Visit us at Booth G18 on the First Level Gallery!
Visit our booth to stay up to date on the latest news from Elsevier’s radiology and radiography journals. Follow us on Twitter for more information about exhibit events and meetings. We look forward to seeing you at the ECR!
Special ECR 2019 article collection: The Bigger Picture
View our special collection of articles published in Elsevier’s radiology, radiography, nuclear medicine and medical physics journals. All articles in the collection are free to access online until 31 March 2019.
As radiology is inherently a data-driven specialty, it is especially conducive to utilizing data processing techniques. One such technique, deep learning (DL), has become a remarkably powerful tool for image processing in recent years. In this work, the Association of University Radiologists Radiology Research Alliance Task Force on Deep Learning provides an overview of DL for the radiologist. This article aims to present an overview of DL in a manner that is understandable to radiologists; to examine past, present, and future applications; as well as to evaluate how radiologists may benefit from this remarkable new tool. We describe several areas within radiology in which DL techniques are having the most significant impact: lesion or disease detection, classification, quantification, and segmentation. The legal and ethical hurdles to implementation are also discussed. By taking advantage of this powerful tool, radiologists can become increasingly more accurate in their interpretations with fewer errors and spend more time to focus on patient care.
Canadian Association of Radiologists Journal
Artificial intelligence (AI) is rapidly moving from an experimental phase to an implementation phase in many fields, including medicine. The combination of improved availability of large datasets, increasing computing power, and advances in learning algorithms has created major performance breakthroughs in the development of AI applications. In the last 5 years, AI techniques known as deep learning have delivered rapidly improving performance in image recognition, caption generation, and speech recognition. Radiology, in particular, is a prime candidate for early adoption of these techniques. It is anticipated that the implementation of AI in radiology over the next decade will significantly improve the quality, value, and depth of radiology's contribution to patient care and population health, and will revolutionize radiologists' workflows. The Canadian Association of Radiologists (CAR) is the national voice of radiology committed to promoting the highest standards in patient-centered imaging, lifelong learning, and research. The CAR has created an AI working group with the mandate to discuss and deliberate on practice, policy, and patient care issues related to the introduction and implementation of AI in imaging. This white paper provides recommendations for the CAR derived from deliberations between members of the AI working group. This white paper on AI in radiology will inform CAR members and policymakers on key terminology, educational needs of members, research and development, partnerships, potential clinical applications, implementation, structure and governance, role of radiologists, and potential impact of AI on radiology in Canada.
Tracing the use of computers in the radiology department from administrative functions through image acquisition, storage, and reporting, to early attempts at improved diagnosis, we begin to imagine possible new frontiers for their use in exam interpretation. Given their initially slow but ultimately substantial progress in the noninterpretive areas, we are left desiring and even expecting more in the interpretation realm. New technological advances may provide the next wave of progress and radiologists should be early adopters. Several potential applications are discussed and hopefully will serve to inspire future progress.
Artificial intelligence in fracture detection: transfer learning from deep convolutional neural networks
- Artificial Intelligence in the form of machine learning can be applied to fracture detection on plain radiographs.
- Transfer learning, from neural networks pre-trained on non-medical images, makes machine learning widely accessible.
- This strategy results in a test with high area under the curve accuracy (0.954).
- Similar applications of this technique could be used to improve efficiency and reduce patient harm.
Computerized Medical Imaging and Graphics
Deep convolutional neural networks for automatic classification of gastric carcinoma using whole slide images in digital histopathology
Deep learning using convolutional neural networks is an actively emerging field in histological image analysis. This study explores deep learning methods for computer-aided classification in H&E stained histopathological whole slide images of gastric carcinoma. An introductory convolutional neural network architecture is proposed for two computerized applications, namely, cancer classification based on immunohistochemical response and necrosis detection based on the existence of tumor necrosis in the tissue. Classification performance of the developed deep learning approach is quantitatively compared with traditional image analysis methods in digital histopathology requiring prior computation of handcrafted features, such as statistical measures using gray level co-occurrence matrix, Gabor filter-bank responses, LBP histograms, gray histograms, HSV histograms and RGB histograms, followed by random forest machine learning. Additionally, the widely known AlexNet deep convolutional framework is comparatively analyzed for the corresponding classification problems. The proposed convolutional neural network architecture reports favorable results, with an overall classification accuracy of 0.6990 for cancer classification and 0.8144 for necrosis detection.
