About

Ronald L. Wolf, MD, PhD, is an Associate Professor of Radiology at the Hospital of the University of Pennsylvania and an active staff member at Penn Presbyterian Medical Center, as well as an attending staff at the Hospital of the University of Pennsylvania. His primary research interests focus on the imaging of acute and chronic cerebrovascular disease, with the goal of better understanding carotid atherosclerosis and its relationship to stroke through structural MR and CT techniques. He aims to integrate structural imaging of the brain and blood vessels with functional imaging methods such as perfusion-weighted imaging, diffusion-weighted and tensor imaging, functional MRI, and spectroscopy to develop comprehensive evaluations for cerebrovascular disease. Wolf has a particular interest in perfusion imaging of the brain using various techniques, including bolus contrast CT and MR perfusion, stable xenon CT perfusion, and arterial spin labeled perfusion imaging, targeting both cerebrovascular conditions and nonischemic pathologies like CNS neoplasms and treatment effects. Clinically, Wolf applies his expertise primarily to noninvasive imaging studies of the brain, head and neck, and spine, with a focus on neuroimaging of cerebrovascular disease and CNS neoplasia. While he performs some interventional procedures such as angiography and image-guided lumbar or cervical punctures, his role is predominantly diagnostic. His clinical skills encompass many standard and advanced neuroimaging modalities, including noninvasive angiography, perfusion imaging, diffusion imaging, fMRI, and spectroscopy, aligning with his research interests.

Research topics

• Medicine
• Sociology
• Medical education
• Nursing
• Medical emergency
• Immunology
• Radiology
• Pathology
• Family medicine

Selected publications

• Impact of Preoperative fMRI and DTI on Neurosurgical Planning for Brain Tumors: A Multi-Institutional Survey Study

  American Journal of Neuroradiology · 2026-03-18

  articleOpen access

  PURPOSE: This multi-institutional study investigates how preoperative functional MRI (fMRI) and diffusion tensor imaging (DTI) influence surgical decision-making and clinical outcomes in patients undergoing brain tumor resection. METHODS AND MATERIALS: Seventy patients from four academic centers: Thomas Jefferson University (TJU), n=51, University Hospital Basel, n=11; University of Pennsylvania (UPenn), n=4, and Johns Hopkins University (JHU) n=3, underwent preoperative task-based fMRI and DTI. Six neurosurgeons completed structured pre- and post-imaging surveys evaluating changes in surgical approach, craniotomy planning, extent of resection, operative duration, and diagnostic confidence. RESULTS: Integration of fMRI and DTI into surgical planning resulted in a significant shift from awake to asleep craniotomies, especially at TJU (P = 0.01), with "asleep craniotomy" increasing to 51% overall (Chi-square P < .0001). fMRI led to a "much more aggressive" surgical plan in 39% of cases globally, most prominently at TJU (74%) and UPenn (50%), while JHU reported a decrease in aggressiveness in 33.3% of cases. DTI had a similar but slightly reduced impact, with "much more aggressive" being the top response (34%). fMRI was rated as more clinically valuable than DTI in 53.4% of cases overall with TJU having the highest rate (72%). Postoperatively, a larger extent of resection was reported in 61% of cases, with shorter-than-expected surgical durations in 51%. Overall, combined fMRI/DTI had a significant "strong positive" influence on surgery in 71% and clinical care in 68% of cases, with significant inter-institutional differences (P < 0.001). CONCLUSION: Preoperative fMRI and DTI significantly reshape neurosurgical planning by optimizing resection strategies. Most notably, preoperative mapping facilitated a significant shift from awake to asleep craniotomies, contributing to shorter than expected surgical durations without compromising the extent of resection.

  PublisherDOI

• Innovative Educational Program to Aid Clinical Vessel Wall MR Imaging Interpretation among Neuroradiologists

  American Journal of Neuroradiology · 2025-06-23

  articleOpen access

  Innovations that introduce new knowledge domains face greater barriers to adoption, often requiring investment in infrastructure, training/education, and cultural change. Sustaining and scaling an advanced clinical vessel wall MR imaging program requires technical resources and subspecialized neuroradiologists with advanced cerebrovascular expertise. A multifaceted educational program, including lectures, reporting templates, and an online resource, was implemented within a large academic Neuroradiology Division to address neuroradiology workforce readiness. Seven faculty "superusers" interested in cerebrovascular imaging were identified to facilitate case discussions and provide daily support for colleagues, clinicians, and MR technologists. Impact was assessed through a 12-month pre-/postintervention survey measuring confidence levels in evaluating vessel wall MR imaging examination appropriateness (a), assessing image quality (b), and diagnostic interpretations (c). Results showed division-wide increases in self-reported confidence and statistically significant increases among the superusers. These results show that a structured, expert-led peer-support model can enhance clinical readiness and sustain advanced imaging programs.

