Natural language processing deep learning models for the differential between high-grade gliomas and metastasis: what if the key is how we report them?

OBJECTIVES: The differential between high-grade glioma (HGG) and metastasis remains challenging in common radiological practice. We compare different natural language processing (NLP)-based deep learning models to assist radiologists based on data contained in radiology reports. METHODS: This retrospective study included 185 MRI reports between 2010 and 2022 from two different institutions. A total of 117 reports were used for the training and 21 were reserved for the validation set, while the rest were used as a test set. A comparison of the performance of different deep learning models for HGG and metastasis classification has been carried out. Specifically, Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (BiLSTM), a hybrid version of BiLSTM and CNN, and a radiology-specific Bidirectional Encoder Representations from Transformers (RadBERT) model were used. RESULTS: For the classification of MRI reports, the CNN network provided the best results among all tested, showing a macro-avg precision of 87.32%, a sensitivity of 87.45%, and an F1 score of 87.23%. In addition, our NLP algorithm detected keywords such as tumor, temporal, and lobe to positively classify a radiological report as HGG or metastasis group. CONCLUSIONS: A deep learning model based on CNN enables radiologists to discriminate between HGG and metastasis based on MRI reports with high-precision values. This approach should be considered an additional tool in diagnosing these central nervous system lesions. CLINICAL RELEVANCE STATEMENT: The use of our NLP model enables radiologists to differentiate between patients with high-grade glioma and metastasis based on their MRI reports and can be used as an additional tool to the conventional image-based approach for this challenging task. KEY POINTS: • Differential between high-grade glioma and metastasis is still challenging in common radiological practice. • Natural language processing (NLP)-based deep learning models can assist radiologists based on data contained in radiology reports. • We have developed and tested a natural language processing model for discriminating between high-grade glioma and metastasis based on MRI reports that show high precision for this task.

An update on susceptibility-weighted imaging in brain gliomas.

Susceptibility-weighted imaging (SWI) has become a standard component of most brain MRI protocols. While traditionally used for detecting and characterising brain hemorrhages typically associated with stroke or trauma, SWI has also shown promising results in glioma assessment. Numerous studies have highlighted SWI's role in differentiating gliomas from other brain lesions, such as primary central nervous system lymphomas or metastases. Additionally, SWI aids radiologists in non-invasively grading gliomas and predicting their phenotypic profiles. Various researchers have suggested incorporating SWI as an adjunct sequence for predicting treatment response and for post-treatment monitoring. A significant focus of these studies is on the detection of intratumoural susceptibility signals (ITSSs) in gliomas, which are indicative of microhemorrhages and vessels within the tumour. The quantity, distribution, and characteristics of these ITSSs can provide radiologists with more precise information for evaluating and characterising gliomas. Furthermore, the potential benefits and added value of performing SWI after the administration of gadolinium-based contrast agents (GBCAs) have been explored. This review offers a comprehensive, educational, and practical overview of the potential applications and future directions of SWI in the context of glioma assessment. CLINICAL RELEVANCE STATEMENT: SWI has proven effective in evaluating gliomas, especially through assessing intratumoural susceptibility signal changes, and is becoming a promising, easily integrated tool in MRI protocols for both pre- and post-treatment assessments. KEY POINTS: • Susceptibility-weighted imaging is the most sensitive sequence for detecting blood and calcium inside brain lesions. • This sequence, acquired with and without gadolinium, helps with glioma diagnosis, characterisation, and grading through the detection of intratumoural susceptibility signals. • There are ongoing challenges that must be faced to clarify the role of susceptibility-weighted imaging for glioma assessment.

Understanding Visual Disorders through Correlation of Clinical and Radiologic Findings.

Patients presenting with visual disturbances often require a neuroimaging approach. The spectrum of visual disturbances includes three main categories: vision impairment, ocular motility dysfunction, and abnormal pupillary response. Decreased vision is usually due to an eye abnormality. However, it can also be related to other disorders affecting the visual pathway, from the retina to the occipital lobe. Ocular motility dysfunction may follow disorders of the cranial nerves responsible for eye movements (ie, oculomotor, trochlear, and abducens nerves); may be due to any abnormality that directly affects the extraocular muscles, such as tumor or inflammation; or may result from any orbital disease that can alter the anatomy or function of these muscles, leading to diplopia and strabismus. Given that pupillary response depends on the normal function of the sympathetic and parasympathetic pathways, an abnormality affecting these neuronal systems manifests, respectively, as pupillary miosis or mydriasis, with other related symptoms. In some cases, neuroimaging studies must complement the clinical ophthalmologic examination to better assess the anatomic and pathologic conditions that could explain the symptoms. US has a major role in the assessment of diseases of the eye and anterior orbit. CT is usually the first-line imaging modality because of its attainability, especially in trauma settings. MRI offers further information for inflammatory and tumoral cases. ©RSNA, 2024 Test Your Knowledge questions for this article are available in the supplemental material.

