Liver MR Imaging in Children: Current Concepts and Technique
Bibliographic record
Abstract
Magnetic resonance (MR) imaging is increasingly being used for comprehensive evaluation of liver diseases in children because of the lack of radiation and better lesion detection and characterization. Liver examination involves routine sequences such as T2-weighted, balanced steady-state free precession, and in-phase and out-of-phase sequences. Dynamic imaging is an essential component of liver examination to characterize focal lesions and involves capturing snapshots of the passage of contrast material in the arterial, portal venous, equilibrium, and sometimes hepatobiliary phases, generally by using T1-weighted three-dimensional gradient-echo sequences. Optimal arterial phase imaging is important for detection and characterization of hypervascular lesions. In the equilibrium phase, the concentration of contrast material is similar in the microvasculature and the extracellular interstitial space. Some superficial, spreading, inflammatory lesions are better seen on equilibrium phase images. Meticulous attention to intravenous access and use of an appropriate timing method are critical for successful dynamic imaging. Commonly used contrast media for liver imaging include gadolinium-based extracellular contrast agents and hepatobiliary contrast agents. A portion of hepatobiliary contrast agents such as gadoxetate and gadobenate is taken up by hepatocytes and excreted through bile. Hepatobiliary phase images acquired after hepatobiliary contrast agent administration are increasingly used to characterize liver lesions in children, such as focal nodular hyperplasia. Interpretation of liver MR images involves synthesis of information acquired from evaluation of background hepatic parenchyma, detection of lesions, and evaluation of signal intensity characteristics on images obtained with various sequences to arrive at a diagnosis or reasonable differential diagnoses. Understanding the appropriate technique, sequences, and contrast media when performing pediatric liver MR imaging is important for high diagnostic yield. (©)RSNA, 2016.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".