 relaxation times for prostate MRI.
Article in Magma (New York, N.Y.), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- The value of synthetic magnetic resonance imaging in the diagnosis and assessment of prostate cancer aggressiveness.Quantitative imaging in medicine and surgery · 2024Article
- A review of PET attenuation correction methods for PET-MR.EJNMMI physics · 2023Review
- Pseudo-T2 mapping for normalization of T2-weighted prostate MRI.Magma (New York, N.Y.) · 2022Article
- The Preoperative Diagnostic Performance of Multi-Parametric Quantitative Assessment in Rectal Carcinoma: A Preliminary Study Using Synthetic Magnetic Resonance Imaging.Frontiers in oncology · 2022Article
- Synthetic MRI for Radiotherapy Planning for Brain and Prostate Cancers: Phantom Validation and Patient Evaluation.Frontiers in oncology · 2022Article
- Feasibility of Novel Three-Dimensional Magnetic Resonance Fingerprinting of the Prostate Gland: Phantom and Clinical Studies.Korean journal of radiology · 2021Article
- Automated reference tissue normalization of T2-weighted MR images of the prostate using object recognition.Magma (New York, N.Y.) · 2021Article
- The diagnostic performance of quantitative mapping in breast cancer patients: a preliminary study using synthetic MRI.Cancer imaging : the official publication of the International Cancer Imaging Society · 2020Article
- Fast and accurate compensation of signal offset for TMagma (New York, N.Y.) · 2019Article
- Automatic classification of tissues on pelvic MRI based on relaxation times and support vector machine.PloS one · 2019Article
- Effects of Echo Time on IVIM Quantification of the Normal Prostate.Scientific reports · 2018Article
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Authors and funding
5 authors.
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Abstract
objectiveTo segment and classify the different attenuation regions from MRI at the pelvis level using the T 1 and T 2 relaxation times and anatomical knowledge as a first step towards the creation of PET/MR attenuation maps. MATERIALS AND
methodsRelaxation times were calculated by fitting the pixel-wise intensities of acquired T 1- and T 2-weighted images from eight men with inversion-recovery and multi-echo multi-slice spin-echo sequences. A decision binary tree based on relaxation times was implemented to segment and classify fat, muscle, prostate, and air (within the body). Connected component analysis and an anatomical knowledge-based procedure were implemented to localize the background and bone.
resultsRelaxation times at 3 T are reported for fat (T 1 = 385 ms, T 2 = 121 ms), muscle (T 1 = 1295 ms, T 2 = 40 ms), and prostate (T 1 = 1700 ms, T 2 = 80 ms). The relaxation times allowed the segmentation-classification of fat, prostate, muscle, and air, and combined with anatomical knowledge, they allowed classification of bone. The good segmentation-classification of prostate [mean Dice similarity score (mDSC) = 0.70] suggests a viable implementation in oncology and that of fat (mDSC = 0.99), muscle (mDSC = 0.99), and bone (mDSCs = 0.78) advocates for its implementation in PET/MR attenuation correction.
conclusionOur method allows the segmentation and classification of the attenuation-relevant structures required for the generation of the attenuation map of PET/MR systems in prostate imaging: air, background, bone, fat, muscle, and prostate.
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