Body Computed Tomography Scan Research Articles

Are invasive postmortem examinations still the ‘gold standard’ in diagnosing the cause of death?

For many years, the foundations of both its art and science in medicine grew from the observations made at the autopsy table. The observations of the pathologists formed the pathologic basis and manifestations of disease, which further advanced the development of therapeutics. For most of the 18th and 19th centuries, morbid anatomy (autopsy pathology) was considered the science of medicine. Today, the autopsy and the knowledge that derives from it do not hold the position they once did in the profession’s history. There is a belief among the medical community a complete autopsy is necessary to find out the answers when someone dies. In some jurisdictions, the forensic pathology community is reluctant to follow the targeted or minimally invasive approach in postmortem examinations due to a lack of understanding of the process. With a complete external examination, trace evidence collection, total body computed tomography (CT) scan, and minimally invasive/targeted dissection with a collection of samples for further analysis, almost all answers can be provided in hospital and medicolegal autopsy settings. We must collaborate with sister disciplines, such as experts in radiology and the legal community, to educate them on the importance of this new approach using advanced technology.Continuously evolving modern radiological imaging has remarkably increased the accuracy of clinical diagnosis. Conventionally, when you hear the word postmortem examination, people think it includes an external examination and dissection of all body cavities. In the 21st century, most questions raised by the family, clinicians, the coroner (medical examiner), courts, and law enforcement agencies can be answered with a complete external examination of the body, total body CT scan +/- magnetic resonance imaging, and targeted dissection or minimally invasive approach incorporated with sample collection for further testing. This is cost-effective and can produce reviewable data.

Disseminated Tuberculosis Revealed by Conus Medullaris Tuberculoma

A 29-year-old man from Comoros presented with rapidly progressive paraplegia and sexual dysfunction. Magnetic resonance imaging (MRI) showed a contrast-enhanced conus medullaris lesion. Differential diagnoses included tumors, abscesses, and inflammatory diseases. Neurosurgery was delayed to complete examinations. Cerebral MRI showed three abscesses. Body computed tomography scan showed supracentimetric polyadenopathies, pulmonary nodules, prostatic lesion, and enhanced seminal vesicle, with hypermetabolism on positron emission tomography with 2-deoxy-2-[fluorine-18]fluoro-D-glucose scan. Histology of lymph node biopsy showed granulomatous infiltration without acid-fast bacilli, and positive polymerase chain reaction for Mycobacterium tuberculosis. Lymph node culture was positive after 2 months, urine culture after 3 weeks, but cerebrospinal fluid and sputum cultures were negative. A 1-year antituberculosis therapy was initiated, associated with corticosteroids because the patient developed tuberculosis-immune reconstitution syndrome, revealed by the recurrence of neurological symptoms. After 2 months the patient completely recovered and could run. MRI showed stability of the voluminous tuberculoma with decrease of medullary edema. Avoiding surgery in those cases may prevent iatrogenic neurological deterioration.

Canine preclinical safety evaluation of a multimodal nanoparticle agent (Nanotrast-CF800) for image-guided cancer surgery.

Surgical oncology often requires the use of contrast-enhanced cross-sectional imaging preoperatively to characterize solitary tumours and identify sentinel lymph nodes. Intraoperative optical guidance can effectively aid tissue-sparing tumour excision and locate sentinel lymph nodes. Nanotrast-CF800 (CF800) is a novel dual-modality contrast agent, which co-encapsulates iohexol and indocyanine green (ICG) within a liposomal nanoparticle. It was developed for preoperative and intraoperative imaging of solitary tumours and sentinel lymph node mapping and its efficacy has been demonstrated in preclinical animal models (Zheng et al. 2015). The objective of this study is to evaluate the safety profile of CF800 following intravenous administration in healthy dogs. Six research dogs were randomized into two groups. Group 1 received a low dose (1 mL/kg) and group 2 received a high dose (5 mL/kg). Dogs were placed under general anesthesia and a continuous rate infusion of CF800 was administered based on group allocation. Physiologic parameters including heart rate, respiratory rate, direct arterial blood pressure, cardiac output, and temperature were measured at set time points. Plasma concentrations of iohexol, ICG, and histamine were measured at set time points. Dogs underwent whole body computed tomography scans pre-injection, 2-, and 7-days post-injection (p.i.). Contrast enhancement was measured in select organ systems and great vessels at each time point. There were no significant changes in physiologic parameters following IV infusion of CF800 in all dogs. Plasma iohexol and ICG concentrations peaked at 1 day p.i., while histamine concentrations peaked at 30 minutes p.i. Significant contrast enhancement was noted within the liver, heart, aorta, and caudal vena cava on day 2 p.i., which was significantly different compared to baseline. Prolonged contrast retention within the liver was identified. Intravenous administration of CF800 was safe to use in healthy dogs with no significant systemic adverse effects.

