Advances and challenges in precision imaging.

Technological innovations in genomics and related fields have facilitated large sequencing efforts, supported new biological discoveries in cancer, and spawned an era of liquid biopsy biomarkers. Despite these advances, precision oncology has practical constraints, partly related to cancer's biological diversity and spatial and temporal complexity. Advanced imaging technologies are being developed to address some of the current limitations in early detection, treatment selection and planning, drug delivery, and therapeutic response, as well as difficulties posed by drug resistance, drug toxicity, disease monitoring, and metastatic evolution.

Multicenter Study of Long-Term Outcomes and Quality of Life in PHACE Syndrome after Age 10.

Objective: To characterize long-term outcomes of PHACE syndrome.

Study Design: Multicenter study with cross-sectional interviews and chart review of individuals with definite PHACE syndrome ≥10 years of age. Data from charts were collected across multiple PHACE-related topics.

Altered Iron and Microstructure in Huntington's Disease Subcortical Nuclei: Insight From 7T MRI.

Background: Pathophysiological changes of Huntington's disease (HD) can precede symptom onset by decades. Robust imaging biomarkers are needed to monitor HD progression, especially before the clinical onset.

Purpose: To investigate iron dysregulation and microstructure alterations in subcortical regions as HD imaging biomarkers, and to associate such alterations with motor and cognitive impairments.

Correlating Quantitative MRI-based Apparent Diffusion Coefficient Metrics with 24-month Neurodevelopmental Outcomes in Neonates from the HEAL Trial.

Background Multiple qualitative scoring systems have been created to capture the imaging severity of hypoxic ischemic brain injury. Purpose To evaluate quantitative volumes of acute brain injury at MRI in neonates with hypoxic ischemic brain injury and correlate these findings with 24-month neurodevelopmental outcomes and qualitative brain injury scoring by radiologists. Materials and Methods In this secondary analysis, brain diffusion-weighted MRI data from neonates in the High-dose Erythropoietin for Asphyxia and Encephalopathy trial, which recruited participants between January 2017 and October 2019, were analyzed.

Implementation and prospective real-time evaluation of a generalized system for in-clinic deployment and validation of machine learning models in radiology.

The medical imaging community has embraced Machine Learning (ML) as evidenced by the rapid increase in the number of ML models being developed, but validating and deploying these models in the clinic remains a challenge. The engineering involved in integrating and assessing the efficacy of ML models within the clinical workflow is complex. This paper presents a general-purpose, end-to-end, clinically integrated ML model deployment and validation system implemented at UCSF.

Ecodesign and Operational Strategies to Reduce the Carbon Footprint of MRI for Energy Cost Savings.

Background Radiology is a major contributor to health care's climate footprint due to energy-intensive devices, particularly MRI, which uses the most energy. Purpose To determine the energy, cost, and carbon savings that could be achieved through different scanner power management strategies. Materials and Methods In this retrospective evaluation, four outpatient MRI scanners from three vendors were individually equipped with power meters (1-Hz sampling rate).

What referring clinicians value most: Accuracy of radiology results and personal interactions with radiologists.

Purpose: We sought to identify which aspects of the referring clinician experience are most strongly correlated with overall satisfaction, and hence of greatest relevant importance to referring clinicians.

Methods: A survey instrument assessing referring clinician satisfaction throughout 11 domains of the radiology process map was distributed 2720 clinicians. The survey contained sections assessing each process map domain, with each section including a question about satisfaction overall in that domain and multiple more granular questions.

DeepSWI: Using Deep Learning to Enhance Susceptibility Contrast on T2*-Weighted MRI.

Background: Although susceptibility-weighted imaging (SWI) is the gold standard for visualizing cerebral microbleeds (CMBs) in the brain, the required phase data are not always available clinically. Having a postprocessing tool for generating SWI contrast from T2*-weighted magnitude images is therefore advantageous.

Purpose: To create synthetic SWI images from clinical T2*-weighted magnitude images using deep learning and evaluate the resulting images in terms of similarity to conventional SWI images and ability to detect radiation-associated CMBs.

Multi-tasking deep network for tinnitus classification and severity prediction from multimodal structural MR images.

Subjective tinnitus is an auditory phantom perceptual disorder without an objective biomarker. Fast and efficient diagnostic tools will advance clinical practice by detecting or confirming the condition, tracking change in severity, and monitoring treatment response. Motivated by evidence of subtle anatomical, morphological, or functional information in magnetic resonance images of the brain, we examine data-driven machine learning methods for joint tinnitus classification (tinnitus or no tinnitus) and tinnitus severity prediction.

Development of Gestational Age-Based Fetal Brain and Intracranial Volume Reference Norms Using Deep Learning.