Current Problems in Diagnostic Radiology
Digital nerves are intrinsic to the sensory and motor function of the hand. These nerves represent the terminal ramifications of the ulnar, median, and radial nerves and are located distal to the carpal tunnel and Guyon canal. With magnetic resonance imaging, traumatic and nontraumatic abnormalities of the digital nerves can be shown with high contrast as well as high spatial resolution. Digital nerve abnormalities are most commonly posttraumatic in nature; however, infection, inflammatory, vascular malformations, and neoplasms can rarely occur. Magnetic resonance imaging is playing an increasing role in the assessment of peripheral nerve injury throughout the body, and in the hand, which can accurately identify the extent and character of digital nerve abnormalities before operative intervention.
Diagnostic and Interventional Imaging
The authors propose a pictorial review illustrating the imaging features of chest wall tumors and their specific features that discusses the main differential diagnoses. This review is based on published information and on our own experience.
European Journal of Radiology
Texture analysis and machine learning to characterize suspected thyroid nodules and differentiated thyroid cancer: Where do we stand?
In thyroid imaging, “texture” refers to the echographic appearence of the parenchyma or a nodule. However, definition of the image characteristics is operator dependent and influenced by the operator’s experience. In a more objective texture analysis, a variety of mathematical methods are used to describe image inhomogeneity, allowing assessment of an image by means of quantitative parameters. Moreover, this approach may be used to develop an efficient computer-aided diagnosis (CAD) system to yield a second opinion when differentiating malignant and benign thyroid lesions. The aim of this review is to summarize the available literature data on texture analysis, with and without CAD, in patients with suspected thyroid nodules or differentiated thyroid cancer, and to assess the current state of the approach.
European Journal of Radiology Open
Although the World Health Organization suggests 10–15% as the adequate cesarean delivery rate to assure optimal prognosis for mother and children, cesarean rates have continuously increased worldwide over the last three decades, even in primiparous women. Moreover, uterine scars after myomectomies, complications of obstetrical interventions and more recently, after fetal surgery, are often observed. This review article describes the most commonly seen complications related to prior uterine scars and discusses their imaging findings, with emphasis on the increasing role of Magnetic Resonance Imaging for diagnosis.
JACC: Cardiovascular Imaging
ANEURYSM OF THE ASCENDING AORTA MAY CAUSE ACUTE TYPE A AORTIC DISSECTION, and the primary aim of a prophylactic operation is avoidance of this life-threatening complication. Current guidelines recommend prophylactic replacement of the aneurysmal ascending aorta at a diameter of ≥5.5 cm. However, several reports have shown that nearly 50% of patients with an acute type A aortic dissection present with an aortic diameter <5.5 cm. It is controversial how to best respond to these observations. Because surgical advances have led to a reduction of early surgical mortality between 1% and 3%, many clinicians believe that surgery is warranted in smaller aortas. Given the frequency of a bicuspid aortic valve (BAV), such a strategy has health implications for this population.
Journal of the American College of Radiology
Thyroid nodules are a frequent finding on neck sonography. Most nodules are benign; therefore, many nodules are biopsied to identify the small number that are malignant or require surgery for a definitive diagnosis. Since 2009, many professional societies and investigators have proposed ultrasound-based risk stratification systems to identify nodules that warrant biopsy or sonographic follow-up. Because some of these systems were founded on the BI-RADS® classification that is widely used in breast imaging, their authors chose to apply the acronym TI-RADS, for Thyroid Imaging, Reporting and Data System. In 2012, the ACR convened committees to (1) provide recommendations for reporting incidental thyroid nodules, (2) develop a set of standard terms (lexicon) for ultrasound reporting, and (3) propose a TI-RADS on the basis of the lexicon. The committees published the results of the first two efforts in 2015. In this article, the authors present the ACR TI-RADS Committee’s recommendations, which provide guidance regarding management of thyroid nodules on the basis of their ultrasound appearance. The authors also describe the committee’s future directions.
Journal of the American Society of Echocardiography
Two-dimensional transthoracic echocardiography is the most widely used noninvasive imaging modality for the evaluation and diagnosis of cardiac pathology. However, because of the physical properties of ultrasound waves and specifics in ultrasound image reconstruction, cardiologists are often confronted with ultrasound image artifacts. It is particularly important to recognize such artifacts in order to avoid misdiagnosis of conditions ranging from aortic dissection to thrombosis and endocarditis. This overview article summarizes the most common image artifacts encountered in routine clinical practice, along with explanations of their physical mechanisms and guidance in avoiding their misinterpretation.