Differentiating age and sex in vertebral body CT scans – Texture analysis versus deep learning approach

The automated analysis of computed tomography (CT) scans of vertebrae, for the purpose of determining an individual's age and sex constitutes a vital area of research. Accurate assessment of bone age in children facilitates the monitoring of their growth and development. Moreover, the determination of both age and sex has significant relevance in various legal contexts involving human remains. We have built a dataset comprising CT scans of vertebral bodies from 166 patients of diverse genders, acquired during routine cardiac examinations. These images were rescaled to 8-bit data, and textural features were computed using the qMaZda software. The results were analysed employing conventional machine learning techniques and deep convolutional networks. The regression model, developed for the automatic estimation of bone age, accurately determined patients' ages, with a mean absolute error of 3.14 years and R2 = 0.79. In the context of classifying patient gender through textural analysis supported by machine learning, we achieved an accuracy of 69 %. However, the application of deep convolutional networks for this task yielded a slightly lower accuracy of 59 %.

Hepatic Angiomyolipoma: A Case Report and Review of the Literature

Background. Hepatic angiomyolipomas are uncommon non-cancerous mesenchymal tumors that belong to the perivascular epithelioid cell tumour group. Smooth muscle cells, fat cells and blood vessels can be observed in these types of neoplasm. Case presentation. We present the case of a 50-year-old patient. The patient was referred to the National Cancer Institute after an abdominal ultrasound, which incidentally revealed a mass in the liver. A whole body computed tomography scan showed a large liver mass, in the VII/VIII/V/VI segments. Trisegmenthomy of the right liver lobe was performed. Pathological examination revealed a rare, but benign tumour – angiomyolipoma. The postoperative course was uneventful and the patient was discharged on postoperative day 9. Two months following the surgery, the patient has no recurrence or late complications. Conclusion. Hepatic angiomyolipoma is a rare, usually benign tumour with malignant potential. It is can be diagnosed by radiological tests, biopsy or after surgery. The most common treatment is surgical resection.

Spinal Instability as a Prognostic Factor in Patients With Spinal Metastasis of Castration-resistant Prostate Cancer.

To evaluate the Spinal Instability Neoplastic Score (SINS) for prediction of survival in patients with spinal column metastasis of castration-resistant prostate cancer (CRPC). A retrospective study of spinal instability was performed in patients with CRPC using SINS. Overall survival was evaluated starting from the time of SINS evaluation. The subjects were 42 patients with CRPC among 261 cases diagnosed with metastatic spinal tumors by radiologists, among 42,152 cases that underwent a body computed tomography scan at Kawasaki Medical School Hospital within 32 months from December 2013 to July 2016. The median age was 78 (range=55-91 years), the median prostate-specific antigen (PSA) level at SINS evaluation was 42.1 (0.1-3,121.6) ng/ml, and 11 patients had visceral metastasis. The median periods from diagnosis of bone metastasis and development of CRPC to SINS evaluation were 17 (0-158) and 20 (0-149) months, respectively. The spine was stable in 32 cases (group S) and potentially unstable or unstable in 10 (24%) (group U). The median observation period was 17.5 (0-83) months and 36 patients died. The median survival period after SINS evaluation was longer in group S than that in group U (20 vs. 10 months, p=0.0221). In multivariate analysis, PSA level, visceral metastasis, and spinal instability were significant prognostic factors. The hazard ratio for patients in group U was 2.60 (95%CI=1.07-5.93, p=0.0345). Spinal stability evaluated using SINS is a new prognostic factor for survival of patients with spinal metastasis of CRPC.