Multimodality Imaging of Infective Endocarditis.

Infective endocarditis (IE) is a complex multisystemic disease resulting from infection of the endocardium, the prosthetic valves, or an implantable cardiac electronic device. The clinical presentation of patients with IE varies, ranging from acute and rapidly progressive symptoms to a more chronic disease onset. Because of its severe morbidity and mortality rates, it is necessary for radiologists to maintain a high degree of suspicion in evaluation of patients for IE. Modified Duke criteria are used to classify cases as "definite IE," "possible IE," or "rejected IE." However, these criteria are limited in characterizing definite IE in clinical practice. The use of advanced imaging techniques such as cardiac CT and nuclear imaging has increased the accuracy of these criteria and has allowed possible IE to be reclassified as definite IE in up to 90% of cases. Cardiac CT may be the best choice when there is high clinical suspicion for IE that has not been confirmed with other imaging techniques, in cases of IE and perivalvular involvement, and for preoperative treatment planning or excluding concomitant coronary artery disease. Nuclear imaging may have a complementary role in prosthetic IE. The main imaging findings in IE are classified according to the site of involvement as valvular (eg, abnormal growths [ie, "vegetations"], leaflet perforations, or pseudoaneurysms), perivalvular (eg, pseudoaneurysms, abscesses, fistulas, or prosthetic dehiscence), or extracardiac embolic phenomena. The differential diagnosis of IE includes evaluation for thrombus, pannus, nonbacterial thrombotic endocarditis, Lambl excrescences, papillary fibroelastoma, and caseous necrosis of the mitral valve. The location of the lesion relative to the surface of the valve, the presence of a stalk, and calcification or enhancement at contrast-enhanced imaging may offer useful clues for their differentiation. ©RSNA, 2024 Test Your Knowledge questions for this article are available in the supplemental material.

How Imaging Advances Are Defining the Future of Precision Radiation Therapy.

Radiation therapy is fundamental in the treatment of cancer. Imaging has always played a central role in radiation oncology. Integrating imaging technology into irradiation devices has increased the precision and accuracy of dose delivery and decreased the toxic effects of the treatment. Although CT has become the standard imaging modality in radiation therapy, the development of recently introduced next-generation imaging techniques has improved diagnostic and therapeutic decision making in radiation oncology. Functional and molecular imaging techniques, as well as other advanced imaging modalities such as SPECT, yield information about the anatomic and biologic characteristics of tumors for the radiation therapy workflow. In clinical practice, they can be useful for characterizing tumor phenotypes, delineating volumes, planning treatment, determining patients' prognoses, predicting toxic effects, assessing responses to therapy, and detecting tumor relapse. Next-generation imaging can enable personalization of radiation therapy based on a greater understanding of tumor biologic factors. It can be used to map tumor characteristics, such as metabolic pathways, vascularity, cellular proliferation, and hypoxia, that are known to define tumor phenotype. It can also be used to consider tumor heterogeneity by highlighting areas at risk for radiation resistance for focused biologic dose escalation, which can impact the radiation planning process and patient outcomes. The authors review the possible contributions of next-generation imaging to the treatment of patients undergoing radiation therapy. In addition, the possible roles of radio(geno)mics in radiation therapy, the limitations of these techniques, and hurdles in introducing them into clinical practice are discussed. ©RSNA, 2024 Test Your Knowledge questions for this article are available in the supplemental material.

Automatic generation of conclusions from neuroradiology MRI reports through natural language processing.

