Computed Tomography Research
The Institute of Diagnostic and Interventional Radiology is one of the world's leading centers for research in the field of computed tomography. The research focus in this field is especially on spectral computed tomography (short: SDCT or spectral CT). The group evaluates and tests technical improvements and new analysis methods from this new method. In addition, it strives for translation, i.e. the transfer of scientific findings into patient care.
Research Areas
Advancing CT Imaging: Radiation and Contrast Dose, Image Quality
Spectral CT allows for a drastic reduction of the radiation dose by significantly improving the image contrast using special image reconstructions. Similarly, the amount of contrast agent applied can also be reduced using spectral CT. In addition to the improvement of image contrast, Spectral CT also allows for the reduction of image artifacts, which are often found in orthopedic and dental implants and thus limit the diagnostic value of CT examinations. A special role is attributed to the head-to-head comparison of different approaches to dual energy CT, for this purpose research cooperations are established, e.g. with the Massachusetts General Hospital of the Harvard Medical School (Dr. S. Lennartz, Dr. A. Kambadakone).
Große Hokamp N, Gilkeson R, Jordan MK, …, and Gilkeson R (2019) Virtual monoenergetic images from spectral detector CT as a surrogate for conventional CT images: Unaltered attenuation characteristics with reduced image noise. Eur J Radiol 117:49–55. doi: 10.1016/j.ejrad.2019.05.019
Reimer RP, Flatten D, Lichtenstein T, …, and Große Hokamp N (2019) Virtual monoenergetic images from spectral detector CT enable radiation dose reduction in unenhanced cranial CT. AJNR Am J Neuroradiol 40:1617–1623. doi: 10.3174/ajnr.A6220
Große Hokamp N, Neuhaus V, Abdullayev N, …, and Borggrefe J (2017) Reduction of artifacts caused by orthopedic hardware in the spine in spectral detector CT examinations using virtual monoenergetic image reconstructions and metal-artifact-reduction algorithms. Skeletal Radiol 47:1–7. doi: 10.1007/s00256-017-2776-5
Laukamp KR, Lennartz S, Neuhaus V-FF, …, and Borggrefe J (2018) CT metal artifacts in patients with total hip replacements: for artifact reduction monoenergetic reconstructions and post-processing algorithms are both efficient but not similar. Eur Radiol 28:4524–4533. doi: 10.1007/s00330-018-5414-2
Große Hokamp N, Hellerbach A, Gierich A, …, and Haneder S (2018) Reduction of Artifacts Caused by Deep Brain Stimulating Electrodes in Cranial Computed Tomography Imaging by Means of Virtual Monoenergetic Images, Metal Artifact Reduction Algorithms, and Their Combination. Invest Radiol 53:424–431. doi: 10.1097/RLI.0000000000000460
Große Hokamp N, Eck B, Siedek F, …, and Haneder S (2020) Quantification of metal artifacts in computed tomography: methodological considerations. Quant Imaging Med Surg 10:1033–1044. doi: 10.21037/qims.2020.04.03
Zopfs D, Laukamp KR, Pinto dos Santos D, …, and Lennartz S 2019) Low-keV virtual monoenergetic imaging reconstructions of excretory phase spectral dual-energy CT in patients with urothelial carcinoma: A feasibility study. Eur J Radiol 116:135–143. doi: 10.1016/j.ejrad.2019.05.003
Iodine maps: Quantitative biomarker from spectral CT
Computed tomography (CT) is one of the most important imaging techniques for visualizing tumors in the chest or abdomen; iodine-containing contrast medium is often injected via a vein in the arm to improve image quality. Length measurements (in cm) are usually taken to understand the extent of tumor burden. A major problem with this approach is that many tumors swell with therapy - similar to a mosquito bite being scratched - and this can mask a response to therapy. Modern CT devices also allow tumor perfusion to be determined by measuring the iodine concentration after administration of iodine-containing contrast media (so-called iodine maps). This can provide valuable additional information regarding the activity or vitality of a tumor. This information can be obtained without increasing risk or radiation exposure for any CT examination and represents a promising follow-up parameter for tumor imaging.
