Erik L. Ridley[email protected]HomeWeek in review: Minnies semifinalists | MRI body composition analysis | ISCT 2023 coverageSeptember 8, 2023HomeWeek in review: MRI prostate cancer screening | Staff safety during Y-90 radioembolization | CT AI modelAugust 25, 2023Breast ImagingWeek in review: DBT in breast cancer survivors | Can ChatGPT improve diagnosis? | Tau-PETAugust 18, 2023Emergency RadiologyWeek in review: Damar Hamlin and emergency radiology | Beast care guidance book | Minnies nominations deadlineAugust 11, 2023HomeWeek in review: A setback for CT colonography | Mammography AI news | AI for lung nodulesAugust 4, 2023Image ProcessingRadiomics algorithm finds key biomarker in multiple myelomaIn patients with multiple myeloma, an MRI radiomics machine-learning algorithm can identify minimal residual disease status, a biomarker linked with longer survival, according to a new study published in Clinical Radiology.August 3, 2023MRIVirtual reality enhances planning of complex surgeriesVirtual-reality models based on preoperative imaging studies can improve planning and surgeon confidence in complex surgical oncology procedures, according to research published August 2 in the Journal of Surgical Research.August 3, 2023HomeWeek in review: Amy Patel on breast imaging advocacy | Body composition analysis | SalaryScan deadlineJuly 28, 2023Image ProcessingCan AI body composition analysis predict mortality risk on LDCT?An AI algorithm can opportunistically predict patient mortality risk by analyzing body composition on baseline low-dose CT lung cancer screening exams, according to research published July 25 in Radiology.July 25, 2023Image ProcessingAuntMinnie.com Advanced Visualization InsiderJuly 24, 2023Page 1 of 388Next PageTop StoriesMRICNN techniques outperform conventional DL for Alzheimer's diagnosisConvolutional neural network (CNN) techniques outperform conventional deep learning (DL) methods in diagnosing Alzheimer's.Womens ImagingAbout half of women undecided on AI's use in breast imagingISMRMISMRM president outlines key highlights of the upcoming annual meeting in HonoluluMolecular ImagingSPECT/CT MPI may guide medical therapy in CAD patientsMRIAI model uses MRI data to predict brain cancer patient survival
![Images depict axial MRI scans in a 50-year-old premenopausal female patient with biopsy-proven 12-mm invasive lobular cancer in the lower outer quadrant of the left breast. Upper images are subtracted dynamic contrast-enhanced (DCE) MRI scans from the first and second postcontrast acquisitions. Lower images are ultrafast MRI scans obtained within 24 hours after the DCE study (four consecutive subtracted dynamic frames from time points [T] 6–9). There is marked background parenchymal enhancement (BPE) on the DCE MRI scans. Thus, the known cancer (arrow) is barely visible; it exhibits only slightly stronger enhancement than the normal fibroglandular tissue, which results in low conspicuity. At ultrafast MRI there is less BPE, and still the cancer is barely visible because it starts to enhance simultaneously with the BPE. The conspicuity of the cancer is even reduced compared with the DCE series. Both of the readers missed this cancer on both the DCE and ultrafast MRI scans.](https://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2025/05/2025-05-06-rsna-ultrafast-dce-mri-breast.Gzvq76udFr.jpg?auto=format%2Ccompress&dpr=2&fit=crop&h=167&q=70&w=250)