Compliance With the Royal College of Radiologists Guideline for Actionable Reporting and Its Impact on Patient Care: A Retrospective Analysis of Reporting Practices From a Major Trauma Center.

Introduction Prompt diagnosis forms the mainstay of management of any patient arriving at the hospital. In developed settings, apart from clinical assessment, imaging in the form of computed tomography (CT) scan plays a vital role in arriving at the patient diagnosis. The reporting should follow pre-defined Royal College of Radiologists (RCR) standards to improve the quality of the diagnostic process. Objectives To identify the compliance of reporting as per the RCR standards for the communication of radiological reports and fail-safe alert notification. Materials and methods A retrospective review of body CT scans was done in two cycles within a span of three months. A total of 100 randomized scans were assessed in each cycle, both from the A&E (accident and emergency) and inpatients. Normal scans and outpatient scans were excluded from the study. Data were collected using the online portal (CRIS) and statistical analysis was performed. Results After the first cycle of the audit, 95 reports out of 100 met the standard RCR criteria. After the second cycle, 97 reports met the criteria of the audit. Oneinpatient scan and twoA&E reports did not meet the specified criteria in the second cycle. Conclusion After the two cycles of the audit carried out over three months, we were able to achieve almost 97% of reporting standards as compared to 95% obtained previously through a quality improvement project and create awareness.

A Rare Cause of Sister Mary Joseph Nodule

A 67-year-old man with hepatitis B–related cirrhosis received liver transplantation because of hepatic decompensation, and small hepatomas were found in the resected liver. The postoperative course was uneventful, but the α-fetoprotein level rose from 5 ng/mL to 107 ng/mL 1 year after transplantation. Whole body computed tomography scans failed to find any recurrent tumor. The α-fetoprotein level continued to increase, and was 413.2 ng/mL 18 months after transplantation. An abdominal computed tomography revealed an enhancing lesion over the umbilical area (Figure A, arrow). An abdominal ultrasound revealed a hypoechoic tumor in the subcutaneous area with increased blood flow (Figure B, arrow). Ultrasound-guided biopsy revealed atypical oval to epithelioid-shaped tumor cells with high N/C ratio, eosinophilic cytoplasm, and hyperchromatic nuclei (Figure C). Immunohistochemical studies demonstrated that the tumor cells were glypican-3(+), Hep-par-1(+), CK19(-), and CK7(-). A diagnosis of umbilical metastatic hepatocellular carcinoma was made (Figure D). He received surgical resection followed by sorafenib therapy. He had no evidence of recurrence in the following 6 months.

Calculation of scan length and size-specific dose at longitudinal positions of body CT scans using dose equilibrium function.