Journal of Medical Imaging and Radiation Sciences
Because of its fast image acquisition and the rich diagnostic information it provides, computed tomography (CT) has gradually become a popular imaging modality among clinicians. Because CT scanners emit x-rays, the increased use of CT in clinical applications inevitably leads to increased medical radiation dose to the population. Because of the well-known cancer-inducing effects of high dose x-ray radiation, this increased dose has caused concerns among policy makers and general public that CT patients may be at a higher risk of developing cancer. Over the years, CT manufacturers have developed a variety of strategies to address this issue, the latest being a model-based iterative reconstruction (MBIR) algorithm. MBIR is an advanced CT algorithm that incorporates modeling of several key parameters that were omitted in earlier algorithms to reduce computational requirement and speed up scans. This review article examines the latest literature in the clinical CT field and discusses the general principles of MBIR, its dose and noise reduction potentials, its imaging characteristics, and its limitations. MBIR algorithm and its application in today's CT imaging will greatly reduce the radiation dose to patients and improve image quality for clinicians.
Journal of Forensic Radiology and Imaging
Case control study of drowning fatalities investigated with autopsy and post mortem computed tomography (PMCT). 40 drowning fatalities (25 men, 15 women; 24 salt water, 16 fresh water) and 80 controls were included. The aim was to investigate the difference in lung tissue density (g/liter) and radio opacity between drowning cases compared to control cases and to determine if it was possible to differentiate saltwater and freshwater drowning by measuring a difference in radio density of blood in the hearth chambers or great vessels before and after passage through the lungs of a drowned individual or when comparing drowned individuals with controls. The lung density was measured by a combination of PMCT measured total lung volume and autopsy measured total lung weight. We found that the lung density and the lung radio density were decreased, the lung volume increased and the lung weights equal in drowning cases compared to controls, illustrating the phenomenon knows as “emphysema aquosum”. The physiological explanation could be washing out of surfactant by the drowning media, resulting in atelectasisand trapping of air in the peripheral lung regions. It was not possible to separate fresh and saltwater drowning by comparing radio opacity of blood in the hearth chambers or great vessels or by comparing the radio opacity of blood in cases and controls. We suspect that sedimentation of red blood cells after death makes such measurements meaningless.
Journal of Vascular and Interventional Radiology
One-Year Analysis of the Prospective Multicenter SENTRY Clinical Trial: Safety and Effectiveness of the Novate Sentry Bioconvertible Inferior Vena Cava Filter
SENTRY Trial Investigators
To prospectively assess the Sentry bioconvertible inferior vena cava (IVC) filter in patients requiring temporary protection against pulmonary embolism (PE).
Materials and Methods
At 23 sites, 129 patients with documented deep vein thrombosis (DVT) or PE, or at temporary risk of developing DVT or PE, unable to use anticoagulation were enrolled. The primary end point was clinical success, including successful filter deployment, freedom from new symptomatic PE through 60 days before filter bioconversion, and 6-month freedom from filter-related complications. Patients were monitored by means of radiography, computerized tomography (CT), and CT venography to assess filtering configuration through 60 days, filter bioconversion, and incidence of PE and filter-related complications through 12 months.
Clinical success was achieved in 111 of 114 evaluable patients (97.4%, 95% confidence interval [CI] 92.5%–99.1%). The rate of freedom from new symptomatic PE through 60 days was 100% (n = 129, 95% CI 97.1%–100.0%), and there were no cases of PE through 12 months for either therapeutic or prophylactic indications. Two patients (1.6%) developed symptomatic caval thrombosis during the first month; neither experienced recurrence after successful interventions. There was no filter tilting, migration, embolization, fracture, or caval perforation by the filter, and no filter-related death through 12 months. Filter bioconversion was successful for 95.7% (110/115) at 6 months and for 96.4% (106/110) at 12 months.
The Sentry IVC filter provided safe and effective protection against PE, with a high rate of intended bioconversion and a low rate of device-related complications, through 12 months of imaging-intense follow-up.
