Investigative Radiology

Semelka, Richard C.; Ramalho, Miguel

doi: 10.1097/rli.0000000000000977pmid: 37058336

Abstract This review describes the current knowledge of a form of gadolinium toxicity termed gadolinium deposition disease (GDD), supplemented with the opinions of the authors developed during 6 years of clinical experience treating GDD. Gadolinium deposition disease can also be considered a subset under the symptoms associated with gadolinium exposure rubric. Young and middle-aged White women of central European genetic origin are the most affected. The most common symptoms are fatigue, brain fog, skin pain, skin discoloration, bone pain, muscle fasciculations, and pins and needles, but a long list of additional symptoms is reported herein. The time of onset of symptoms ranges from immediate to 1 month after gadolinium-based contrast agent (GBCA) administration. The primary treatment is to avoid further GBCAs and metal removal through chelation. Presently, the most effective chelating agent is DTPA because of its high affinity with gadolinium. Flare development is an expected outcome, amenable to concurrent immune dampening. We emphasize in this review the critical nature of recognizing GDD when it first arises, as the disease becomes progressively more severe with each subsequent GBCA injection. It is generally very treatable after the first symptoms of GDD, often arising after the first GBCA injection. Future directions of disease detection and treatment are discussed.

Parillo, Marco; Mallio, Carlo A.; Van der Molen, Aart J.; Rovira, Àlex; Ramalho, Joana; Ramalho, Miguel; Gianolio, Eliana; Karst, Uwe; Radbruch, Alexander; Stroomberg, Gerard; Clement, Olivier; Dekkers, Ilona A.; Nederveen, Aart J.; Quattrocchi, Carlo C.;

Haase, Robert; Pinetz, Thomas; Kobler, Erich; Paech, Daniel; Effland, Alexander; Radbruch, Alexander; Deike-Hofmann, Katerina

doi: 10.1097/rli.0000000000000963pmid: 36822654

Abstract Deep learning approaches are playing an ever-increasing role throughout diagnostic medicine, especially in neuroradiology, to solve a wide range of problems such as segmentation, synthesis of missing sequences, and image quality improvement. Of particular interest is their application in the reduction of gadolinium-based contrast agents, the administration of which has been under cautious reevaluation in recent years because of concerns about gadolinium deposition and its unclear long-term consequences. A growing number of studies are investigating the reduction (low-dose approach) or even complete substitution (zero-dose approach) of gadolinium-based contrast agents in diverse patient populations using a variety of deep learning methods. This work aims to highlight selected research and discusses the advantages and limitations of recent deep learning approaches, the challenges of assessing its output, and the progress toward clinical applicability distinguishing between the low-dose and zero-dose approach.

Hagiwara, Akifumi; Fujita, Shohei; Kurokawa, Ryo; Andica, Christina; Kamagata, Koji; Aoki, Shigeki

doi: 10.1097/rli.0000000000000962pmid: 36822661

Abstract With the recent advancements in rapid imaging methods, higher numbers of contrasts and quantitative parameters can be acquired in less and less time. Some acquisition models simultaneously obtain multiparametric images and quantitative maps to reduce scan times and avoid potential issues associated with the registration of different images. Multiparametric magnetic resonance imaging (MRI) has the potential to provide complementary information on a target lesion and thus overcome the limitations of individual techniques. In this review, we introduce methods to acquire multiparametric MRI data in a clinically feasible scan time with a particular focus on simultaneous acquisition techniques, and we discuss how multiparametric MRI data can be analyzed as a whole rather than each parameter separately. Such data analysis approaches include clinical scoring systems, machine learning, radiomics, and deep learning. Other techniques combine multiple images to create new quantitative maps associated with meaningful aspects of human biology. They include the magnetic resonance g-ratio, the inner to the outer diameter of a nerve fiber, and the aerobic glycolytic index, which captures the metabolic status of tumor tissues.

Gaur, Sonia; Panda, Ananya; Fajardo, Jesus E.; Hamilton, Jesse; Jiang, Yun; Gulani, Vikas

doi: 10.1097/rli.0000000000000975pmid: 37026802

Abstract Magnetic resonance fingerprinting (MRF) is an approach to quantitative magnetic resonance imaging that allows for efficient simultaneous measurements of multiple tissue properties, which are then used to create accurate and reproducible quantitative maps of these properties. As the technique has gained popularity, the extent of preclinical and clinical applications has vastly increased. The goal of this review is to provide an overview of currently investigated preclinical and clinical applications of MRF, as well as future directions. Topics covered include MRF in neuroimaging, neurovascular, prostate, liver, kidney, breast, abdominal quantitative imaging, cardiac, and musculoskeletal applications.