  PublisherDOI

• Automated Quality Evaluation Index for Arterial Spin Labeling Derived Cerebral Blood Flow Maps

  Journal of Magnetic Resonance Imaging · 2024-02-24 · 15 citations

  articleOpen access

  BACKGROUND: Arterial spin labeling (ASL) derived cerebral blood flow (CBF) maps are prone to artifacts and noise that can degrade image quality. PURPOSE: To develop an automated and objective quality evaluation index (QEI) for ASL CBF maps. STUDY TYPE: Retrospective. POPULATION: Data from N = 221 adults, including patients with Alzheimer's disease (AD), Parkinson's disease, and traumatic brain injury. FIELD STRENGTH/SEQUENCE: Pulsed or pseudocontinuous ASL acquired at 3 T using non-background suppressed 2D gradient-echo echoplanar imaging or background suppressed 3D spiral spin-echo readouts. ASSESSMENT: The QEI was developed using N = 101 2D CBF maps rated as unacceptable, poor, average, or excellent by two neuroradiologists and validated by 1) leave-one-out cross validation, 2) assessing if CBF reproducibility in N = 53 cognitively normal adults correlates inversely with QEI, 3) if iterative discarding of low QEI data improves the Cohen's d effect size for CBF differences between preclinical AD (N = 27) and controls (N = 53), 4) comparing the QEI with manual ratings for N = 50 3D CBF maps, and 5) comparing the QEI with another automated quality metric. STATISTICAL TESTS: Inter-rater reliability and manual vs. automated QEI were quantified using Pearson's correlation. P < 0.05 was considered significant. RESULTS: The correlation between QEI and manual ratings (R = 0.83, CI: 0.76-0.88) was similar (P = 0.56) to inter-rater correlation (R = 0.81, CI: 0.73-0.87) for the 2D data. CBF reproducibility correlated negatively (R = -0.74, CI: -0.84 to -0.59) with QEI. The effect size comparing patients and controls improved (R = 0.72, CI: 0.59-0.82) as low QEI data was discarded iteratively. The correlation between QEI and manual ratings (R = 0.86, CI: 0.77-0.92) of 3D ASL was similar (P = 0.09) to inter-rater correlation (R = 0.78, CI: 0.64-0.87). The QEI correlated (R = 0.87, CI: 0.77-0.92) significantly better with manual ratings than did an existing approach (R = 0.54, CI: 0.30-0.72). DATA CONCLUSION: Automated QEI performed similarly to manual ratings and can provide scalable ASL quality control. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 1.

