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Radiomics is a type of quantitative analysis that provides a more objective approach to detecting tumor subtypes using medical imaging. The goal of this paper is to conduct a comprehensive assessment of the literature on computed tomography (CT) radiomics for distinguishing renal cell carcinomas (RCCs) from oncocytoma.From February 15th 2012 to 2022, we conducted a broad search of the current literature using the PubMed/MEDLINE, Google scholar, Cochrane Library, Embase, and Web of Science. A meta-analysis of radiomics studies concentrating on discriminating between oncocytoma and RCCs was performed, and the risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies method. The pooled sensitivity, specificity, and diagnostic odds ratio were evaluated via a random-effects model, which was applied for the meta-analysis. This study is registered with PROSPERO (CRD42022311575).After screening the search results, we identified 6 studies that utilized radiomics to distinguish oncocytoma from other renal tumors; there were a total of 1064 lesions in 1049 patients (288 oncocytoma lesions vs 776 RCCs lesions). The meta-analysis found substantial heterogeneity among the included studies, with pooled sensitivity and specificity of 0.818 [0.619-0.926] and 0.808 [0.537-0.938], for detecting different subtypes of RCCs (clear cell RCC, chromophobe RCC, and papillary RCC) from oncocytoma. Also, a pooled sensitivity and specificity of 0.83 [0.498-0.960] and 0.92 [0.825-0.965], respectively, was found in detecting oncocytoma from chromophobe RCC specifically.According to this study, CT radiomics has a high degree of accuracy in distinguishing RCCs from RO, including chromophobe RCCs from RO. Radiomics algorithms have the potential to improve diagnosis in scenarios that have traditionally been ambiguous. However, in order for this modality to be implemented in the clinical setting, standardization of image acquisition and segmentation protocols as well as inter-institutional sharing of software is warranted.

Upfront knowledge of tumor growth rates of clear cell renal cell carcinoma in von Hippel-Lindau syndrome (VHL) patients can allow for a more personalized approach to either surveillance imaging frequency or surgical planning. In this study, we implement a machine learning algorithm utilizing radiomic features of renal tumors identified on baseline magnetic resonance imaging (MRI) in VHL patients to predict the volumetric growth rate category of these tumors.A total of 73 VHL patients with 173 pathologically confirmed Clear Cell Renal Cell Carcinoma (ccRCCs) underwent MRI at least at two different time points between 2015 and 2021. Each tumor was manually segmented in excretory phase contrast T1 weighed MRI and co-registered on pre-contrast, corticomedullary and nephrographic phases. Radiomic features and volumetric data from each tumor were extracted using the PyRadiomics library in Python (4544 total features). Tumor doubling time (DT) was calculated and patients were divided into two groups: DT = 1 year and DT 1 year. Random forest classifier (RFC) was used to predict the DT category. To measure prediction performance, the cohort was randomly divided into 100 training and test sets (80% and 20%). Model performance was evaluated using area under curve of receiver operating characteristic curve (AUC-ROC), as well as accuracy, F1, precision and recall, reported as percentages with 95% confidence intervals (CIs).The average age of patients was 47.2 ± 10.3 years. Mean interval between MRIs for each patient was 1.3 years. Tumors included in this study were categorized into 155 Grade 2; 16 Grade 3; and 2 Grade 4. Mean accuracy of RFC model was 79.0% [67.4-90.6] and mean AUC-ROC of 0.795 [0.608-0.988]. The accuracy for predicting DT classes was not different among the MRI sequences (P-value = 0.56).Here we demonstrate the utility of machine learning in accurately predicting the renal tumor growth rate category of VHL patients based on radiomic features extracted from different T1-weighted pre- and post-contrast MRI sequences.

Purpose: PANVAC is a recombinant poxviral vaccine that contains transgenes for MUC-1, CEA, and 3 T-cell costimulatory molecules. This study was conducted to obtain preliminary evidence of clinical response in metastatic breast and ovarian cancer patients.Experimental design: Twenty-six patients were enrolled and given monthly vaccinations. Clinical and immune outcomes were evaluated.Results: These patients were heavily pretreated, with 21 of 26 patients having 3 or more prior chemotherapy regimens. Side effects were largely limited to mild injection-site reactions. For the 12 breast cancer patients enrolled, median time to progression was 2.5 months (1–37+) and median overall survival was 13.7 months. Four patients had stable disease. One patient had a complete response by RECIST and remained on study for 37 months or more, with a significant drop in serum interleukin (IL)-6 and IL-8 by day 71. Another patient with metastatic disease confined to the mediastinum had a 17% reduction in mediastinal mass and was on study for 10 months. Patients with stable or responding disease had fewer prior therapies and lower tumor marker levels than patients with no evidence of response. For the ovarian cancer patients (n = 14), the median time to progression was 2 months (1–6) and median overall survival was 15.0 months. Updated data are presented here for one patient treated with this vaccine in a previous trial, with a time to progression of 38 months.Conclusions: Some patients who had limited tumor burden with minimal prior chemotherapy seemed to benefit from the vaccine. Further studies to confirm these results are warranted. Clin Cancer Res; 17(22); 7164–73. ©2011 AACR.