PURPOSE: The conclusion section of a radiology report is crucial for summarizing the primary radiological findings in natural language and essential for communicating results to clinicians. However, creating these summaries is time-consuming, repetitive, and prone to variability and errors among different radiologists. To address these issues, we evaluated a fine-tuned Text-To-Text Transfer Transformer (T5) model for abstractive summarization to automatically generate conclusions for neuroradiology MRI reports in a low-resource language. METHODS: We retrospectively applied our method to a dataset of 232,425 neuroradiology MRI reports in Spanish. We compared various pre-trained T5 models, including multilingual T5 and those newly adapted for Spanish. For precise evaluation, we employed BLEU, METEOR, ROUGE-L, CIDEr, and cosine similarity metrics alongside expert radiologist assessments. RESULTS: The findings are promising, with the models specifically fine-tuned for neuroradiology MRI achieving scores of 0.46, 0.28, 0.52, 2.45, and 0.87 in the BLEU-1, METEOR, ROUGE-L, CIDEr, and cosine similarity metrics, respectively. In the radiological experts' evaluation, they found that in 75% of the cases evaluated, the conclusions generated by the system were as good as or even better than the manually generated conclusions. CONCLUSION: The methods demonstrate the potential and effectiveness of customizing state-of-the-art pre-trained models for neuroradiology, yielding automatic MRI report conclusions that nearly match expert quality. Furthermore, these results underscore the importance of designing and pre-training a dedicated language model for radiology report summarization.

Emerging Quantitative Imaging Techniques in Sports Medicine.

This article describes recent advances in quantitative imaging of musculoskeletal extremity sports injuries, citing the existing literature evidence and what additional evidence is needed to make such techniques applicable to clinical practice. Compositional and functional MRI techniques including T2 mapping, diffusion tensor imaging, and sodium imaging as well as contrast-enhanced US have been applied to quantify pathophysiologic processes and biochemical compositions of muscles, tendons, ligaments, and cartilage. Dual-energy and/or spectral CT has shown potential, particularly for the evaluation of osseous and ligamentous injury (eg, creation of quantitative bone marrow edema maps), which is not possible with standard single-energy CT. Recent advances in US technology such as shear-wave elastography or US tissue characterization as well as MR elastography enable the quantification of mechanical, elastic, and physical properties of tissues in muscle and tendon injuries. The future role of novel imaging techniques such as photon-counting CT remains to be established. Eventual prediction of return to play (ie, the time needed for the injury to heal sufficiently so that the athlete can get back to playing their sport) and estimation of risk of repeat injury is desirable to help guide sports physicians in the treatment of their patients. Additional values of quantitative analyses, as opposed to routine qualitative analyses, still must be established using prospective longitudinal studies with larger sample sizes.

Natural Language Processing in Pathology: Current Trends and Future Insights.

Natural language processing (NLP) plays a key role in advancing health care, being key to extracting structured information from electronic health reports. In the last decade, several advances in the field of pathology have been derived from the application of NLP to pathology reports. Herein, a comprehensive review of the most used NLP methods for extracting, coding, and organizing information from pathology reports is presented, including how the development of tools is used to improve workflow. In addition, this article discusses, from a practical point of view, the steps necessary to extract data and encode natural language information for its analytical processing, ranging from preprocessing of text to its inclusion in complex algorithms. Finally, the potential of NLP-based automatic solutions for improving workflow in pathology and their further applications in the near future is highlighted.

Past, present, and future in sports imaging: how to drive in a three-lane freeway.

KEY POINTS: • Morphological evaluation of SRIs is still nowadays the clinical standard in daily practice.• New functional imaging modalities show potential to add valuable physiopathological information about the insights of SRIs in specific clinical scenarios.• In the era of personalized medicine, AI algorithms may help athletes and all professionals involved in their care to improve the evaluation of SRIs through a definitive quantitative metric approach.

Diffusion-weighted Imaging of the Chest: A Primer for Radiologists.

