Evaluation of Deep Learning–Based Approaches to Segment Bowel Air Pockets and Generate Pelvic Attenuation Maps from CAIPIRINHA-Accelerated Dixon MR Images

• Published in: Journal of Nuclear Medicine Vol. 63; no. 3; pp. 468 - 475
• Main Authors: Sari, Hasan, Reaungamornrat, Ja, Catalano, Onofrio A., Vera-Olmos, Javier, Izquierdo-Garcia, David, Morales, Manuel A., Torrado-Carvajal, Angel, Ng, Thomas S.C., Malpica, Norberto, Kamen, Ali, Catana, Ciprian
• Format: Journal Article
• Language: English
• Published: United States Society of Nuclear Medicine 01.03.2022
• Subjects: Air pockets; Artificial neural networks; Attenuation; Clinical Investigation; Computed tomography; Deep Learning; Gastrointestinal tract; Humans; Image processing; Image Processing, Computer-Assisted - methods; Image reconstruction; Image segmentation; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Medical imaging; Neural networks; Pelvis; Pelvis - diagnostic imaging; Positron emission; Positron emission tomography; Positron-Emission Tomography - methods; Synthesis; Tomography; Tomography, X-Ray Computed; pseudo-CT; deep learning; PET/MRI; attenuation correction; PET quantification

Attenuation correction remains a challenge in pelvic PET/MRI. In addition to the segmentation/model-based approaches, deep learning methods have shown promise in synthesizing accurate pelvic attenuation maps (μ-maps). However, these methods often misclassify air pockets in the digestive tract, poten...