  PublisherOA PDFDOI

• Severe caustic burns due to gastric acid: an unrecognized complication

  International Journal of Dermatology · 2024-09-02

  letterOpen access

  An elderly female patient, aged 85, was brought to the Emergency Department in a diminished overall state, disoriented, exhibiting flu-like symptoms, and experiencing a productive cough for the past 5 days. The admission photographs revealed the presence of vesicles and blisters in specific areas on the decolletage skin and around the clavicular area. Additionally, eroded regions around the mouth and red, inflamed patches on both cheeks extended toward the temporal region. A computed tomography (CT) scan revealed signs of aspiration pneumonia in the right upper lobe with tonsillitis and peritonsillar abscess. The afebrile patient was hypotonic and tachycardic. Laboratory tests revealed a leukocytosis of 13.1 G/l [4.5–11 G/l] and a CRP of 146 mg/l [10–100 mg/l]. The patient was transferred to the Internal Medicine Department for further management, and she was treated intravenously with ceftriaxone for 2 days. This was subsequently replaced by an oral combination of co-amoxicillin and clindamycin for 6 days. Once her condition improved, she was referred for an examination of her skin lesions. On examination, the patient had asymmetric, sharply defined, eroded areas around the mouth and on the left cheek, which extended with a drip-like pattern forming long streaks from the face to the right side of the neck and upper anterior trunk. The eroded areas, especially around the lips and perioral region, were strongly erythematous and covered with dried crusts and fibrin. There was widespread superficial skin detachment. Two lesions corresponded to partial-thickness (second-degree) burns (Figure 1a). The oral mucosa was not affected. Microbiological wound swabs for bacterial and viral infections were performed. PCR confirmed an HSV-1 superinfection in the perioral area, and the patient was administered valacyclovir for 5 days. Light microscopy studies of a skin biopsy showed an ortho-keratotic epidermis, full-thickness epidermal and adnexal necrosis, junctional degeneration of collagen fibers, and a sparse dermal infiltrate (Figure 2). The patient was questioned again about what happened prior to admission. She was in good general condition, fully oriented, and provided reliable answers during medical history taking. With time, she remembered having repeatedly vomited during the night and staying in bed without washing her body or changing her wet pajamas for hours because she had felt too weak. The patient denied increased alcohol consumption or excessive food intake prior to the onset of vomiting. Due to the clinical presentation, a possible suicidal intent through ingestion of liquid was also ruled out based on the medical history. Based on the patient's history, the large, eroded lesions were diagnosed as caustic skin burns due to prolonged contact with gastric juice. The wounds were treated with daily debridement in combination with topical anti-septics, hyaluronic acid cream, and occlusive wound dressings, with only slow improvement over 2 weeks. Finally, the patient underwent surgical debridement under local anesthesia, which allowed the wounds to evolve favorably, with complete healing complicated by scar formation, particularly around the mouth (Figure 1b).1 Caustic burns of the skin caused by gastric acid have rarely been described; there are only a few publications on the matter. There are two case reports about skin burns due to vomiting. The first patient repeatedly vomited because of binge drinking and came into contact with her vomit, causing burns on her breasts and upper abdomen.2 The second case had skin contact with her vomit for about 48 hours after a fall, which resulted in severe burns on her back and neck.3 Furthermore, there are anecdotal observations of severe skin burns due to leakage from percutaneous endoscopic gastrostomy (PEG) tubes. The burn was severe, up to the second degree, and one had to be treated with a skin graft.4, 5 Our case was peculiar since the patient experienced vomiting during her sleep and had inadvertently prolonged contact with the gastric juice on her skin, resulting in severe caustic damage corresponding to a second degree burn. Based on our experience, burns caused by gastric acid represent an unrecognized complication, knowledge of which is critical for its appropriate management. Where indicated, burn treatment specialists may need to be involved. Consent for the publication of all patient photographs and medical information was provided by the authors at the time of article submission to the journal, stating that all patients gave consent for their photographs and medical information to be published in print and online and with the understanding that this information may be publicly available.

  PublisherOA PDFDOI

• Artificial intelligence-based locoregional markers of brain peritumoral microenvironment

  Scientific Reports · 2023-01-18 · 11 citations

  articleOpen access

  Abstract In malignant primary brain tumors, cancer cells infiltrate into the peritumoral brain structures which results in inevitable recurrence. Quantitative assessment of infiltrative heterogeneity in the peritumoral region, the area where biopsy or resection can be hazardous, is important for clinical decision making. Here, we derive a novel set of Artificial intelligence (AI)-based markers capturing the heterogeneity of tumor infiltration, by characterizing free water movement restriction in the peritumoral region using Diffusion Tensor Imaging (DTI)-based free water volume fraction maps. We leverage the differences in the peritumoral region of metastasis and glioblastomas, the former consisting of vasogenic versus the latter containing infiltrative edema, to extract a voxel-wise deep learning-based peritumoral microenvironment index (PMI). Descriptive characteristics of locoregional hubs of uniformly high PMI values are then extracted as AI-based markers to capture distinct aspects of infiltrative heterogeneity. The proposed markers are utilized to stratify patients’ survival and IDH1 mutation status on a population of 275 adult-type diffuse gliomas (CNS WHO grade 4). Our results show significant differences in the proposed markers between patients with different overall survival and IDH1 mutation status ( t test, Wilcoxon rank sum test, linear regression; p &lt; 0.01). Clustering of patients using the proposed markers reveals distinct survival groups (logrank; p &lt; 10 −5 , Cox hazard ratio = 1.82; p &lt; 0.005). Our findings provide a panel of markers as surrogates of infiltration that might capture novel insight about underlying biology of peritumoral microstructural heterogeneity, providing potential biomarkers of prognosis pertaining to survival and molecular stratification, with applicability in clinical decision making.