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Almost 1 in 10 individuals can suffer from one of many rare diseases (RDs). The average time to diagnosis for an RD patient is as high as 7 years. Artificial intelligence (AI)-based positron emission tomography (PET), if implemented appropriately, has tremendous potential to advance the diagnosis of RDs. Patient advocacy groups must be active stakeholders in the AI ecosystem if we are to avoid potential issues related to the implementation of AI into health care. AI medical devices must not only be RD-aware at each stage of their conceptualization and life cycle but also should be trained on diverse and augmented datasets representative of the end-user population including RDs. Inability to do so leads to potential harm and unsustainable deployment of AI-based medical devices (AIMDs) into clinical practice.

Trust in artificial intelligence (AI) by society and the development of trustworthy AI systems and ecosystems are critical for the progress and implementation of AI technology in medicine. With the growing use of AI in a variety of medical and imaging applications, it is more vital than ever to make these systems dependable and trustworthy. Fourteen core principles are considered in this article aiming to move the needle more closely to systems that are accurate, resilient, fair, explainable, safe, and transparent: toward trustworthy AI.

Adrenocortical carcinoma (ACC) is a rare malignancy with an overall unfavorable prognosis. Clinicians treating patients with ACC have noted accelerated growth in metastatic liver lesions that requires rapid intervention compared to other metastatic locations. This study measured and compared the growth rates of metastatic ACC lesions in the lungs, liver, and lymph nodes using volumetric segmentation. A total of 12 patients with metastatic ACC (six male; six female) were selected based on their medical history. Computer tomography (CT) exams were retrospectively reviewed and a sampling of ≤5 metastatic lesions per organ were selected for evaluation. Lesions in the liver, lung, and lymph nodes were measured and evaluated by volumetric segmentation. Statistical analyses were performed to compare the volumetric growth rates of the lesions in each organ system. In this cohort, 5/12 had liver lesions, 7/12 had lung lesions, and 5/12 had lymph node lesions. A total of 92 lesions were evaluated and segmented for lesion volumetry. The volume doubling time per organ system was 27 days in the liver, 90 days in the lungs, and 95 days in the lymph nodes. In this series of 12 patients with metastatic ACC, liver lesions showed a faster growth rate than lung or lymph node lesions.

Chronic granulomatous disease (CGD) is a rare primary immunodeficiency which can lead to gastrointestinal (GI) complications including inflammatory bowel disease. Radiographic findings in this cohort have not been well described. To describe the frequency and spectrum of gastrointestinal abnormalities seen on computed tomography (CT) in patients with CGD and determine whether radiography was predictive of endoscopic or histopathologic inflammatory findings. A retrospective review was conducted on 141 consecutive CGD patients seen at the National Institutes of Health between 1988 and 2011. All corresponding CTs were reviewed for gastrointestinal abnormalities including wall thickening. Endoscopic and histopathologic findings were reviewed in subjects with documented endoscopy within 30 days of an imaging study. Findings were compared between patients with and without wall thickening on CT to determine whether bowel wall thickening was predictive of endoscopic or histologic inflammatory findings. Two hundred and ninety-two CTs were reviewed. GI wall thickening was present on CT in 61% of patients (n = 86). Among a subgroup of 20 patients who underwent endoscopy at the time of their imaging, there was a statistically significant correlation between radiographic gastrointestinal wall thickening and endoscopic inflammation in the same intestinal segment (p = 0.035). Additionally, there was a significant correlation between radiographic gastrointestinal wall thickening and inflammatory features on histopathology (p = 0.02). GI abnormalities are commonly observed on CT in CGD patients. Bowel wall thickening correlates with endoscopic and histopathologic evidence of inflammation. These findings may be used to better facilitate directed endoscopic assessment and histopathologic sampling in patients with CGD.

To determine which methods of assessment of splenic size most accurately represent the actual spleen volume in patients with Chronic Lymphocytic Leukemia (CLL).The Abdominal Computed Tomography images of 48 patients with CLL enrolled on a phase 2 clinical trial at two time-points before and after 2-months of continuous acalabrutinib treatment were analyzed. Linear one-dimensional measurements of the spleen were taken in different planes. Two-dimensional and three-dimensional measurements were calculated from the linear measurements using mathematical formulae. The spleen volume was determined by manual segmentation as the ground truth. Data derived were analyzed using Pearson correlation and statistical significance was set at p0.05.Among the single-dimensional measurements, the strongest correlation with the segmented splenic volume was the sagittal long axis diameter (LAD) (r = 0.89, p0.05), followed closely by Coronal LAD (r = 0.87, p0.05) and cephalocaudal length (iwCLL) (r = 0.84, p0.05). For the two-dimensional indices, the sum of LAD and short axis diameter (SAD) of the spleen in axial plane showed good correlation with the splenic volume (r = 0.77, p0.05). Among the three-dimensional indices, the splenic index (0.523 x axial LAD x axial SAD x coronal height) and a formula for volume (30 + 0.58 x axial LAD x axial SAD x coronal height) had the strongest correlation (both r = 0.92, p0.05) with the spleen volume.The three-dimensional formulae showed the strongest correlation with volumetric reference spleen measurement. Among unidimensional measurements, the sagittal LAD had the best correlation with the actual splenic volume. The two-dimensional calculation methods were less reliable.