The accuracy of such iodine maps has been explored and demonstrated in phantom and test measurements to date. In order to bring this technique to the clinic, it is important to understand whether the results and measurements are reliable in humans. These investigations are the focus of the working group. In a final project step, the iodine maps will be used specifically for tumor progression imaging to understand their utility for tumor disease therapy monitoring.
Zopfs D, Reimer RP, Sonnabend K, ..., and Große Hokamp N (2020) Intraindividual Consistency of Iodine Concentration in Dual-Energy Computed Tomography of the Chest and Abdomen. Invest Radiol Publish Ah:181–187. doi: 10.1097/RLI.0000000000000724
Zopfs D, Graffe J, Reimer RP, ..., and Große Hokamp N (2020) Quantitative distribution of iodinated contrast media in body computed tomography: data from a large reference cohort. Eur Radiol. doi: 10.1007/s00330-020-07298-3
Große Hokamp N, Abdullayev N, Persigehl T, ..., and Haneder S (2019) Precision and reliability of liver iodine quantification from spectral detector CT: evidence from phantom and patient data. Eur Radiol 29:2098–2106. doi: 10.1007/s00330-018-5744-0
Lennartz S, Täger P, Zopfs D, Große Hokamp N, ..., and Persigehl T (2021) Lymph Node Assessment in Prostate Cancer: Evaluation of Iodine Quantification With Spectral Detector CT in Correlation to PSMA PET/CT. Clin Nucl Med 46:303–309. doi: 10.1097/RLU.0000000000003496
Große Hokamp N, Persigehl T (2019) Applications of Dual Energy Computed Tomography in Oncologic Imaging. Cancer Imaging (Proceedings 19th ICIS Meet Teach Course) 19:62. doi: 10.1186/s40644-019-0244-2
Lennartz S, Zopfs D, Abdullayev N, ..., and Persigehl T (2020) Iodine overlays to improve differentiation between peritoneal carcinomatosis and benign peritoneal lesions. Eur Radiol. doi: 10.1007/s00330-020-06729-5
Holz JA, Alkadhi H, Laukamp KR, ..., and Große Hokamp N (2020) Quantitative accuracy of virtual non ‑ contrast images derived from spectral detector computed tomography : an abdominal phantom study. Sci Rep 1–8. doi: 10.1038/s41598-020-78518-5
Lennartz S, Le Blanc M, Zopfs D, ..., and Persigehl T (2019) Dual-Energy CT-derived Iodine Maps: Use in Assessing Pleural Carcinomatosis. Radiology 290:796–804. doi: 10.1148/radiol.2018181567
Body Composition Analysis
Every CT scan performed in routine clinical practice also provides information about the muscle and fat masses of patients, which is usually not considered further. These information can be used "opportunistically" as biomarkers for a wide variety of diseases. A prominent example is the influence of body composition on overall survival in patients with esophageal cancer. The aim is to develop reliable methods for determining body composition from opportunistically obtained data and to correlate body composition with clinical parameters. In addition to modern image reconstructions from spectral CT, artificial intelligence methods are also used. A specific focus of research in this group lies in the impact of body composition in patients with neurological diseases.
Zopfs D, Rinneburger M, Pinto Dos Santos D, ..., and Große Hokamp N (2021) Evaluating anemia using contrast-enhanced spectral detector CT of the chest in a large cohort of 522 patients. Eur Radiol 31:4350–4357. doi: 10.1007/s00330-020-07497-y
Zopfs D, Bousabarah K, Lennartz S, ..., and Große Hokamp N (2020) Evaluating body composition by combining quantitative spectral detector computed tomography and deep learning-based image segmentation. Eur J Radiol 130:109153. doi: 10.1016/j.ejrad.2020.109153
Zopfs D, Theurich S, Große Hokamp N, ..., and Pinto dos Santos D (2019) Single-slice CT measurements allow for accurate assessment of sarcopenia and body composition. Eur Radiol 30:1701–1708. doi: 10.1007/s00330-019-06526-9
Zopfs D, Lennartz S, Zäeske C, ..., and Große Hokamp N (2020) Phantomless assessment of volumetric bone mineral density using virtual non-contrast images from spectral detector computed tomography. Br J Radiol 93:20190992. doi: 10.1259/bjr.20190992
Innovative imaging in kidney stone disease
Imaging of kidney stones plays an important role in the diagnosis, course assessment and treatment decision for patients with (suspected) kidney stone disease. The optimal treatment depends on various factors, such as the composition, location and size of the stone. Technical progress in recent decades has enabled a wide range of new applications in this field, while at the same time raising numerous scientific questions. In cooperation with the Department of Urology, our research group is working in particular on improving the determination of stone size and composition using novel techniques such as dual-layer CT and artificial intelligence.