Dose evaluation at longitudinal positions of body computed tomography (CT) scans is useful for CT quality assurance programs and patient organ dose evaluation. Accurate estimates depend on both patient size and scan length. To propose practical evaluation of the average dose to the transverse slab of an axial image slice for adult body CT examinations, considering not only patient size but also scan length, and to compare the results with those of Monte Carlo (Geant4) simulation [Dsim (z)] and size-specific dose estimates at longitudinal positions of scans [SSDE(z)] from international standards (IEC publication no. 62985). In a scan series, the total dose at each z-axis location was calculated using the input information identical to the SSDE(z) evaluation. Each axial image slice (slice thickness, 2.5 or 5mm) was first considered independently. Its z-axis coverage and CTDIvol (from the DICOM headers) were used to directly calculate a z-axis dose profile for the average dose over the cross-section of a water phantom, using the approach to equilibrium function. The phantom diameter was taken to be equal to the patient water equivalent diameter at that slice. The above was repeated at all slices and the dose at each z-axis location was accumulated from all profiles, referred to as Dcalc (z). For validation, we considered a cohort of 65 patients, who underwent chest and abdominopelvic examinations. The resultant Dcalc (z) was compared with Dsim (z) and SSDE(z), both available in a previous paper. Dcalc (z) evaluation could be used to accurately assess the scan range average dose, with an accuracy of 7.1%-8.7% for 65 patients in two examinations. On individual image slices, the maximum difference in magnitude between Dcalc (z) and Dsim (z) [and between SSDE(z) and Dsim (z) in parentheses] was 37.5% (85%) [two edges (2×5cm) of chest scan range], 17.8% (35.2%) (the remaining central region of chest scan), 26.8% (74.1%) [two edges (2×5cm) of abdominopelvic scan range], and 14.2% (22.5%) (the remaining central region of abdominopelvic scan). Identical input data are used for Dcalc (z) and SSDE(z) evaluations. The latter is limited to the z-axis locations within scan range. At each image slice, SSDE(z) is equivalent to the midpoint dose of a fixed-mA scan of 15-30cm (scan length). In contrast, Dcalc (z) considers dose accumulation from varying scan length (from sub-centimeter to about 1m) and tube current, and dose profile is also computed outside scan range. Besides greatly improving dose evaluation for individual image slices, Dcalc (z) allows for evaluating dose accumulation from multiple series, which typically span different scan ranges. Our proposal may assist CT manufacturers and dose index monitoring software in assessing dose at longitudinal positions of body CT scans.

64-Year-Old Woman With Aphasia and Troponin Elevation

64-Year-Old Woman With Aphasia and Troponin Elevation

Sarcopenia and Myosteatosis Are Associated with Neutrophil to Lymphocyte Ratio but Not Glasgow Prognostic Score in Colorectal Cancer Patients.

Cancer patients commonly present sarcopenia, myosteatosis, and systemic inflammation, which are risk factors of poor survival. In this study, sarcopenia and myosteatosis were defined from preoperative body computed tomography scans of 222 colorectal cancer (CRC) patients and analyzed in relation to tumor and patient characteristics, markers of systemic inflammation (modified Glasgow prognostic score (mGPS), neutrophil–lymphocyte ratio (NLR), serum levels of C-reactive protein (CRP), albumin, and 13 cytokines, and survival. Of the systemic inflammation markers, sarcopenia and/or myosteatosis associated with elevated NLR (p = 0.005) and low albumin levels (≤35 g/L) (p = 0.018), but not with mGPS or serum cytokine levels. In addition, myosteatosis was associated with a proximal tumor location (p = 0.039), serrated tumor subtype (p < 0.001), and severe comorbidities (p = 0.004). Multivariable analyses revealed that severe comorbidities and serrated histology were independent predictors of myosteatosis, and older age and elevated NLR were independent indicators of sarcopenia. Myosteatosis associated with shorter overall survival in univariable analysis (HR 1.959, 95% CI 1.24–3.10, p = 0.004) but not in multivariable analysis (p = 0.075). We conclude that sarcopenia and myosteatosis were associated with inflammatory marker NLR, but not with mGPS. Moreover, patients with serrated CRC may have an increased risk of myosteatosis. Myosteatosis or sarcopenia were not independent predictors of patient survival.

Evaluation of radiation dose to pediatric models from whole body PET/CT imaging.

Positron emission tomography (PET)/computed tomography (CT) is a well‐known modality for the diagnosis of various diseases in children and adult patients. On the other hand, younger patients are more radiosensitive than adults, so there are concerns about the level of ionizing radiation exposure in pediatric whole body PET/CT imaging. In this regard, comprehensive specific radiation dosimetry for whole body PET/CT imaging is highly desired for different ages, sizes, and shapes. Therefore, in this study, organ absorbed doses were evaluated for pediatric voxel models from 4 to 14 years old and compared with those of ICRP phantoms. Monte Carlo calculation was performed to evaluate S‐value, absorbed dose, and effective dose from 18F‐FDG radiotracers and whole body CT scan for different computational models, including 4‐ to 14‐year‐old phantoms. The results showed that the S‐value and, therefore, absorbed dose of 18F‐FDG strongly depended on the phantom anatomy. These variations were justified by the distance between source and target organs. Moreover, on average, the absorbed doses from whole body CT scans were 13.5 times lower than those from 18F‐FDG for all organs. According to the results, various anatomies and ages should be considered for accurate dose evaluation. These data can be used for specific risk assessment of the pediatric population in clinical nuclear imaging.