Total-body PET (TB-PET) is a technical advancement providing improved signal detection (effective sensitivity). This is achieved through a novel extended detector geometry among many other fine attunements. This technical evolution will ripple through molecular imaging revolutionizing clinical practice beyond the expectations of its designers. This article explores how this leap forward in effective sensitivity through TB-PET could enable dose reduction, faster imaging, temporal resolution improvement, dynamic imaging, and the in vivo noninvasive study of clinical systems biology. TB-PET has the potential to be an enabler of disruptive innovations in molecular imaging. This will change the landscape of medical imaging.

With improved molecular characterization of clear cell renal cancer and advances in texture analysis as well as machine learning, diagnostic radiology is primed to enter personalized medicine with radiogenomics: the identification of relationships between tumor image features and underlying genomic expression. By developing surrogate image biomarkers, clinicians can augment their ability to non-invasively characterize a tumor and predict clinically relevant outcomes (i.e., overall survival; metastasis-free survival; or complete/partial response to treatment). It is thus important for clinicians to have a basic understanding of this nascent field, which can be difficult due to the technical complexity of many of the studies. We conducted a review of the existing literature for radiogenomics in clear cell kidney cancer, including original full-text articles until September 2021. We provide a basic description of radiogenomics in diagnostic radiology; summarize existing literature on relationships between image features and gene expression patterns, either computationally or by radiologists; and propose future directions to facilitate integration of this field into the clinical setting.

The goal of parathyroid imaging is to identify all sources of excess parathyroid hormone secretion pre-operatively. A variety of imaging approaches have been evaluated and utilized over the years for this purpose. Ultrasound relies solely on structural features and is without radiation, however is limited to superficial evaluation. 4DCT and 4DMRI provide enhancement characteristics in addition to structural features and dynamic enhancement has been investigated as a way to better distinguish parathyroid from adjacent structures. It is important to recognize that 4DCT provides valuable information however results in much higher radiation dose to the thyroid gland than the other available examinations, and therefore the optimal number of phases is an area of controversy. Single-photon scintigraphy with 99mTc-Sestamibi, or dual tracer 99mTc-pertechnetate and 99mTc-sestamibi with or without SPECT or SPECT/CT is part of the standard of care in many centers with availability and expertise in nuclear medicine. This molecular imaging approach detects cellular physiology such as mitochondria content found in parathyroid adenomas. Combining structural imaging such as CT or MRI with molecular imaging in a hybrid approach allows the ability to obtain robust structural and functional information in one examination. Hybrid PET/CT is widely available and provides improved imaging and quantification over SPECT or SPECT/CT. Emerging PET imaging techniques, such as 18F-Fluorocholine, have the exciting potential to reinvent parathyroid imaging. PET/MRI may be particularly well suited to parathyroid imaging, where available, because of the ability to perform dynamic contrast-enhanced imaging and co-registered 18F-Fluorocholine PET imaging simultaneously with low radiation dose to the thyroid. A targeted agent specific for a parathyroid tissue biomarker remains to be identified.

Photon-counting CT (PCCT) detectors are the next step in advancing CT system development and will replace the current energy-integrating detectors (EIDs) in CT systems in the near future. In this context, the performance of PCCT was compared to EID CT for three clinically relevant tasks: 1) abdominal soft tissue imaging, where differentiating low contrast features is important; 2) vascular imaging, where iodine detectability is critical; and 3) high-resolution skeletal and lung imaging. A multitiered phantom was imaged on an investigational clinical PCCT system (Siemens Healthineers) across different doses using three imaging modes: macro and ultrahigh resolution (UHR) PCCT modes and EID CT. Images were reconstructed using filtered backprojection and soft tissue (B30f), vascular (B46f), or high-resolution (B70f; U70f for UHR) kernels. Noise power spectra, task transfer functions, and detectability index were evaluated. For a soft tissue task, PCCT modes showed comparable noise and resolution with improved contrast-to-noise ratio. For a vascular task, PCCT modes showed lower noise and improved iodine detectability. For a high resolution task, macro mode showed lower noise and comparable resolution while UHR mode showed higher noise but improved spatial resolution for both air and bone. PCCT offers competitive advantages to EID CT for clinical tasks.

