Tomography, Volume 4, Issue 4 (December 2018) – 6 articles

Virtual reality (VR) systems can offer benefits of improved ergonomics, but their resolution may currently be limited for the detection of small features. For detection of lung nodules, we compared the performance of VR versus standard picture archiving and communication system (PACS) monitor. Four radiologists and 1 novice radiologist reviewed axial computed tomography (CTs) of the thorax using standard PACS monitors (SM) and a VR system (HTC Vive, HTC). In this study, 3 radiologists evaluated axial lung-window CT images of a Lungman phantom. One radiologist and the novice radiologist reviewed axial lung-window patient CT thoracic images (32 patients). This HIPAA-compliant study was approved by the institutional review board. Detection of 227 lung nodules on patient CTs did not result in different sensitivity with SM compared with VR. Detection of 23 simulated Lungman phantom lung nodules on CT with SM resulted in statistically greater sensitivity (78.3%) than with VR (52.2%, P = 0.041) for 1 of 3 radiologists. The trend was similar but not significant for the other radiologists. There was no significant difference in the time spent by readers reviewing CT images with VR versus SM. These findings indicate that performance of a commercially available VR system for detection of lung nodules may be similar to traditional radiology monitors for assessment of small lung nodules on CTs of the thorax for most radiologists. These results, along with the potential of improving ergonomics for radiologists, are promising for the future development of VR in diagnostic radiology. Full article

Multiple sclerosis (MS) is a progressive neurodegenerative disease, affecting 1 million Americans and 2.5 million people globally. Although the diagnosis is made clinically, imaging plays a major role in diagnosing and monitoring disease progression and treatment response. Magnetic resonance imaging (MRI) has proven sensitive in imaging MS lesions, but the characterization offered by routine clinical MRI remains qualitative and with discrepancies between imaging and clinical findings. We investigated the ability of digital analysis of noncontrast head computed tomography (CT) images to detect global brain changes of MS. All routine diagnostic head CTs obtained on patients with known MS obtained from 1 of 2 scan platforms from 6/1/2011 to 6/1/2015 were reviewed. Head CT images from 54 patients with MS met inclusion criteria. Head CT images were processed and histogram metrics were compared to age- and gender- matched control subjects from the same CT scanners during the same time interval. Histogram metrics were correlated with plaque burden as seen on MRI studies. Compared with control subjects, patients had increased total brain radiodensity (P < 0.0001), further characterized as an increased histogram modal radiodensity (P < 0.0001) with decrease in histogram skewness (P < 0.0001). Radiodensity decreased with increasing plaque burden. Similar findings were seen in the patients with only mild plaque burden sub- group. Radiodensity is a unique tissue metric that is not measured by other imaging techniques. Our study finds that brain radiodensity histogram metrics highly correlate with MS, even in cases with minimal plaque burden. Full article

The novel Gallium-68 prostate-specific membrane antigen (PSMA)-bis [2-hydroxy-5-(carboxyethyl)benzyl] ethylenediamine-diacetic acid positron emission tomography (PET) tracer is increasingly used in the evaluation of prostate cancer, particularly in the detection of recurrent disease. However, PSMA is expressed in nonprostatic tissues, as well as in other pathologic conditions. Here we illustrate such interpretive pitfalls with relevant images that one may encounter while reporting PSMA PET/CT. This study aims to show variation in physiological distribution of PSMA activity and uptake in various benign and neoplastic disorders that may be misinterpreted as prostatic metastatic disease. These pitfalls are illustrated to enhance awareness, aiding a more accurate interpretation of the study. Retrospective database of all (68)Ga PSMA PET/CT was created and reviewed. In total, 1115 PSMA PET/CT studies performed between February 27, 2015, and May 31, 2017, were reviewed. Any unusual uptake of PSMA was documented, described, and followed up. All cases were then subdivided into the following 4 categories: physiological uptake, benign pathological uptake, nonprostatic neoplastic uptake, and miscellaneous uptake. A variety of nonprostatic tissues and lesions, including accessory salivary gland, celiac ganglion, gall bladder, Paget's bone disease, reactive lymph nodes, non–small cell lung cancer, renal cell cancer, and neuroendocrine tumor, were found to show PSMA uptake. PSMA uptake is not prostate-specific and can be taken up physiologically and pathologically in nonprostatic tissue. It is important for reporting physicians to recognize these findings and instigate appropriate investigations when required while avoiding unnecessary procedures in physiological variation. Full article