Magnetic Resonance Imaging
Magnetic resonance (MR) imaging offers a wide variety of imaging techniques. A large amount of data is created per examination which needs to be checked for sufficient quality in order to derive a meaningful diagnosis. This is a manual process and therefore time- and cost-intensive. Any imaging artifacts originating from scanner hardware, signal processing or induced by the patient may reduce the image quality and complicate the diagnosis or any image post-processing. Therefore, the assessment or the ensurance of sufficient image quality in an automated manner is of high interest. Usually no reference image is available or difficult to define. Therefore, classical reference-based approaches are not applicable. Model observers mimicking the human observers (HO) can assist in this task. Thus, we propose a new machine-learning-based reference-free MR image quality assessment framework which is trained on HO-derived labels to assess MR image quality immediately after each acquisition. We include the concept of active learning and present an efficient blinded reading platform to reduce the effort in the HO labeling procedure. Derived image features and the applied classifiers(support-vector-machine, deep neural network) are investigated for a cohort of 250 patients. The MR image quality assessment framework can achieve a high test accuracy of 93.7% for estimating quality classes on a 5-point Likert-scale. The proposed MR image quality assessment framework is able to provide an accurate and efficient quality estimation which can be used as a prospective quality assurance including automatic acquisition adaptation or guided MR scanner operation, and/or as a retrospective quality assessment including support of diagnostic decisions or quality control in cohort studies.
Medical Engineering and Physics
Technological advances in sensors and communications have enabled discrete integration into everyday objects, both in the home and about the person. Information gathered by monitoring physiological, behavioural, and social aspects of our lives, can be used to achieve a positive impact on quality of life, health, and well-being. Wearable sensors are at the cusp of becoming truly pervasive, and could be woven into the clothes and accessories that we wear such that they become ubiquitous and transparent. To interpret the complex multidimensional information provided by these sensors, data fusion techniques are employed to provide a meaningful representation of the sensor outputs. This paper is intended to provide a short overview of data fusion techniques and algorithms that can be used to interpret wearable sensor data in the context of health monitoring applications. The application of these techniques are then described in the context of healthcare including activity and ambulatory monitoring, gait analysis, fall detection, and biometric monitoring. A snap-shot of current commercially available sensors is also provided, focusing on their sensing capability, and a commentary on the gaps that need to be bridged to bring research to market.
Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks. Concise overviews are provided of studies per application area: neuro, retinal, pulmonary, digital pathology, breast, cardiac, abdominal, musculoskeletal. We end with a summary of the current state-of-the-art, a critical discussion of open challenges and directions for future research.
The mechanism underlying neurological dysfunction in chronic fatigue syndrome/myalgic encephalomyelitis (CFS/ME) is yet to be established. This study investigated the temporal complexity of blood oxygenation level dependent (BOLD) changes in response to the Stroop task in CFS patients.
43 CFS patients (47.4 ± 11.8 yrs) and 26 normal controls (NCs, 43.4 ± 13.9 yrs) were included in this study. Their mental component summary (MCS) and physical component summary (PCS) from the 36-item Short Form Health Survey (SF-36) questionnaire were recorded. Their Stroop colour-word task performance was measured by accuracy and response time (RT). The BOLD changes associated with the Stroop task were evaluated using a 2-level general linear model approach. The temporal complexity of the BOLD responses, a measure of information capacity and thus adaptability to a challenging environment, in each activated region was measured by sample entropy (SampEn).
The CFS patients showed significantly longer RTs than the NCs (P < 0.05) but no significant difference in accuracy. One sample t-tests for the two groups (Family wise error adjusted PFWE < 0.05) showed more BOLD activation regions in the CFS, although a two sample group comparison did not show significant difference. BOLD SampEns in ten regions were significantly lower (FDR-q < 0.05) in CFS patients. BOLD SampEns in 15 regions were significantly associated with PCS (FDR-q < 0.05) and in 9 regions were associated with MCS (FDR-q < 0.05) across all subjects. SampEn of the BOLD signal in the medioventral occipital cortex could explain 40% and 31% of the variance in the SF-36 PCS and MCS scores, and those in the precentral gyrus could explain an additional 16% and 7% across all subjects.
This is the first study to investigate BOLD signal SampEn in response to tasks in CFS. The results suggest the brain responds differently to a cognitive challenge in patients with CFS, with recruitment of wider regions to compensate for lower information capacity.