  PublisherOA PDFDOI

• Clinical Applications of MR Perfusion Imaging

  2023-01-01

  book-chapterSenior author
  PublisherDOI

• The University of Pennsylvania glioblastoma (UPenn-GBM) cohort: advanced MRI, clinical, genomics, &amp; radiomics

  Scientific Data · 2022-07-29 · 166 citations

  articleOpen access

  Glioblastoma is the most common aggressive adult brain tumor. Numerous studies have reported results from either private institutional data or publicly available datasets. However, current public datasets are limited in terms of: a) number of subjects, b) lack of consistent acquisition protocol, c) data quality, or d) accompanying clinical, demographic, and molecular information. Toward alleviating these limitations, we contribute the "University of Pennsylvania Glioblastoma Imaging, Genomics, and Radiomics" (UPenn-GBM) dataset, which describes the currently largest publicly available comprehensive collection of 630 patients diagnosed with de novo glioblastoma. The UPenn-GBM dataset includes (a) advanced multi-parametric magnetic resonance imaging scans acquired during routine clinical practice, at the University of Pennsylvania Health System, (b) accompanying clinical, demographic, and molecular information, (d) perfusion and diffusion derivative volumes, (e) computationally-derived and manually-revised expert annotations of tumor sub-regions, as well as (f) quantitative imaging (also known as radiomic) features corresponding to each of these regions. This collection describes our contribution towards repeatable, reproducible, and comparative quantitative studies leading to new predictive, prognostic, and diagnostic assessments.

  PublisherOA PDFDOI

• Artificial intelligence-based locoregional markers of brain peritumoral microenvironment

  arXiv (Cornell University) · 2022-08-29

  preprintOpen access

  In malignant primary brain tumors, cancer cells infiltrate into the peritumoral brain structures which results in inevitable recurrence. Quantitative assessment of infiltrative heterogeneity in the peritumoral region, the area where biopsy or resection can be hazardous, is important for clinical decision making. Previous work on characterizing the infiltrative heterogeneity in the peritumoral region used various imaging modalities, but information of extracellular free water movement restriction has been limitedly explored. Here, we derive a unique set of Artificial Intelligence (AI)-based markers capturing the heterogeneity of tumor infiltration, by characterizing free water movement restriction in the peritumoral region using Diffusion Tensor Imaging (DTI)-based free water volume fraction maps. A novel voxel-wise deep learning-based peritumoral microenvironment index (PMI) is first extracted by leveraging the widely different water diffusivity properties of glioblastomas and brain metastases as regions with and without infiltrations in the peritumoral tissue. Descriptive characteristics of locoregional hubs of uniformly high PMI values are extracted as AI-based markers to capture distinct aspects of infiltrative heterogeneity. The proposed markers are applied to two clinical use cases on an independent population of 275 adult-type diffuse gliomas (CNS WHO grade 4), analyzing the duration of survival among Isocitrate-Dehydrogenase 1 (IDH1)-wildtypes and the differences with IDH1-mutants. Our findings provide a panel of markers as surrogates of infiltration that captures unique insight about underlying biology of peritumoral microstructural heterogeneity, establishing them as biomarkers of prognosis pertaining to survival and molecular stratification, with potential applicability in clinical decision making.

  PublisherOA PDFDOI

• NIMG-23. AI-BASED CONNECTED COMPONENT MARKERS OF BRAIN PERITUMORAL MICROENVIRONMENT USING WATER RESTRICTION INFORMATION