Guo, Jing; Savic, Lynn Jeanette; Hillebrandt, Karl Herbert; Sack, Ingolf

doi: 10.1097/rli.0000000000000971pmid: 36897804

Abstract The mechanical traits of cancer include abnormally high solid stress as well as drastic and spatially heterogeneous changes in intrinsic mechanical tissue properties. Whereas solid stress elicits mechanosensory signals promoting tumor progression, mechanical heterogeneity is conducive to cell unjamming and metastatic spread. This reductionist view of tumorigenesis and malignant transformation provides a generalized framework for understanding the physical principles of tumor aggressiveness and harnessing them as novel in vivo imaging markers. Magnetic resonance elastography is an emerging imaging technology for depicting the viscoelastic properties of biological soft tissues and clinically characterizing tumors in terms of their biomechanical properties. This review article presents recent technical developments, basic results, and clinical applications of magnetic resonance elastography in patients with malignant tumors.

Lell, Michael; Kachelrieß, Marc

doi: 10.1097/rli.0000000000000995pmid: 37378467

Abstract Computed tomography (CT) dramatically improved the capabilities of diagnostic and interventional radiology. Starting in the early 1970s, this imaging modality is still evolving, although tremendous improvements in scan speed, volume coverage, spatial and soft tissue resolution, as well as dose reduction have been achieved. Tube current modulation, automated exposure control, anatomy-based tube voltage (kV) selection, advanced x-ray beam filtration, and iterative image reconstruction techniques improved image quality and decreased radiation exposure. Cardiac imaging triggered the demand for high temporal resolution, volume acquisition, and high pitch modes with electrocardiogram synchronization. Plaque imaging in cardiac CT as well as lung and bone imaging demand for high spatial resolution. Today, we see a transition of photon-counting detectors from experimental and research prototype setups into commercially available systems integrated in patient care. Moreover, with respect to CT technology and CT image formation, artificial intelligence is increasingly used in patient positioning, protocol adjustment, and image reconstruction, but also in image preprocessing or postprocessing. The aim of this article is to give an overview of the technical specifications of up-to-date available whole-body and dedicated CT systems, as well as hardware and software innovations for CT systems in the near future.

Dack, Ethan; Christe, Andreas; Fontanellaz, Matthias; Brigato, Lorenzo; Heverhagen, Johannes T.; Peters, Alan A.; Huber, Adrian T.; Hoppe, Hanno; Mougiakakou, Stavroula; Ebner, Lukas

doi: 10.1097/rli.0000000000000974pmid: 37058321

Abstract Interstitial lung disease (ILD) is now diagnosed by an ILD-board consisting of radiologists, pulmonologists, and pathologists. They discuss the combination of computed tomography (CT) images, pulmonary function tests, demographic information, and histology and then agree on one of the 200 ILD diagnoses. Recent approaches employ computer-aided diagnostic tools to improve detection of disease, monitoring, and accurate prognostication. Methods based on artificial intelligence (AI) may be used in computational medicine, especially in image-based specialties such as radiology. This review summarises and highlights the strengths and weaknesses of the latest and most significant published methods that could lead to a holistic system for ILD diagnosis. We explore current AI methods and the data use to predict the prognosis and progression of ILDs. It is then essential to highlight the data that holds the most information related to risk factors for progression, e.g., CT scans and pulmonary function tests. This review aims to identify potential gaps, highlight areas that require further research, and identify the methods that could be combined to yield more promising results in future studies.

Pieper, Claus C.

doi: 10.1097/rli.0000000000000966pmid: 37058335

Abstract Lymphatic imaging and interventional therapies of disorders affecting the lymphatic vascular system have evolved rapidly in recent years. Although x-ray lymphangiography had been all but replaced by the advent of cross-sectional imaging and the scientific focus shifted to lymph node imaging (eg, for detection of metastatic disease), interest in lymph vessel imaging was rekindled by the introduction of lymphatic interventional treatments in the late 1990s. Although x-ray lymphangiography is still the mainstay imaging technique to guide interventional procedures, several other, often less invasive, techniques have been developed more recently to evaluate the lymphatic vascular system and associated pathologies. Especially the introduction of magnetic resonance, and even more recently computed tomography, lymphangiography with water-soluble iodinated contrast agent has furthered our understanding of complex pathophysiological backgrounds of lymphatic diseases. This has led to an improvement of treatment approaches, especially of nontraumatic disorders caused by lymphatic flow abnormalities including plastic bronchitis, protein-losing enteropathy, and nontraumatic chylolymphatic leakages. The therapeutic armamentarium has also constantly grown and diversified in recent years with the introduction of more complex catheter-based and interstitial embolization techniques, lymph vessel stenting, lymphovenous anastomoses, as well as (targeted) medical treatment options. The aim of this article is to review the relevant spectrum of lymphatic disorders with currently available radiological imaging and interventional techniques, as well as the application of these methods in specific, individual clinical situations.