Nuclear Medicine and Biology
Over recent years, within the community of radiopharmaceutical sciences, there has been an increased incidence of incorrect usage of established scientific terms and conventions, and even the emergence of ‘self-invented’ terms. In order to address these concerns, an international Working Group on ‘Nomenclature in Radiopharmaceutical Chemistry and related areas’ was established in 2015 to achieve clarification of terms and to generate consensus on the utilisation of a standardised nomenclature pertinent to the field.
Upon open consultation, the following consensus guidelines were agreed, which aim to:
- Provide a reference source for nomenclature good practice in the radiopharmaceutical sciences.
- Clarify the use of terms and rules concerning exclusively radiopharmaceutical terminology, i.e. nuclear- and radiochemical terms, symbols and expressions.
- Address gaps and inconsistencies in existing radiochemistry nomenclature rules.
- Provide source literature for further harmonisation beyond our immediate peer group (publishers, editors, IUPAC, pharmacopoeias, etc.).
Noticing the fast growing translation of artificial intelligence (AI) technologies to medical image analysis this paper emphasizes the future role of the medical physicist in this evolving field. Specific challenges are addressed when implementing big data concepts with high-throughput image data processing like radiomics and machine learning in a radiooncology environment to support clinical decisions.
Based on the experience of our interdisciplinary radiomics working group, techniques for processing minable data, extracting radiomics features and associating this information with clinical, physical and biological data for the development of prediction models are described. A special emphasis was placed on the potential clinical significance of such an approach.
Clinical studies demonstrate the role of radiomics analysis as an additional independent source of information with the potential to influence the radiooncology practice, i.e. to predict patient prognosis, treatment response and underlying genetic changes. Extending the radiomics approach to integrate imaging, clinical, genetic and dosimetric data (‘panomics’) challenges the medical physicist as member of the radiooncology team.
The new field of big data processing in radiooncology offers opportunities to support clinical decisions, to improve predicting treatment outcome and to stimulate fundamental research on radiation response both of tumor and normal tissue. The integration of physical data (e.g. treatment planning, dosimetric, image guidance data) demands an involvement of the medical physicist in the radiomics approach of radiooncology. To cope with this challenge national and international organizations for medical physics should organize more training opportunities in artificial intelligence technologies in radiooncology.
Challenges in mammography education and training today: The perspectives of radiography teachers/mentors and students in five European countries
This study aims to explore current challenges in mammography education from the perspectives of radiography teachers, mentors and students.
A qualitativee study including two focus groups interviews, with radiography teachers/mentors (n = 5) and student radiographers (n = 5) exploring their perspectives on challenges in mammography education today. The content analysis methodology proposed by Graneheim and Lundman was applied to the interviews.
Three main categories were identified, each with subcategories identified as: (1) Building Bridges; Applying Theoretical knowledge in Practice, Performing Mammograms, Communication and Quality Assessment (2) State of the Art in Mammography; Personal Attitudes and Skills, Quality Awareness and Patient Care(3) Exploring the Curriculum; Time Constraints, Capacity in Clinical Placement, Multidisciplinary Field and Elective Course.
The short study period allocated to this discipline and lack of material resources were considered the main limitations in mammography education, both impacting on the development of students' skills. Breast positioning, patient communication and quality control were considered key factors affecting mammography performance, patient experience and diagnostic outcome and should therefore be the core focus in mammography education.
Radiology Case Reports
We report a typical case of “tennis leg”, in which the main finding was a fluid collection between the medial head of the gastrocnemius and soleus muscles. Since the first clinical description of this entity in 1883, the injury has been attributed to rupture of the plantaris tendon. However, recent studies of this condition with sonography and magnetic resonance imaging have shown that most of these cases are actually due to injury to the gastrocnemius and/or soleus muscles, and up to 10% are due to deep venous thrombosis masquerading as muscle injury. The plantaris muscle and tendon are only rarely involved in this injury.
Seminars in Nuclear Medicine
Fever of unknown origin (FUO) is commonly defined as fever higher than 38.3°C on several occasions during at least 3 weeks with uncertain diagnosis after a number of obligatory investigations. The differential diagnosis of FUO can be subdivided in four categories: infections, malignancies, noninfectious inflammatory diseases, and miscellaneous causes. In most cases of FUO, there is an uncommon presentation of a common disease. FDG-PET/CT is a sensitive diagnostic technique for the evaluation of FUO by facilitating anatomical localization of focally increased FDG uptake, thereby guiding further diagnostic tests to achieve a final diagnosis. FDG-PET/CT should become a routine procedure in the workup of FUO when diagnostic clues are absent. FDG-PET/CT appears to be a cost-effective routine imaging technique in FUO by avoiding unnecessary investigations and reducing the duration of hospitalization.