  Neuro-Oncology · 2022-11-01

  articleOpen access

  Abstract PURPOSE Glioblastoma is the most aggressive adult brain tumor, with heterogeneous neoplastic cell peritumoral infiltration. Characterization of this infiltrative peritumoral heterogeneity is an unmet clinical need that could contribute to strengthening our understanding of this disease. We propose novel AI-based markers of infiltration using deep-learning (DL) based on water restriction caused by infiltration, identified by diffusion tensor imaging (DTI). These markers could contribute to precision/personalized medicine, towards influencing clinical decision-making, including planning for biopsies, surgery, and radiation. METHOD: We automatically extracted peritumoral patches from free water volume fraction maps (FW-VF) of a retrospective cohort of 44 brain metastases and 66 glioblastomata patients and labelled them as high- and low- free water, respectively. An AI/DL model was then trained on these patches to distinguish differences in water restriction. Our trained AI/DL model was then applied on FW-VF of 264 hold-out glioblastoma patients (survival:0.43-76.9 months, age:21-88, 104 females) to generate a peritumoral microenvironment index (PMI) map quantifying infiltrative heterogeneity. Connected components (CCs) of high PMI values were calculated and their descriptive characteristics of size, number, shape, directionality, and spatial location, were extracted as AI-based markers. Gaussian mixture model clustering was then applied on these markers to determine if their representative infiltrative peritumoral heterogeneity can capture overall survival differences, by partitioning the patients into three clusters: low, moderate, and high risk. RESULTS The log-rank test yielded significant differences (p&amp;lt; 10-5) between low- and high-risk patients, (HR= 0.47, 95% CI:0.34-0.65; P&amp;lt; 0.005). Average PMI values were significantly greater in high-risk patients (P&amp;lt; 0.05). CONCLUSION We introduced novel AI-based markers of infiltration in the peritumoral microenvironment, using information of water restriction extracted from DTI. Our proposed markers can capture overall survival differences, based on the patterns of infiltration using DTI-based characterization of the water restriction, that show promise as clinically relevant prognostic biomarkers.

  PublisherOA PDFDOI

• BRMP-04. AI-based biomarker of the peritumoral region using tissue microstructure

  Neuro-Oncology · 2021-11-02 · 2 citations

  articleOpen access

  Abstract PURPOSE Glioblastomas, the most common malignant brain tumor [BS1], infiltrate into peritumoral brain structures, making clinical management challenging. An unmet need is to develop a biomarker that reliably characterize infiltration in the peritumoral region, where surgical biopsy or resection may be hazardous. Diffusion tensor imaging (DTI) with multicompartment modeling can characterize extracellular free water, providing unique information of the tissue microstructure that is able to capture this heterogeneity. We propose a novel biomarker based on peritumoral tissue microstructure, using deep-learning on DTI-based free water map. METHOD Peritumoral regions were automatically segmented for 136 patients with brain tumors (86 glioblastomas and 50 metastases, ages 23–87 years, 65 females). We trained a Convolutional Neural Network (CNN) on free-water maps using automatically defined patches in the peritumoral area from glioblastomas and metastases, labeled as low free-water and high free-water to extract a microstructural index for each voxel. To extract the biomarker, we grouped peritumoral voxels into connected components (CCs) where adjacent voxels have high (&amp;gt;0.9) microstructural index values. Two independent test sets related to two clinically significant problems were evaluated: i) metastases vs. glioblastomas; ii) glioma patients categorized into short and long survival groups and the number of CCs were statistically compared. RESULT Two-sample t-tests showed significant group difference in the number of CCs between metastases and glioblastomas (p&amp;lt; 0.05), and short and long-survivals (p&amp;lt;0.05) with higher number of CCs in metastases and long-survivals, which suggests smaller number of voxels in CCs. CONCLUSION The proposed biomarker based on CCs of microstructural index captures the differences in infiltration of the peritumoral region, showing larger CCs in glioblastomas and short-survivals corresponding to higher infiltration. CLINICAL IMPORTANCE The proposed biomarker provides a novel insight into the peritumoral microenvironment and can be derived from clinically feasible DTI data, providing new possibilities for the diagnosis and treatment of glioblastoma.

  PublisherOA PDFDOI

Recent grants

• NIH Grant R21CA170284

  NIH · $665k · 2016

• NIH Grant K23NS043381

  NIH · $863k · 2008

• NIH Grant P41RR002305

  NIH · $8.8M · 2015

Frequent coauthors

• Donald M. O’Rourke

  University of Pennsylvania

  45 shared

• Elias R. Melhem

  University of Maryland, Baltimore

  41 shared

• Sanjeev Chawla

  30 shared

• Harish Poptani

  University of Liverpool

  29 shared

• Kevin Judy

  Thomas Jefferson University

  24 shared

• John Woo

  Johns Hopkins University

  23 shared

• Christos Davatzikos

  University of Pennsylvania

  23 shared

• Hamed Akbari

  Santa Clara University

  22 shared