Background Commercial low-field-strength MRI systems are generally not equipped with state-of-the-art MRI hardware, and are not suitable for demanding imaging techniques. An MRI system was developed that combines low field strength (0.55 T) with high-performance imaging technology. Purpose To evaluate applications of a high-performance low-field-strength MRI system, specifically MRI-guided cardiovascular catheterizations with metallic devices, diagnostic imaging in high-susceptibility regions, and efficient image acquisition strategies. Materials and Methods A commercial 1.5-T MRI system was modified to operate at 0.55 T while maintaining high-performance hardware, shielded gradients (45 mT/m; 200 T/m/sec), and advanced imaging methods. MRI was performed between January 2018 and April 2019. T1, T2, and T2* were measured at 0.55 T; relaxivity of exogenous contrast agents was measured; and clinical applications advantageous at low field were evaluated. Results There were 83 0.55-T MRI examinations performed in study participants (45 women; mean age, 34 years ± 13). On average, T1 was 32% shorter, T2 was 26% longer, and T2* was 40% longer at 0.55 T compared with 1.5 T. Nine metallic interventional devices were found to be intrinsically safe at 0.55 T (

Highlights • We developed an automatic method to segment cardiac sub-structures for radiotherapy planning CTs. • The Dice Similarity Coefficients and Average Surface Distance were up to 97% and < 11 mm, respectively. • The whole heart showed the absolute dose difference < 0.3 Gy whereas the coronary arteries showed < 2.3 Gy in breast radiotherapy simulations. • No notable improvement in our method beyond 10 atlases and using the manual guide points., Background and purpose We developed an automatic method to segment cardiac substructures given a radiotherapy planning CT images to support epidemiological studies or clinical trials looking at cardiac disease endpoints after radiotherapy. Material and methods We used a most-similar atlas selection algorithm and 3D deformation combined with 30 detailed cardiac atlases. We cross-validated our method within the atlas library by evaluating geometric comparison metrics and by comparing cardiac doses for simulated breast radiotherapy between manual and automatic contours. We analyzed the impact of the number of cardiac atlas in the library and the use of manual guide points on the performance of our method. Results The Dice Similarity Coefficients from the cross-validation reached up to 97% (whole heart) and 80% (chambers). The Average Surface Distance for the coronary arteries was less than 10.3 mm on average, with the best agreement (7.3 mm) in the left anterior descending artery (LAD). The dose comparison for simulated breast radiotherapy showed differences less than 0.06 Gy for the whole heart and atria, and 0.3 Gy for the ventricles. For the coronary arteries, the dose differences were 2.3 Gy (LAD) and 0.3 Gy (other arteries). The sensitivity analysis showed no notable improvement beyond ten atlases and the manual guide points does not significantly improve performance. Conclusion We developed an automated method to contour cardiac substructures for radiotherapy CTs. When combined with accurate dose calculation techniques, our method should be useful for cardiac dose reconstruction of a large number of patients in epidemiological studies or clinical trials.

PURPOSE: To compare Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1 with volumetric measurement in the setting of target lymph nodes that split into two or more nodes or merge into one conglomerate node. MATERIALS AND METHODS: In this retrospective study, target lymph nodes were evaluated on CT scans from 166 patients with different types of cancer; 158 of the scans came from The Cancer Imaging Archive. Each target node was measured using RECIST 1.1 criteria before and after merging or splitting, followed by volumetric segmentation. To compare RECIST 1.1 with volume, a single-dimension hypothetical diameter (HD) was determined from the nodal volume. The nodes were divided into three groups: (a) one-target merged (one target node merged with other nodes); (b) two-target merged (two neighboring target nodes merged); and (c) split node (a conglomerate node cleaved into smaller fragments). Bland-Altman analysis and t test were applied to compare RECIST 1.1 with HD. On the basis of the RECIST 1.1 concept, we compared response category changes between RECIST 1.1 and HD. RESULTS: The data set consisted of 30 merged nodes (19 one-target merged and 11 two-target merged) and 20 split nodes (mean age for all 50 included patients, 50 years ± 7 [standard deviation]; 38 men). RECIST 1.1, volumetric, and HD measurements indicated an increase in size in all one-target merged nodes. While volume and HD indicated an increase in size for nodes in the two-target merged group, RECIST 1.1 showed a decrease in size in all two-target merged nodes. Although volume and HD demonstrated a decrease in size of all split nodes, RECIST 1.1 indicated an increase in size in 60% (12 of 20) of the nodes. Discrepancy of the response categories between RECIST 1.1 and HD was observed in 5% (one of 19) in one-target merged, 82% (nine of 11) in two-target merged, and 55% (11 of 20) in split nodes. CONCLUSION: RECIST 1.1 does not optimally reflect size changes when lymph nodes merge or split. Keywords: CT, Lymphatic, Tumor Response Supplemental material is available for this article. © RSNA, 2021