Diffusion-weighted imaging (DWI) is a fundamental sequence not only in neuroimaging but also in oncologic imaging and has emerging applications for MRI evaluation of the chest. DWI can be used in clinical practice to enhance lesion conspicuity, tissue characterization, and treatment response. While the spatial resolution of DWI is in the order of millimeters, changes in diffusion can be measured on the micrometer scale. As such, DWI sequences can provide important functional information to MRI evaluation of the chest but require careful optimization of acquisition parameters, notably selection of b values, application of parallel imaging, fat saturation, and motion correction techniques. Along with assessment of morphologic and other functional features, evaluation of DWI signal attenuation and apparent diffusion coefficient maps can aid in tissue characterization. DWI is a noninvasive noncontrast acquisition with an inherent quantitative nature and excellent reproducibility. The outstanding contrast-to-noise ratio provided by DWI can be used to improve detection of pulmonary, mediastinal, and pleural lesions, to identify the benign nature of complex cysts, to characterize the solid portions of cystic lesions, and to classify chest lesions as benign or malignant. DWI has several advantages over fluorine 18 (18F)-fluorodeoxyglucose PET/CT in the assessment, TNM staging, and treatment monitoring of lung cancer and other thoracic neoplasms with conventional or more recently developed therapies. © RSNA, 2023 Quiz questions for this article are available in the supplemental material. Supplemental material and the slide presentation from the RSNA Annual Meeting are available for this article.

Current and Advanced Applications of Gadoxetic Acid-enhanced MRI in Hepatobiliary Disorders.

Gadoxetic acid is an MRI contrast agent that has specific applications in the study of hepatobiliary disease. After being distributed in the vascular and extravascular spaces during the dynamic phase, gadoxetic acid is progressively taken up by hepatocytes and excreted to the bile ducts during the hepatobiliary phase. The information derived from the enhancement characteristics during dynamic and hepatobiliary phases is particularly relevant in the detection and characterization of focal liver lesions and in the evaluation of the structure and function of the liver and biliary system. The use of new MRI sequences and advanced imaging techniques (eg, relaxometry, multiparametric imaging, and analysis of heterogeneity), the introduction of artificial intelligence, and the development of biomarkers and radiomic and radiogenomic tools based on gadoxetic acid-enhanced MRI findings will play an important role in the future in assessing liver function, chronic liver disease, and focal liver lesions; in studying biliary pathologic conditions; and in predicting treatment responses and prognosis. © RSNA, 2023 Quiz questions for this article are available in the supplemental material.

Clinical applications of skeletal muscle diffusion tensor imaging.

Diffusion tensor imaging (DTI) may allow the determination of new threshold values, based on water anisotropy, to differentiate between healthy muscle and various pathological processes. Additionally, it may quantify treatment monitoring or training effects. Most current studies have evaluated the potential of DTI of skeletal muscle to assess sports-related injuries or therapy, and training monitoring. Another critical area of application of this technique is the characterization and monitoring of primary and secondary myopathies. In this manuscript, we review the application of DTI in the evaluation of skeletal muscle in these and other novel clinical scenarios, with emphasis on the use of quantitative imaging-derived biomarkers. Finally, the main limitations of the introduction of DTI in the clinical setting and potential areas of future use are discussed.

How to do and evaluate DWI and DCE-MRI sequences for diabetic foot assessment.

MRI evaluation of the diabetic foot is still a challenge not only from an interpretative but also from a technical point of view. The incorporation of advanced sequences such as diffusion-weighted imaging (DWI) and dynamic contrast-enhanced (DCE) MRI into standard protocols for diabetic foot assessment could aid radiologists in differentiating between neuropathic osteoarthropathy (Charcot's foot) and osteomyelitis. This distinction is crucial as both conditions can coexist in diabetic patients, and they require markedly different clinical management and have distinct prognoses. Over the past decade, several studies have explored the effectiveness of DWI and dynamic contrast-enhanced MRI (DCE-MRI) in distinguishing between septic and reactive bone marrow, as well as soft tissue involvement in diabetic patients, yielding promising results. DWI, without the need for exogenous contrast, can provide insights into the cellularity of bone marrow and soft tissues. DCE-MRI allows for a more precise evaluation of soft tissue and bone marrow perfusion compared to conventional post-gadolinium imaging. The data obtained from these sequences will complement the traditional MRI approach in assessing the diabetic foot. The objective of this review is to familiarize readers with the fundamental concepts of DWI and DCE-MRI, including technical adjustments and practical tips for image interpretation in diabetic foot cases.

New pattern of atypical advanced interatrial block.