Background And Purpose: Fetal brain MR imaging interpretations are subjective and require subspecialty expertise. We aimed to develop a deep learning algorithm for automatically measuring intracranial and brain volumes of fetal brain MRIs across gestational ages.

Materials And Methods: This retrospective study included 246 patients with singleton pregnancies at 19-38 weeks gestation.

The University of California San Francisco Preoperative Diffuse Glioma MRI Dataset.

Informatics, MR Diffusion Tensor Imaging, MR Perfusion, MR Imaging, Neuro-Oncology, CNS, Brain/Brain Stem, Oncology, Radiogenomics, Radiology-Pathology Integration © RSNA, 2022.

Comparison of quantitative susceptibility mapping methods for iron-sensitive susceptibility imaging at 7T: An evaluation in healthy subjects and patients with Huntington's disease.

Quantitative susceptibility mapping (QSM) is a promising tool for investigating iron dysregulation in neurodegenerative diseases, including Huntington's disease (HD). Many diverse methods have been proposed to generate accurate and robust QSM images. In this study, we evaluated the performance of different dipole inversion algorithms for iron-sensitive susceptibility imaging at 7T on healthy subjects of a large age range and patients with HD.

Survey of the American Society of Neuroradiology Membership on the Use and Value of Extracranial Carotid Vessel Wall MRI.

Background And Purpose: Extracranial vessel wall MRI (EC-VWI) contributes to vasculopathy characterization. This survey study investigated EC-VWI adoption by American Society of Neuroradiology (ASNR) members and indications and barriers to implementation.

Materials And Methods: The ASNR Vessel Wall Imaging Study Group survey on EC-VWI use, frequency, applications, MR imaging systems and field strength used, protocol development approaches, vendor engagement, reasons for not using EC-VWI, ordering provider interest, and impact on clinical care was distributed to the ASNR membership between April 2, 2019, to August 30, 2019.

Longitudinal Assessment of Posttreatment Diffuse Glioma Tissue Volumes with Three-dimensional Convolutional Neural Networks.

Neural networks were trained for segmentation and longitudinal assessment of posttreatment diffuse glioma. A retrospective cohort (from January 2018 to December 2019) of 298 patients with diffuse glioma (mean age, 52 years ± 14 [SD]; 177 men; 152 patients with glioblastoma, 72 patients with astrocytoma, and 74 patients with oligodendroglioma) who underwent two consecutive multimodal MRI examinations were randomly selected into training ( = 198) and testing ( = 100) samples. A posttreatment tumor segmentation three-dimensional nnU-Net convolutional neural network with multichannel inputs (T1, T2, and T1 postcontrast and fluid-attenuated inversion recovery [FLAIR]) was trained to segment three multiclass tissue types (peritumoral edematous, infiltrated, or treatment-changed tissue [ED]; active tumor or enhancing tissue [AT]; and necrotic core).

Functional network alterations in young brain tumor patients with radiotherapy-induced memory impairments and vascular injury.

Background: Cognitive impairment and cerebral microbleeds (CMBs) are long-term side-effects of cranial radiation therapy (RT). Previously we showed that memory function is disrupted in young patients and that the rate of cognitive decline correlates with CMB development. However, vascular injury alone cannot explain RT-induced cognitive decline.

Deep Learning to Predict Neonatal and Infant Brain Age from Myelination on Brain MRI Scans.

Background Assessment of appropriate brain myelination on T1- and T2-weighted MRI scans is based on gestationally corrected age (GCA) and requires subjective visual inspection of the brain with knowledge of normal myelination milestones. Purpose To develop a convolutional neural network (CNN) capable of estimating neonatal and infant GCA based on brain myelination on MRI scans. Materials and methods In this retrospective study from one academic medical center, brain MRI scans of patients aged 0-25 months with reported normal myelination were consecutively collected between January 1995 and June 2019.

Interinstitutional Portability of a Deep Learning Brain MRI Lesion Segmentation Algorithm.

Purpose: To assess how well a brain MRI lesion segmentation algorithm trained at one institution performed at another institution, and to assess the effect of multi-institutional training datasets for mitigating performance loss.

Materials And Methods: In this retrospective study, a three-dimensional U-Net for brain MRI abnormality segmentation was trained on data from 293 patients from one institution (IN1) (median age, 54 years; 165 women; patients treated between 2008 and 2018) and tested on data from 51 patients from a second institution (IN2) (median age, 46 years; 27 women; patients treated between 2003 and 2019). The model was then trained on additional data from various sources: 285 multi-institution brain tumor segmentations, 198 IN2 brain tumor segmentations, and 34 IN2 lesion segmentations from various brain pathologic conditions.