PURPOSE: To compare the performance of energy-integrating detector (EID) CT, photon-counting detector CT (PCCT), and high-resolution PCCT (HR-PCCT) for the visualization of coronary plaques and reduction of stent artifacts in a phantom model. MATERIALS AND METHODS: An investigational scanner with EID and PCCT subsystems was used to image a coronary artery phantom containing cylindrical probes simulating different plaque compositions. The phantom was imaged with and without coronary stents using both subsystems. Images were reconstructed with a clinical cardiac kernel and an additional HR-PCCT kernel. Regions of interest were drawn around probes and evaluated for in-plane diameter and a qualitative comparison by expert readers. A linear mixed-effects model was used to compare the diameter results, and a Shrout-Fleiss intraclass correlation coefficient was used to assess consistency in the reader study. RESULTS: Comparing in-plane diameter to the physical dimension for nonstented and stented phantoms, measurements of the HR-PCCT images were more accurate (nonstented: 4.4% ± 1.1 [standard deviation], stented: −9.4% ± 4.6) than EID (nonstented: 15.5% ± 4.0, stented: −19.5% ± 5.8) and PCCT (nonstented: 19.4% ± 2.5, stented: −18.3% ± 4.4). Our analysis of variance found diameter measurements to be different across image groups for both nonstented and stented cases (P < .001). HR-PCCT showed less change on average in percent stenosis due to the addition of a stent (−5.5%) than either EID (+90.5%) or PCCT (+313%). For both nonstented and stented phantoms, observers rated the HR-PCCT images as having higher plaque conspicuity and as being the image type that was least impacted by stent artifacts, with a high level of agreement (interclass correlation coefficient = 0.85). CONCLUSION: Despite increased noise, HR-PCCT images were able to better visualize coronary plaques and reduce stent artifacts compared with EID or PCCT reconstructions. Keywords: CT-Spectral Imaging (Dual Energy), Phantom Studies, Cardiac, Physics, Technology Assessment © RSNA, 2021

PURPOSE: To assess differences in FDG-PET/CT uptake among four subtypes of renal tumors: clear cell RCC (ccRCC), papillary type I and II RCC (pRCC), and oncocytoma. METHODS: This retrospective study investigated 33 patients with 98 hereditary renal tumors. Lesions greater than 1 cm and patients with a timeframe of less than 18 months between preoperative imaging and surgery were considered. FDG-PET/CT images were independently reviewed by two nuclear medicine physicians, blinded to clinical information. Volumetric lesion SUV(mean) was measured and used to calculate a target-to-background ratio respective to liver (TBR). The Shrout-Fleiss intra-class correlation coefficient was used to assess reliability between readers. A linear mixed effects model, accounting for within-patient correlations, was used to compare TBR values of primary renal lesions with and without distant metastasis. RESULTS: The time interval between imaging and surgery for all tumors had a median of 77 (Mean: 139; Range: 1–512) days. Intra-class reliability of mean TBR resulted in a mean κ score of 0.93, indicating strong agreement between the readers. The mixed model showed a significant difference in mean TBR among the subtypes (p < 0.0001). Pairwise comparison showed significant differences between pRCC type II and ccRCC (p < 0.0001), pRCC type II and pRCC type I (p = 0.0001), and pRCC type II and oncocytoma (p = 0.0016). Furthermore, a significant difference in FDG uptake was present between primary pRCC type II renal lesions with and without distant metastasis (p = 0.023). CONCLUSION: pRCC type II lesions demonstrated significantly higher FDG activity than ccRCC, pRCC type I, or oncocytoma. These findings indicate that FDG may prove useful in studying the metabolic activity of renal neoplasms, identifying lesions of highest clinical concern, and ultimately optimizing active surveillance, and personalizing management plans.

Signal separability is an important factor in the differentiation of materials in spectral computed tomography. In this work, we evaluated the separability of two such materials, iodine and gadolinium with k-edges of 33.1 keV and 50.2 keV, respectively, with an investigational photon-counting CT scanner (Siemens, Germany). A 20 cm water equivalent phantom containing vials of iodine and gadolinium was imaged. Two datasets were generated by either varying the amount of contrast (iodine – 0.125-10 mg/mL, gadolinium 0.125-12 mg/mL) or by varying the tube current (50-300 mAs). Regions of interest were drawn within vials and then used to construct multivariate Gaussian models of signal. We evaluated three separation metrics using the Gaussian models: the area under the curve (AUC) of the receiver operating characteristic curve, the mean Mahalanobis distance, and the Jaccard index. For the dataset with varying contrast, all three metrics showed similar trends by indicating a higher separability when there was a large difference in signal magnitude between iodine and gadolinium. For the dataset with varying tube current, AUC showed the least variation due to change in noise condition and had a higher coefficient of determination (0.99, 0.97) than either mean Mahalanobis distance (0.69, 0.62) or Jaccard index (0.80, 0.75) when compared to material decomposition results for iodine or gadolinium respectively.