INTRODUCTION: Interatrial block (IAB) is defined as prolonged P-wave duration (≥ 120 ms) due to delayed conduction in the Bachmann bundle. This is readily identifiable using surface electrocardiogram (ECG). Advanced IAB can be classified as typical and atypical. Atypical IAB can be further categorized by (i) duration or (ii) morphology. In this report, we have identified a new pattern of atypical IAB with triphasic morphology of the P-wave in the inferior leads. METHODS: Two clinical cases were evaluated including surface ECGs. P-wave durations and amplitudes were measured with digital calipers using ECG analysis software (MUSE, GE HealthCare). Comparisons were made using prior data to evaluate IAB and P-wave duration and morphology. RESULTS: A new pattern of atypical advanced IAB shows prolonged P-wave duration (P wave >160 ms) and triphasic morphology in all inferior leads with P +/+/- and P +/-/+, respectively. We speculate that triphasic P-waves in the inferior leads represent three moments of atrial depolarization; from right to left. CONCLUSION: This study describes a novel pattern of atypical advanced IAB. Further investigation regarding the increased risk of atrial fibrillation and stroke associated with this new pattern is warranted in the future.

Automated Medical Diagnosis of Alzheimer´s Disease Using an Efficient Net Convolutional Neural Network.

Alzheimer's disease (AD) poses an enormous challenge to modern healthcare. Since 2017, researchers have been using deep learning (DL) models for the early detection of AD using neuroimaging biomarkers. In this paper, we implement the EfficietNet-b0 convolutional neural network (CNN) with a novel approach-"fusion of end-to-end and transfer learning"-to classify different stages of AD. 245 T1W MRI scans of cognitively normal (CN) subjects, 229 scans of AD subjects, and 229 scans of subjects with stable mild cognitive impairment (sMCI) were employed. Each scan was preprocessed using a standard pipeline. The proposed models were trained and evaluated using preprocessed scans. For the sMCI vs. AD classification task we obtained 95.29% accuracy and 95.35% area under the curve (AUC) for model training and 93.10% accuracy and 93.00% AUC for model testing. For the multiclass AD vs. CN vs. sMCI classification task we obtained 85.66% accuracy and 86% AUC for model training and 87.38% accuracy and 88.00% AUC for model testing. Based on our experimental results, we conclude that CNN-based DL models can be used to analyze complicated MRI scan features in clinical settings.

A handbook for beginners in skeletal muscle diffusion tensor imaging: physical basis and technical adjustments.

Magnetic resonance imaging (MRI) of skeletal muscle is routinely performed using morphological sequences to acquire anatomical information. Recently, there is an increasing interest in applying advanced MRI techniques that provide pathophysiologic information for skeletal muscle evaluation to complement standard morphologic information. Among these advanced techniques, diffusion tensor imaging (DTI) has emerged as a potential tool to explore muscle microstructure. DTI can noninvasively assess the movement of water molecules in well-organized tissues with anisotropic diffusion, such as skeletal muscle. The acquisition of DTI studies for skeletal muscle assessment requires specific technical adjustments. Besides, knowledge of DTI physical basis and skeletal muscle physiopathology facilitates the evaluation of this advanced sequence and both image and parameter interpretation. Parameters derived from DTI provide a quantitative assessment of muscle microstructure with potential to become imaging biomarkers of normal and pathological skeletal muscle. KEY POINTS: • Diffusion tensor imaging (DTI) allows to evaluate the three-dimensional movement of water molecules inside biological tissues. • The skeletal muscle structure makes it suitable for being evaluated with DTI. • Several technical adjustments have to be considered for obtaining robust and reproducible DTI studies for skeletal muscle assessment, minimizing potential artifacts.

Radiomics in Abdominopelvic Solid-Organ Oncologic Imaging: Current Status.

Radiomics is the process of extraction of high-throughput quantitative imaging features from medical images. These features represent noninvasive quantitative biomarkers that go beyond the traditional imaging features visible to the human eye. This article first reviews the steps of the radiomics pipeline, including image acquisition, ROI selection and image segmentation, image preprocessing, feature extraction, feature selection, and model development and application. Current evidence for the application of radiomics in abdominopelvic solid-organ cancers is then reviewed. Applications including diagnosis, subtype determination, treatment response assessment, and outcome prediction are explored within the context of hepatobiliary and pancreatic cancer, renal cell carcinoma, prostate cancer, gynecologic cancer, and adrenal masses. This literature review focuses on the strongest available evidence, including systematic reviews, meta-analyses, and large multicenter studies. Limitations of the available literature are highlighted, including marked heterogeneity in radiomics methodology, frequent use of small sample sizes with high risk of overfitting, and lack of prospective design, external validation, and standardized radiomics workflow. Thus, although studies have laid a foundation that supports continued investigation into radiomics models, stronger evidence is needed before clinical adoption.

