PurposeDynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is widely used for breast cancer diagnosis due to its high sensitivity; however, limited specificity often leads to false positives and unnecessary biopsies. Treatment response assessment maps (TRAMs), derived from delayed contrast-enhanced imaging, characterize contrast accumulation and clearance and may improve diagnostic specificity.ApproachWe utilized the Advanced MRI Breast Lesions dataset from The Cancer Imaging Archive to develop deep learning–based models for breast lesion classification using both multiphase DCE-MRI and TRAM images. A 3D convolutional residual network (ResNet18) was employed for feature extraction, followed by a fully connected classifier. To assess prediction reliability, test-time augmentation was used to estimate aleatoric uncertainty. Radiomics-based models were also implemented for comparison.ResultsThe TRAM-based deep learning model achieved numerically higher area under the receiver operating characteristic curve (0.870 versus 0.835), sensitivity (0.848 versus 0.818), and specificity (0.823 versus 0.759) compared with DCE-MRI. Radiomics analysis showed similar trends. Uncertainty stratification demonstrated improved performance in low-uncertainty predictions for both modalities, where high-uncertainty DCE-MRI predictions showed a marked performance decline compared with TRAM.ConclusionsThese results suggest that TRAM-based deep learning models may provide a promising complementary imaging approach for breast lesion classification. Integration of uncertainty quantification could further enhance reliability, showing the potential of this framework for improving breast cancer diagnosis.
PurposeDeep learning–based magnetic resonance imaging (MRI) reconstruction can enable faster imaging and improved image quality; however, clinical adoption is limited by the lack of diverse raw multicoil training datasets. In routine clinical workflows, multicoil k-space data needed for training are often discarded, and only magnitude images are retained. In addition, phase is not deterministically recoverable from magnitude, and storing full k-space is impractical.ApproachWe address these limitations by (1) using a deep generative model to produce multiple plausible synthetic phase images conditioned on a single magnitude image and (2) introducing a radiofrequency (RF) coil library to synthesize multicoil k-space from the generated complex-valued images.ResultsThe synthetic phase images resemble ground-truth phase as assessed by Fréchet inception distance, and the generated multicoil k-space effectively trains unrolled MRI reconstruction networks.ConclusionsThese results indicate that generative phase synthesis coupled with an RF coil library provides a practical alternative to real multicoil k-space for training reconstruction models.
PurposeDeformational plagiocephaly and craniosynostosis require early detection for effective and less invasive interventions. Although 3D imaging and automated analysis allow for image-based assessment by nonspecialists, current 3D imaging systems are often costly and immobile. We present SoftSpot3D, a mobile three-dimensional head reconstruction framework designed as an accessible, low-cost screening solution for infant cranial assessment.ApproachThe system is independent of marker placement, leveraging a patterned cap strictly for feature-based tracking rather than geometric registration. Developed for iPad using consumer-grade RGB-D sensors, our pipeline mitigates infant motion through RGB-D odometry and generalized iterative closest point refinement to generate accurate cranial models. Clinical metrics are then automatically extracted by analyzing cross-sectional contours at the standardized measurement plane.ResultsValidated against the 3dMD (3dMD LLC, Atlanta, Georgia, United States) medical-grade stereophotogrammetry system on 143 infants (mean age: 5.73±2.99 months; 98 males, 45 females), the system achieved an average cranial Hausdorff distance of 2.94±1.32 mm compared with 3dMD reference meshes. Key diagnostic shape indices demonstrated high clinical agreement, with mean absolute errors of 2.18±1.63 for cranial index and 2.16±1.90 for cranial vault asymmetry index (both unitless ratios).ConclusionThese results establish the clinical accuracy of the system as an accessible, low-cost screening solution, providing the evidence base for its integration into routine pediatric care to enable more efficient early detection and referrals. Furthermore, the framework automatically generates a structured clinical report incorporating quantitative metrics and multiview visualizations, facilitating immediate point-of-care decision-making.
PurposeThe inclusion of diffusion-weighted magnetic resonance imaging (DW-MRI) in major national studies has allowed for quantification and characterization of white matter macrostructure and microstructure in cases of disease and disorder. Diffusion tensor imaging (DTI) yields 3D scalar maps to summarize the DW-MRI signal with quantitative metrics. Fractional anisotropy (FA) is a DTI metric that describes the degree of anisotropy within diffusion and is frequently used to interpret white matter integrity, but it exhibits high sensitivity in pathological interpretation. These properties of FA make it difficult to use as a consistent candidate imaging biomarker, as it makes different ages, cognitive statuses, and pathologies difficult to discern. With the development of other white matter models and metrics, such as structural connectivity network measures and tractography bundle analyses, there are many combinations to analyze for white matter integrity analysis, especially in the context of lower-quality clinical data that FA’s voxel-wise computation can be sensitive to. The widespread collection of DW-MRI data necessitates an analysis of these metrics to interpret white matter integrity beyond what FA can capture.ApproachWe designed a challenge for the medical imaging community to compare crowdsourced DW-MRI candidate imaging biomarkers to a baseline relying on FA in an age, sex, and cognitive status classification task and variables unknown to teams during the challenge. After the challenge submission period ended, we evaluated methods based on sensitivity to environmental variables (humidity and temperature on the day of the scan), and we correlated features with known Alzheimer’s disease (AD) pathology (amyloid beta 42, tau, and ptau cerebrospinal fluid/CSF readings) to evaluate if the methods collected in this study are robust to the limitations of FA. The key innovations are that we crowdsource DW-MRI feature representations for AD, we develop a framework to compare imaging feature representations in this context, we evaluate the sensitivity of DW-MRI imaging candidate biomarkers to environmental variables, and we correlate the novel features to known AD pathological measures.ResultsWe find that bundle-based methods outperform whole-brain methods in age classification tasks, but deep learning, computer vision-based methods show a new avenue for cognitive status prediction tasks.ConclusionThe submissions to our challenge show that there are many approaches to consider when developing new AD feature representations.
The editorial provides an overview of the JMI Special Section, Peer-Validated Resources for Medical Imaging.