In this work, we define a theoretical approach to characterizing the signal-to-noise ratio (SNR) of multi-channeled systems such as spectral computed tomography image series. Spectral image datasets encompass multiple near-simultaneous acquisitions that share information. The conventional definition of SNR is applicable to a single image and thus does not account for the interaction of information between images in a series. We propose an extension of the conventional SNR definition into a multivariate space where each image in the series is treated as a separate information channel thus defining a spectral SNR matrix. We apply this to the specific case of contrast-to-noise ratio (CNR). This matrix is able to account for the conventional CNR of each image in the series as well as a covariance weighted CNR (Cov-CNR), which accounts for the covariance between two images in the series. We evaluate this experimentally with data from an investigational photon-counting CT scanner (Siemens).

Photon-counting computed tomography (CT) is a developing technology that has the potential to address some limitations of CT imaging and bring about improvements and potentially new applications to this field. Photon-counting detectors have a fundamentally different detection mechanism from conventional CT energy-integrating detectors that can improve dose efficiency, spatial resolution, and energy-discrimination capabilities. In the past decade, promising human studies have been reported in the literature that have demonstrated benefits of this relatively new technology for various clinical applications. In this review, we provide a succinct description of the photon-counting detector technology and its detection mechanism in comparison with energy-integrating detectors in a manner understandable for clinicians and radiologists, introduce benefits and some of the existing challenges present in this technology, and provide an overview of the current status and potential clinical applications of this technology in imaging of the thorax by providing example images acquired with an investigational whole-body photon-counting CT scanner.

Total-body PET (TB-PET) is a technical advancement providing improved signal detection (effective sensitivity). This is achieved through a novel extended detector geometry among many other fine attunements. This technical evolution will ripple through molecular imaging revolutionizing clinical practice beyond the expectations of its designers. This article explores how this leap forward in effective sensitivity through TB-PET could enable dose reduction, faster imaging, temporal resolution improvement, dynamic imaging, and the in vivo noninvasive study of clinical systems biology. TB-PET has the potential to be an enabler of disruptive innovations in molecular imaging. This will change the landscape of medical imaging.

Chest CT is emerging as a valuable diagnostic tool for clinical management of COVID-19 associated lung disease. Artificial intelligence (AI) has the potential to aid in rapid evaluation of CT scans for differentiation of COVID-19 findings from other clinical entities. Here we show that a series of deep learning algorithms, trained in a diverse multinational cohort of 1280 patients to localize parietal pleura/lung parenchyma followed by classification of COVID-19 pneumonia, can achieve up to 90.8% accuracy, with 84% sensitivity and 93% specificity, as evaluated in an independent test set (not included in training and validation) of 1337 patients. Normal controls included chest CTs from oncology, emergency, and pneumonia-related indications. The false positive rate in 140 patients with laboratory confirmed other (non COVID-19) pneumonias was 10%. AI-based algorithms can readily identify CT scans with COVID-19 associated pneumonia, as well as distinguish non-COVID related pneumonias with high specificity in diverse patient populations., Chest CT is emerging as a valuable diagnostic tool for clinical management of COVID-19 associated lung disease. Here, the authors present a multinational study on the application of deep learning algorithms for COVID-19 diagnosis against multiple lung conditions as controls.

Rationale and Objectives The purpose of this study was to investigate the potential of photon-counting CT (PCCT) to improve quantitative image quality for low dose imaging compared to energy-integrating detector CT (EID CT). Materials and Methods An investigational scanner (Siemens, Germany) with PCCT and EID CT subsystems was used to compare image quality performance at four dose levels: 1.7, 2, 4, 6 mGy CTDIvol, all at or below current dose values used for conventional abdominal CT. A CT quality control phantom with a homogeneous section for noise measurements and a section with cylindrical inserts of air (−910 HU), polystyrene (50 HU), acrylic (205 HU), and Teflon (1000 HU) was imaged and characterized in terms of noise, resolution, contrast-to-noise ratio (CNR), and detectability index. A second phantom with a 30 cm diameter was also imaged containing iodine solutions ranging from 0.125 to 8 mg I/mL. CNR of the iodine vials was computed as a function of CT dose and iodine concentration. Results With resolution unaffected by dose in both PCCT and EID CT, PCCT images exhibited 22.1-24.0% improvement in noise across dose levels evaluated. This noise improvement translated into a 29-41% improvement in CNR and 20-36% improvement in detectability index. For iodine contrast, PCCT images had a higher CNR for all combinations of iodine contrast and dose evaluated. Conclusion For the conditions studied, PCCT exhibited superior image quality compared to EID CT. For iodine detection, PCCT offered a notable advantage with improved CNR at all doses and iodine concentration levels.