Pneumonia continues to be a significant global health problem, remaining among the top 10 causes of death globally and in the US,1 especially among elderly patients.2 The diagnosis of pneumonia relies mainly on clinical symptoms and imaging findings. Despite imaging studies playing an important role in early diagnosis, laboratory confirmation can be obtained in only 30%-70% of cases even after a full microbial battery is performed.3 Invasive procedures, such as bronchoscopy with lavage and biopsy, are limited to hospital-associated infections and immunocompromised patients.
Seminars in Ultrasound, CT, and MRI
With the increasing discovery of small renal masses with cross-sectional imaging, there has been the concomitant rise in their treatment. With the intent of early curative surgery for a presumed renal cell carcinoma, many renal masses are being resected at surgery without a confirmed diagnosis. Many of them are benign, and some are angiomyolipomas. The diagnosis of renal angiomyolipoma using imaging is, therefore, is as important as ever. Although most, if not all angiomyolipomas with abundant fat are diagnosed readily, some have too little fat to be detected with imaging. This article reviews the current classification, imaging pitfalls, and diagnosis of angiomyolipoma with an emphasis on the fat-poor types. Proper imaging technique, a thorough search for fat, and the appropriate use of percutanoeus biopsy are all needed to eliminate the unnecessary treatment of these benign neoplasms.
Techniques in Vascular and Interventional Radiology
Pulmonary embolism (PE) is a common and potentially fatal form of venous thromboembolism that can be challenging to diagnose and manage. PE occurs when there is obstruction of the pulmonary vasculature and is a common cause of morbidity and mortality in the United States. A combination of acquired and inherited factors may contribute to the development of this disease and should be considered, since they have implications for both susceptibility to PE and treatment. Patients with suspected PE should be evaluated efficiently to diagnose and administer therapy as soon as possible, but the presentation of PE is variable and nonspecific so diagnosis is challenging. PE can range from small, asymptomatic blood clots to large emboli that can occlude the pulmonary arteries causing sudden cardiovascular collapse and death. Thus, risk stratification is critical to both the prognosis and management of acute PE. In this review, we discuss the epidemiology, risk factors, pathophysiology, and natural history of PE and deep vein thrombosis.
Ultrasound in Medicine & Biology
Common Carotid Artery Diameter, Blood Flow Velocity and Wave Intensity Responses at Rest and during Exercise in Young Healthy Humans: A Reproducibility Study
The aim of this study was to assess the reproducibility of non-invasive, ultrasound-derived wave intensity (WI) in humans at the common carotid artery. Common carotid artery diameter and blood velocity of 12 healthy young participants were recorded at rest and during mild cycling, to assess peak diameter, change in diameter, peak velocity, change in velocity, time derivatives, non-invasive wave speed and WI. Diameter, velocity and WI parameters were fairly reproducible. Diameter variables exhibited higher reproducibility than corresponding velocity variables (intra-class correlation coefficient [ICC] = 0.79 vs. 0.73) and lower dispersion (coefficient of variation [CV] = 5% vs. 9%). Wave speed had fair reproducibility (ICC = 0.6, CV = 16%). WI energy variables exhibited higher reproducibility than corresponding peaks (ICC = 0.78 vs. 0.74) and lower dispersion (CV = 16% vs. 18%). The majority of variables had higher ICCs and lower CVs during exercise. We conclude that non-invasive WI analysis is reliable both at rest and during exercise.
Zeitschrift fuer Medizinische Physik
Quantitative Susceptibility Mapping (QSM) is a novel MRI based technique that relies on estimates of the magnetic field distribution in the tissue under examination. Several sophisticated data processing steps are required to extract the magnetic field distribution from raw MRI phase measurements. The objective of this review article is to provide a general overview and to discuss several underlying assumptions and limitations of the pre-processing steps that need to be applied to MRI phase data before the final field-to-source inversion can be performed. Beginning with the fundamental relation between MRI signal and tissue magnetic susceptibility this review covers the reconstruction of magnetic field maps from multi-channel phase images, background field correction, and provides an overview of state of the art QSM solution strategies.