We investigated multivoxel proton magnetic resonance spectroscopy (¹H-MRS) biometrics for preoperative differentiation and prognosis of patients with brain metastases (MET), low-grade glioma (LGG) and high-grade glioma (HGG). In total, 33 patients (HGG, 14; LGG, 9; and 10 MET) were included. ¹H-MRS imaging (MRSI) data were assessed and neurochemical profiles for metabolites N-acetyl aspartate (NAA) + NAAG(NAA), Cr + PCr(total creatine, tCr), Glu + Gln(Glx), lactate (Lac), myo-inositol(Ins), GPC + PCho(total choline, tCho), and total lipids, and macromolecule (tMM) signals were estimated. Metabolites were reported as absolute concentrations or ratios to tCho or tCr levels. Voxels of interest in an MRSI matrix were labeled according to tissue. Logistic regression, receiver operating characteristic, and Kaplan–Meier survival analysis was performed. Across HGG, LGG, and MET, average Ins/tCho was shown to be prognostic for overall survival (OS): low values (≤1.29) in affected hemisphere predicting worse OS than high values (>1.29), (log rank < 0.007). Lip/tCho and Ins/tCho combined showed 100% sensitivity and specificity for both HGG/LGG (P < .001) and LGG/MET (P < .001) measured in nonenhancing/contrast-enhancing lesional tissue. Combining tCr/tCho in perilesional edema with tCho/tCr and NAA/tCho from ipsilateral normal- appearing tissue yielded 100% sensitivity and 81.8% specificity (P < .002) for HGG/MET. Best single biomarker: Ins/tCho for HGG/LGG and total lipid/tCho for LGG/MET showed 100% sensitivity and 75% and 100% specificity, respectively. HGG/MET; NAA/tCho showed 75% sensitivity and 84.6% specificity. Multivoxel 1H-MRSI provides prognostic information for OS for HGG/LGG/MET and a multibiometric approach for differentiation may equal or outperform single biometrics. Full article

Dynamic glucose-enhanced (DGE) imaging uses chemical exchange saturation transfer magnetic resonance imaging to retrieve information about the microcirculation using infusion of a natural sugar (D-glucose). However, this new approach is not yet well understood with respect to the dynamic tissue response. DGE time curves for arteries, normal brain tissue, and cerebrospinal fluid (CSF) were analyzed in healthy volunteers and compared with the time dependence of sampled venous plasma blood glucose levels. The arterial response curves (arterial input function [AIF]) compared reasonably well in shape with the time curves of the sampled glucose levels but could also differ substantially. The brain tissue response curves showed mainly negative responses with a peak intensity that was of the order of 10 times smaller than the AIF peak and a shape that was susceptible to both noise and partial volume effects with CSF, attributed to the low contrast-to-noise ratio. The CSF response curves showed a rather large and steady increase of the glucose uptake during the scan, due to the rapid uptake of D-glucose in CSF. Importantly, and contrary to gadolinium studies, the curves differed substantially among volunteers, which was interpreted to be caused by variations in insulin response. In conclusion, while AIFs and tissue response curves can be measured in DGE experiments, partial volume effects, low concentration of D-glucose in tissue, and osmolality effects between tissue and blood may prohibit quantification of normal tissue perfusion parameters. However, separation of tumor responses from normal tissue responses would most likely be feasible. Full article

Even as medical data sets become more publicly accessible, most are restricted to specific medical conditions. Thus, data collection for machine learning approaches remains challenging, and synthetic data augmentation, such as generative adversarial networks (GAN), may overcome this hurdle. In the present quality control study, deep convolutional GAN (DCGAN)–based human brain magnetic resonance (MR) images were validated by blinded radiologists. In total, 96 T1-weighted brain images from 30 healthy individuals and 33 patients with cerebrovascular accident were included. A training data set was generated from the T1-weighted images and DCGAN was applied to generate additional artificial brain images. The likelihood that images were DCGAN-created versus acquired was evaluated by 5 radiologists (2 neuroradiologists [NRs], vs 3 non-neuroradiologists [NNRs]) in a binary fashion to identify real vs created images. Images were selected randomly from the data set (variation of created images, 40%–60%). None of the investigated images was rated as unknown. Of the created images, the NRs rated 45% and 71% as real magnetic resonance imaging images (NNRs, 24%, 40%, and 44%). In contradistinction, 44% and 70% of the real images were rated as generated images by NRs (NNRs, 10%, 17%, and 27%). The accuracy for the NRs was 0.55 and 0.30 (NNRs, 0.83, 0.72, and 0.64). DCGAN-created brain MR images are similar enough to acquired MR images so as to be indistinguishable in some cases. Such an artificial intelligence algorithm may contribute to synthetic data augmentation for “data-hungry” technologies, such as supervised machine learning approaches, in various clinical applications. Full article