This is a single-center report on coordination of staff and handling of patients during the outbreak of the COVID-19 (coronavirus disease 2019) in a region with high incidence and prevalence of disease. The selection of procedures for interventional radiology (IR), preparation of staff and interventional suite before the arrival of patients, the facility ventilation systems and intra- and post-procedural workflow optimization are described. The control measures described may increase the cost of the equipment, prolong procedural times and increase technical difficulties. However, these precautions may help control the spread of COVID-19 within the healthcare facility.

In photon-counting CT, detector energy thresholds directly affect image quality attributes such as contrast and noise. The purpose of this study was to identify optimum energy thresholds using a comprehensive virtual clinical trial platform. The virtual trial was done using a computational, anthropomorphic phantom and a photon-counting CT simulator. The phantom (adult male, 50th percentile body mass index) was chosen from the library of extended cardiac-torso (XCAT) phantoms. A vessel growth algorithm was used to model detailed hepatic arteries within the liver. Five computational lesions were inserted to the liver. The phantom was “imaged” with iodinated contrast at three contrast phases, i.e. arterial, portal venous, and delayed phases. All virtual sinograms were simulated using a validated CT simulator (DukeSim) modeling a prototype photon-counting scanner (CounT, Siemens Healthcare). The simulator included an energy- and threshold-dependent detector model accounting for X-ray and electronic effects and noise. At each contrast phase, the phantom was imaged at 35 detector energy threshold combinations with five lower-energy thresholds (20-40 keV) and seven upper-energy thresholds (50-80 keV). For each scan, two “threshold” images (photons detected beyond a threshold) and one “bin” image (photons detected between the two thresholds) were acquired. Noise magnitude, image contrast, and contrast-to-noise ratio (CNR) were measured in the aorta and the liver lesions. Optimum energy thresholds were identified as those yielding higher CNRs. From the simulations, noise magnitude was found to increase and CNR decrease with the increase in energy thresholds. Keeping the low threshold constant, the noise decreased and CNR increased with the increase in the upper energy threshold. Therefore, our results suggest that a combination of a lower threshold at 20-30 keV and upper threshold at 80 keV maximize the image quality. This study demonstrates an application of a virtual trial platform to quantify and optimize the quality of photon-counting CT.

Purpose To retrospectively investigate the radiological presentations of HLRCC-associated renal tumors to facilitate accurate lesion characterization and compare these presentations with simple cysts and characteristics of other subtypes of renal cell carcinoma (RCC) as reported in the literature. Methods The MRI and CT imaging characteristics of 39 pathologically confirmed lesions from 30 patients (20 male, 10 female) with HLRCC syndrome were evaluated by two radiologists. Patients had an average age at diagnosis of 43.8 ± 13.1 years. Lesion characteristics including laterality, homogeneity, diameter (cm), nodularity, septations, T1 and T2 signal intensity, enhancement, and restricted diffusion were recorded. Imaging characteristics of the lesions were further compared to characteristics of benign simple cysts surgically removed at the same time point. Results The examined lesions had a mean diameter of 5.06 ± 3.80 cm, an average growth rate of 2.91 × 10−3 cm/day and an estimated annual growth rate of 1.06 cm/year. 50% of lesions demonstrated nodularity, 65% were mostly T2-hyperintense, 83% demonstrated restricted diffusion in solid portions of the lesions, and 65% had well-defined margins. 76% of patients demonstrated extra-renal manifestations, 53% lymphadenopathy, and 43% distant metastasis. Conclusions Our analysis confirmed that while HLRCC-associated renal lesions demonstrate diversity in imaging presentations, the majority are unilateral and solitary, T2-hyperintense, heterogeneous with well-defined margins, and frequently demonstrate restricted diffusion and nodularity.

PURPOSE: To assess a 0.55-T MRI system for imaging lung disease and to compare image quality with clinical CT scans. MATERIALS AND METHODS: In this prospective study conducted between November 2018 and December 2019, respiratory-triggered T2-weighted turbo spin-echo MRI at 0.55 T was compared with clinical CT scans in 24 participants (mean age, 59 years ± 16 [standard deviation]; 18 women) with common lung abnormalities. MR images were reviewed and scored by experienced readers. Abnormal findings identified with MRI and CT were compared using the Cohen κ statistic. RESULTS: High-quality structural pulmonary MR images were attained with an average acquisition time of 11 minutes ± 3. MRI generated sufficient image quality to robustly detect bronchiectasis (κ = 0.61), consolidative opacities (κ = 1.00), cavitary lesions (κ = 1.00), effusion (κ = 0.64), mucus plug (κ = 0.68), and solid scattered nodularity (κ = 0.82). Diffuse disease, including ground-glass opacities (κ = 0.57) and tree-in-bud nodules (κ = 0.48), were the findings that were most difficult to discern using MRI, with false readings in four of 18 patients for each feature. Nodule size, which was measured independently at CT and MRI, was strongly correlated (R(2) = 0.99) for nodules with a measurement of 10 mm ± 5 (range, 5–23 mm). CONCLUSION: This initial study indicates that high-performance 0.55-T MRI holds promise in the evaluation of common lung disease. Clinical trials registration no. NCT03331380 Supplemental material is available for this article. Keywords: MRI, Pulmonary, Technology Assessment © RSNA, 2021