Developing the Minimum Dataset for the New Mexico Decedent Image Database.

A minimum dataset (MDS) can be determined ad hoc by an investigator or small team; by a metadata expert; or by using a consensus method to take advantage of the global knowledge and expertise of a large group of experts. The first method is the most commonly applied. Here, we describe a use of the third approach using a modified Delphi method to determine the optimal MDS for a dataset of full body computed tomography scans. The scans are of decedents whose deaths were investigated at the New Mexico Office of the Medical Investigator and constitute the New Mexico Decedent Image Database (NMDID). The authors initiated the consensus process by suggesting 50 original variables to elicit expert reactions. Experts were recruited from a variety of scientific disciplines and from around the world. Three rounds of variable selection showed high rates of consensus. In total, 59 variables were selected, only 52% of which the original resource authors selected. Using a snowball method, a second set of experts was recruited to validate the variables chosen in the design phase. During the validation phase, no variables were selected for deletion. NMDID is likely to remain more "future proof" than if a single metadata expert or only the original team of investigators designed the metadata.

Realistic High-Resolution Body Computed Tomography Image Synthesis by Using Progressive Growing Generative Adversarial Network: Visual Turing Test.

BackgroundGenerative adversarial network (GAN)–based synthetic images can be viable solutions to current supervised deep learning challenges. However, generating highly realistic images is a prerequisite for these approaches.ObjectiveThe aim of this study was to investigate and validate the unsupervised synthesis of highly realistic body computed tomography (CT) images by using a progressive growing GAN (PGGAN) trained to learn the probability distribution of normal data.MethodsWe trained the PGGAN by using 11,755 body CT scans. Ten radiologists (4 radiologists with <5 years of experience [Group I], 4 radiologists with 5-10 years of experience [Group II], and 2 radiologists with >10 years of experience [Group III]) evaluated the results in a binary approach by using an independent validation set of 300 images (150 real and 150 synthetic) to judge the authenticity of each image.ResultsThe mean accuracy of the 10 readers in the entire image set was higher than random guessing (1781/3000, 59.4% vs 1500/3000, 50.0%, respectively; P<.001). However, in terms of identifying synthetic images as fake, there was no significant difference in the specificity between the visual Turing test and random guessing (779/1500, 51.9% vs 750/1500, 50.0%, respectively; P=.29). The accuracy between the 3 reader groups with different experience levels was not significantly different (Group I, 696/1200, 58.0%; Group II, 726/1200, 60.5%; and Group III, 359/600, 59.8%; P=.36). Interreader agreements were poor (κ=0.11) for the entire image set. In subgroup analysis, the discrepancies between real and synthetic CT images occurred mainly in the thoracoabdominal junction and in the anatomical details.ConclusionsThe GAN can synthesize highly realistic high-resolution body CT images that are indistinguishable from real images; however, it has limitations in generating body images of the thoracoabdominal junction and lacks accuracy in the anatomical details.

Body Part Regression With Self-Supervision.

Body part regression is a promising new technique that enables content navigation through self-supervised learning. Using this technique, the global quantitative spatial location for each axial view slice is obtained from computed tomography (CT). However, it is challenging to define a unified global coordinate system for body CT scans due to the large variabilities in image resolution, contrasts, sequences, and patient anatomy. Therefore, the widely used supervised learning approach cannot be easily deployed. To address these concerns, we propose an annotation-free method named blind-unsupervised-supervision network (BUSN). The contributions of the work are in four folds: (1) 1030 multi-center CT scans are used in developing BUSN without any manual annotation. (2) the proposed BUSN corrects the predictions from unsupervised learning and uses the corrected results as the new supervision; (3) to improve the consistency of predictions, we propose a novel neighbor message passing (NMP) scheme that is integrated with BUSN as a statistical learning based correction; and (4) we introduce a new pre-processing pipeline with inclusion of the BUSN, which is validated on 3D multi-organ segmentation. The proposed method is trained on 1,030 whole body CT scans (230,650 slices) from five datasets, as well as an independent external validation cohort with 100 scans. From the body part regression results, the proposed BUSN achieved significantly higher median R-squared score (=0.9089) than the state-of-the-art unsupervised method (=0.7153). When introducing BUSN as a preprocessing stage in volumetric segmentation, the proposed pre-processing pipeline using BUSN approach increases the total mean Dice score of the 3D abdominal multi-organ segmentation from 0.7991 to 0.8145.