Reimer RP, Salem J, Merkt M, ..., and Große Hokamp N (2020) Size and volume of kidney stones in computed tomography: Influence of acquisition techniques and image reconstruction parameters. Eur J Radiol 132:109267. doi: 10.1016/j.ejrad.2020.109267
Reimer RP, Klein K, Rinneburger M, ..., and Große Hokamp N (2021) Manual kidney stone size measurements in computed tomography are most accurate using multiplanar image reformatations and bone window settings. Sci Rep 11:1–7. doi: 10.1038/s41598-021-95962-z
Nestler T, Haneder S, Grosse Hokamp N (2019) Modern imaging techniques in urinary stone disease. Curr Opin Urol 29:81–88. doi: 10.1097/MOU.0000000000000572
Große Hokamp N, Salem J, Hesse A, ..., and Haneder S (2018) Low-Dose Characterization of Kidney Stones Using Spectral Detector Computed Tomography: An Ex Vivo Study. Invest Radiol 53:457–462. doi: 10.1097/RLI.0000000000000468
Große Hokamp N, Lennartz S, Salem J, ..., and Haneder S (2019) Dose independent characterization of renal stones by means of dual energy computed tomography and machine learning: an ex-vivo study. Eur Radiol. doi: 10.1007/s00330-019-06455-7
Bone Marrow Imaging using Spectral CT and Artificial Intelligence
While bone is well assessable using routine CT imaging, assessing the bone marrow is considered a domain of MRI imaging. We address this shortcoming by combining virtual non-calcium reconstructions (obtained from dual energy CT) with segmentation techniques leveraging convolutional neural networks. Doing so, we aim to better identify metastatic bone and to identify cellular infiltration in multiple myeloma; however, we also strive to improve bone imaging in non-oncologic disease as well (such as BMD assessment).
Fervers P, Celik E, Bratke G, ..., and Große Hokamp N (2021) Radiotherapy Response Assessment of Multiple Myeloma: A Dual-Energy CT Approach With Virtual Non-Calcium Images. Front Oncol 11:1–10. doi: 10.3389/fonc.2021.734819
Fervers P, Fervers F, Kottlors J, ..., and Große Hokamp N (2021) Feasibility of artificial intelligence–supported assessment of bone marrow infiltration using dual-energy computed tomography in patients with evidence of monoclonal protein — a retrospective observational study. Eur Radiol. doi: 10.1007/s00330-021-08419-2
Abdullayev N, Große Hokamp N, Lennartz S, ..., and Borggrefe J (2019) Improvements of diagnostic Accuracy and visualization of vertebral metastasis using multi-level virtual non-calcium reconstructions from dual-layer spectral detector computed tomography. Eur Radiol 29:5941–5949. doi: 10.1007/s00330-019-06233-5
Koch V, Große Hokamp N, Albrecht MH, ..., and Booz C (2021) Accuracy and precision of volumetric bone mineral density assessment using dual-source dual-energy versus quantitative CT: a phantom study. Eur Radiol Exp 5:1–10. doi: 10.1186/s41747-021-00241-1
Zopfs D, Lennartz S, Zäeske C, ..., and Große Hokamp N (2020) Phantomless assessment of volumetric bone mineral density using virtual non-contrast images from spectral detector computed tomography. Br J Radiol 93:20190992. doi: 10.1259/bjr.20190992