New insights into the evaluation of peripheral nerves lesions: a survival guide for beginners.

PURPOSE: To perform a review of the physical basis of DTI and DCE-MRI applied to Peripheral Nerves (PNs) evaluation with the aim of providing readers the main concepts and tools to acquire these types of sequences for PNs assessment. The potential added value of these advanced techniques for pre-and post-surgical PN assessment is also reviewed in diverse clinical scenarios. Finally, a brief introduction to the promising applications of Artificial Intelligence (AI) for PNs evaluation is presented. METHODS: We review the existing literature and analyze the latest evidence regarding DTI, DCE-MRI and AI for PNs assessment. This review is focused on a practical approach to these advanced sequences providing tips and tricks for implementing them into real clinical practice focused on imaging postprocessing and their current clinical applicability. A summary of the potential applications of AI algorithms for PNs assessment is also included. RESULTS: DTI, successfully used in central nervous system, can also be applied for PNs assessment. DCE-MRI can help evaluate PN's vascularization and integrity of Blood Nerve Barrier beyond the conventional gadolinium-enhanced MRI sequences approach. Both approaches have been tested for PN assessment including pre- and post-surgical evaluation of PNs and tumoral conditions. AI algorithms may help radiologists for PN detection, segmentation and characterization with promising initial results. CONCLUSION: DTI, DCE-MRI are feasible tools for the assessment of PN lesions. This manuscript emphasizes the technical adjustments necessary to acquire and post-process these images. AI algorithms can also be considered as an alternative and promising choice for PN evaluation with promising results.

Atrial myxoma surgery and P-wave remodeling.

INTRODUCTION: Data regarding atrial electrocardiographic parameters in patients with atrial myxomas are scarce. METHODS: We aimed to study atrial electrocardiographic features in patients with atrial myxomas, before and after surgery. We also analyze the incidence of atrial fibrillation during follow-up and its correlation with different P-wave indexes. In total 32 patients in sinus rhythm that underwent atrial myxoma surgery were included. RESULTS: Mean age was 55.0 ± 12.6 years and 18 (56.3%) were women. Ten patients had left atrial enlargement (31.3%). Only one myxoma was located in the right atrium. At baseline seven cases of partial interatrial block (IAB) were detected (21.9%), two in the absence of left atrial enlargement. There were significant differences in atrial electrocardiographic indexes before and after surgery, including P-wave duration (108.9 ± 17.9 ms vs. 93.0 ± 12.4 ms; p < .001), partial IAB (21.9% vs. 3.1%; p = .012) and duration of P-wave terminal force in lead V1 negativity (-0.6 ± 0.3 vs. -0.5 ± 0.3 mm; p = .034). At a mean follow-up of 10.0 ± 5.5 years, 10 patients (31.3%) had experienced at least one episode of atrial fibrillation. Post-operative P-wave duration was associated with atrial fibrillation occurrence during follow-up (Hazard ratio: 0.90, 95% confidence interval: 0.83-0.98; p = .020). CONCLUSIONS: Abnormalities in atrial electrocardiographic indexes are common in atrial myxomas and frequently improve after surgery. Post-operative P-wave duration is associated with atrial fibrillation occurrence during follow-up.

Technical Update on MR Neurography.

Imaging evaluation of peripheral nerves (PNs) is challenging. Magnetic resonance imaging (MRI) and ultrasonography are the modalities of choice in the imaging assessment of PNs. Both conventional MRI pulse sequences and advanced techniques have important roles. Routine MR sequences are the workhorse, with the main goal to provide superb anatomical definition and identify focal or diffuse nerve T2 signal abnormalities. Selective techniques, such as three-dimensional (3D) cranial nerve imaging (CRANI) or 3D NerveVIEW, allow for a more detailed evaluation of normal and pathologic states. These conventional pulse sequences have a limited role in the comprehensive assessment of pathophysiologic and ultrastructural abnormalities of PNs. Advanced functional MR neurography sequences, such as diffusion tensor imaging tractography or T2 mapping, provide useful and robust quantitative parameters that can be useful in the assessment of PNs on a microscopic level. This article offers an overview of various technical parameters, pulse sequences, and protocols available in the imaging of PNs and provides tips on avoiding potential pitfalls.