Should We Report Incidental Low-Density Liver Lesions with Benign Features? A Retrospective Single-Center Analysis of Trauma CT Scans

Abstract Background Many incidental liver lesions are benign and require no additional workup. Investigation of such lesions can have a negative impact of both the patient and health care system. However, the impact of how radiologists report these incidental lesions is not clear. We aimed to investigate how reporting of incidental liver lesions on trauma computed tomography (CT) scan affects follow-up. Methods This is a retrospective single-center analysis of body CT scans performed following abdominal trauma. Information was collected on the reporting of incidental low-density liver lesions and any additional imaging performed. Results A total of 3,595 trauma body CT scan reports were reviewed. Incidental liver lesions were identified in 527 (15%) patients, with 347 (10%) fulfilling the inclusion criteria. Additional imaging was requested by the referring doctor for 43 out of 285 patients (15%) when lesions were mentioned in the body of the report only, compared with 41 out of 62 patients (66%) when mentioned in the conclusion (odds ratio [OR] = 10.99, p &lt; 0.0001). When additional imaging was recommended in the report, follow-up was arranged for 36 out of 52 patients (69%), compared with 48 out of 285 patients (16%) when it was not suggested (OR = 11.58, p &lt; 0.0001). Additional imaging was requested for 84 of the 347 patients (24%), with 24 of these performed at our institution. All patients followed-up at our institution were diagnosed with a benign lesion. Conclusion Reporting incidental hypodense liver lesions in the conclusion or specifically recommending further additional imaging, both led to significantly increased likelihood of additional imaging being performed. Radiologists who encounter such lesions should consider excluding them from the conclusion if there are no malignant features or patient risk factors.

Ruptured Epiploic Artery Aneurysm Associated With Fibromuscular Dysplasia

A 69 year old woman presented with severe abdominal pain and haemodynamic instability. Computed tomography (CT) angiography demonstrated multiple ectasia of the visceral arteries and a massive haemoperitoneum. An exploratory laparotomy was performed. Multiple epiploic artery aneurysms with a string of beads aspect were found (A), one of which had ruptured. Total omentectomy was performed, and she was discharged from hospital after one week. Histopathological analysis of the epiploic arteries indicated fibromuscular dysplasia with intimal thickening (B, red asterisk) with no lesion of the vein (B, blue asterisk). Annual surveillance with a whole body CT scan has been arranged.Image 1

Deep learning method for localization and segmentation of abdominal CT

Computed Tomography (CT) imaging is widely used for studying body composition, i.e., the proportion of muscle and fat tissues with applications in areas such as nutrition or chemotherapy dose design. In particular, axial CT slices from the 3rd lumbar (L3) vertebral location are commonly used for body composition analysis. However, selection of the third lumbar vertebral slice and the segmentation of muscle/fat in the slice is a tedious operation if performed manually. The objective of this study is to automatically find the middle axial slice at L3 level from a full or partial body CT scan volume and segment the skeletal muscle (SM), subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT) and intermuscular adipose tissue (IMAT) on that slice. The proposed algorithm includes an L3 axial slice localization network followed by a muscle-fat segmentation network. The localization network is a fully convolutional classifier trained on more than 12,000 images. The segmentation network is a convolutional neural network with an encoder–decoder architecture. Three datasets with CT images taken for patients with different types of cancers are used for training and validation of the networks. The mean slice error of 0.87±2.54 was achieved for L3 slice localization on 1748 CT scan volumes. The performance of five class tissue segmentation network evaluated on two datasets with 1327 and 1202 test samples. The mean Jaccard score of 97% was achieved for SM and VAT tissue segmentation on 1327 images. The mean Jaccard scores of 98% and 83% are corresponding to SAT and IMAT tissue segmentation on the same dataset. The localization and segmentation network performance indicates the potential for fully automated body composition analysis with high accuracy.

