Canonical text (the exact bytes fingerprinted)

TRUVACE RECORD VERSION
record: TRV-2026-1291
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-10-06T06:55:06.983312Z
status: published
lens: g_space
sector: health
headline: Slice-Wise Quality Assessment of High b Value Breast DWI via Deep Learning-Based Artifact Detection
dek: Diffusion-weighted imaging (DWI) can support lesion detection and characterization in breast MRI. Therefore, DWI is increasingly being incorporated into breast MRI protocols to address some of the shortcomings of routine clinical breast MRI. However, especially high b value DWI can be prone to intensity artifacts that can affect diagnostic image assessment. To detect hyper- and hypointense artifacts on high b value DWI (b = 1500 s/mm2) using deep learning, employing either a binary classification (artifact prese…
gain_title: A DenseNet121 model trained on 11,806 breast DWI slices can automatically flag hyper- and hypointense artifacts on high b-value images with high discrimination, supporting quality assessment for lesion detection.
problem_title: (none)
trace_subject: (none)
gain_reading: A DenseNet121 model trained on 11,806 breast DWI slices can automatically flag hyper- and hypointense artifacts on high b-value images with high discrimination, supporting quality assessment for lesion detection.
gain_evidence: DenseNet121 achieved AUROCs of 0.91 and 0.95 for hyper- and hypointense artifact detection, respectively | Diffusion-weighted imaging (DWI) can support lesion detection and characterization in breast MRI.
problem_reading: (none)
problem_evidence: (none)
quick_read: Researchers developed convolutional neural networks to automatically flag intensity artifacts on high b-value breast diffusion-weighted MRI. Using 11,806 slices from 156 exams, DenseNet121 distinguished hyper- and hypointense artifacts with AUROCs of 0.91 and 0.95 on binary tasks and weighted AUROCs of 0.84 and 0.90 on multiclass tasks.

Reliable slice-wise quality assessment could help prevent artifact-related misinterpretation as DWI is added to breast MRI protocols to support lesion detection. The work remains retrospective and limited to one dataset, so prospective testing across scanners, protocols, and patient populations is needed to confirm generalizability and clinical impact.
limitation: Retrospective single-cohort design with limited examinations and need for external validation before clinical use.
tag: Evidence-backed gain
key_points: Retrospective study used 11,806 DWI slices from 156 examinations of female patients between 2022 and mid-2023 at 3T with b values 50, 750, and 1500 s/mm2. | Three CNNs (DenseNet121, ResNet18, SEResNet50) were trained for binary artifact presence; best model DenseNet121 was tested on independent holdout sets of 1836 and 1822 slices. | Multiclass classification yielded weighted AUROCs of 0.84 and 0.90, with Grad-CAM bounding boxes scored 3.52 ± 0.81 and 3.62 ± 0.65 on 5-point Likert-like scale by radiologist.
rundown: The team labeled slices for hyper- and hypointense artifacts and split them into training, validation, and holdout sets (8164/1806/1836 and 8164/1820/1822). They compared DenseNet121, ResNet18, and SEResNet50 for binary detection, then extended the best model to multiclass intensity classification.

Evaluation used accuracy, precision, recall, AUROC and AUPRC, plus radiologist review of Grad-CAM heatmap bounding boxes on 150 validation and 200 holdout slices. Results were reported on single-slice high b-value diffusion-weighted images at b = 1500 s/mm2.
sources:
- peer_reviewed | Journal of Magnetic Resonance Imaging | https://doi.org/10.1002/jmri.70572 | 2026-10-05
prev: 0000000000000000000000000000000000000000000000000000000000000000

sha256

ad35d58710a4853ade036a121ceb1e8f2ae2694dd35f402a95be2c691a2b0163

previous

0000000000000000000000000000000000000000000000000000000000000000