Primary Ewing\u2019s sarcoma in a small intestine \u2013 a case report and review of the literature

BackgroundEwing’s sarcoma usually presents in paediatric patients with its primary location being bone tissue. Nevertheless, we present such an adult case which arises from the small intestine. We registered thirty one cases of such origin published so far excluding ours.Case presentationWe report a case of 30 year old female who was admitted due to the persistent anaemia. Whole body computed tomography scan revealed abdominal mass in her left upper abdominal compartment. Surgery on the mass originating from jejunum was performed, although due to extremely complicated postoperative period and rapid dissemination no additional therapy had been performed. The tumour was positive for CD99, ERG, CD56, Synaptophysin, PanCK, Cam5.2.ConclusionExtraosseus Ewing’s sarcoma is extremely rare entity, with poor prognosis.

Using Virtual Reality to Study Real Patient Anatomy

Utilizing virtual reality (VR) is an excellent way to learn anatomy. The user can visualize different anatomical structures from all angles (360 degrees) allowing for understanding the relative 3D distances between various structures, and overall interacts with the anatomies within an immersive environment. Other benefits of VR include reduced uses of cadaver donors and animal models, as well as a more engaging learning experience than a textbook. Today, some medical schools have already incorporated VR into their curriculum. However, the bodies for study are often idealized models of “perfected” anatomy. Bones, organs, and vessels are typically depicted as smooth, straight anatomies that only represent a healthy human patient population. In the Visible Heart® Laboratories, we have developed several VR tutorials based on real patient anatomies via whole body scans from donated individuals to the Anatomy Bequest Program at the University of Minnesota. This allows the user to “fly” around the bodies of these patients with varying demographics and disease states. This allows for better understanding the patient comorbidities, conditions, and potentially techniques on how to treat patients as a whole.The 3D renderings in the VR scenes were specifically modeled from full body computed tomography (CT) scans of these fresh human cadavers. To highlight the vessels in the imaging, the bilateral carotid arteries, jugular veins, and femoral arteries and veins were cannulated and ~8 liters of radiopaque contrast was manually injected to the body. Each full body CT scan was performed allowing for &lt;600μm resolution. A similar protocol was repeated with a 3T MRI of the thoracic cavity for better visualizations of the thoracic and abdominal cavities. These DICOM datasets were then uploaded into Materialise Mimics software where bones, arteries, veins, lungs, and organs, were individually segmented and rendered into 3D models. These were next imported into Unity, a free video game development platform.When any user wears the HTC Vive VR headset and holds the controller, they are able to “fly” through these real patients’ anatomies (Figure 1). These bodies have been digitally preserved through these DICOM datasets and computational models. Relevant medical history, including disease states and device implants, can also be studied. So far, two full body specimens have been reconstructed in VR – a 60 y/o white male with history of congestive heart failure and a brain tumor and a 76 y/o white male with history of chronic obstructive pulmonary disease. Additionally, we have used VR to study cadaver vasculatures (arteries and veins) to gain an understanding of how the insertion of transcatheter devices affects the vessels and surrounding tissues. Future anatomical educational tools in development include a multi‐user VR environments with 3D glasses (Figure 2), as well as a portable VR system which can be run on a phone or tablet.VR scene created from real human patient anatomies. Cadaver specimens were both CT and MRI scanned to create 3D models. These were imported into VR scenes and color coded to represent different anatomical structures, including the lungs, arteries, veins, bones, and other organs. The left is the view of the body from above and the right is the view from within the liver. Note again, the hepatic vasculature can be visualized in any desired orientation.Figure 1Students in a large lecture setting demonstrating the multi‐user VR capability with 3D glasses.Figure 2