Purpose To assess the feasibility of transarterial chemoembolization with drug-eluting embolic (DEE) microspheres in a woodchuck model of hepatocellular carcinoma (HCC). Materials and Methods Nine woodchucks were studied: 4 normal animals and 5 animals infected with woodchuck hepatitis virus in which HCC had developed. Three animals with HCC underwent multidetector CT. A 3-F sheath was introduced into the femoral artery, and the hepatic arteries were selectively catheterized with 2.0–2.4-F microcatheters. Normal animals underwent diagnostic angiography and bland embolization. Animals with HCC underwent DEE transarterial chemoembolization with 70–150-μm radiopaque microspheres loaded with 37.5 mg doxorubicin per milliliter. Cone-beam CT and multidetector CT were performed. Following euthanasia, explanted livers underwent micro-CT, histopathologic examination, and fluorescence imaging of doxorubicin. Results The tumors were hypervascular and supplied by large-caliber tortuous vessels, with arteriovenous shunts present in 2 animals. There was heterogeneous enhancement on multidetector CT with areas of necrosis. Six tumors were identified. The most common location was the right medial lobe (n = 3). Mean tumor volume was 30.7 cm3 ± 12.3. DEE chemoembolization of tumors was achieved. Excluding the 2 animals with arteriovenous shunts, the mean volume of DEE microspheres injected was 0.49 mL ± 0.17. Fluorescence imaging showed diffusion of doxorubicin from the DEE microspheres into the tumor. Conclusions Woodchuck HCC shares imaging appearances and biologic characteristics with human HCC. Selective catheterization and DEE chemoembolization may similarly be performed. Woodchucks may be used to model interventional therapies and possibly characterize radiologic–pathologic correlations.

The purpose of this study was to evaluate the potential of a prototype photon-counting CT system scanner to characterize liver texture and lung lesion morphology features. We utilized a multi-tiered phantom (Mercury Phantom 4.0) to characterize the noise power spectrum and task-transfer functions of both conventional and photoncounting modes on the scanner. Using these metrics, we blurred three textures models and fifteen model lesions for four doses (CTDIvol: 4, 8, 16, 24 mGy), and three slice thicknesses (1.6, 2.5, 4 mm), for a total of 12 imaging conditions. Twenty texture features and twenty-one morphology features were evaluated. Performance was characterized in terms of accuracy (percent bias of features across different conditions) and variability (coefficient of variation of features due to repeats and averaged across conditions). Compared to conventional CT, photon-counting CT had comparable accuracy and variability for texture features. For morphology features, photon-counting CT had comparable accuracy and less variability than conventional CT. For both imaging modes, change in dose showed slight variation in features and increasing slice thickness caused a monotonic change with feature dependent directionality. Photon-counting CT can improve the characterization of morphology features without compromising texture features.

In this study, we examined image quality in photon-counting CT images due to variation in energy thresholds. Images of an ACR quality control phantom were acquired using a prototype photon-counting CT scanner with two variable energy thresholds. The lower threshold, which varied between 20 to 50 keV, and the higher threshold, which varied between 50 to 90 keV, were used to separate the data into two energy bins. This produced a total of four images: threshold 1, containing signal between the lower threshold and the maximal value, threshold 2, containing signal between the higher threshold and the maximal value, bin 1, containing signal between the lower and higher threshold, and bin 2, containing signal between the higher threshold and maximal value. Thirteen pairs of energy thresholds were evaluated spanning the entire energy threshold space. An automated program was used to analyze images for standard quality control metrics including noise measurement, resolution, low contrast detectability, and contrast-to-noise ratio (CNR). Metrics were compared between image types and across energy thresholds. Threshold 1 images showed the least variation despite change in thresholds. Increasing the higher threshold degraded image quality in threshold 2 and bin 2 images, but improved performance in bin 1 images. Increasing the lower threshold decreased performance for bin 1 images. Resolution was largely unaffected by change in energy threshold.

To evaluate the performance of a prototype photon-counting detector (PCD) computed tomography (CT) system for abdominal CT in humans and to compare the results with a conventional energy-integrating detector (EID).The study was HIPAA-compliant and institutional review board-approved with informed consent. Fifteen asymptomatic volunteers (seven men; mean age, 58.2 years ± 9.8 [standard deviation]) were prospectively enrolled between September 2 and November 13, 2015. Radiation dose-matched delayed contrast agent-enhanced spiral and axial abdominal EID and PCD scans were acquired. Spiral images were scored for image quality (Wilcoxon signed-rank test) in five regions of interest by three radiologists blinded to the detector system, and the axial scans were used to assess Hounsfield unit accuracy in seven regions of interest (paired t test). Intraclass correlation coefficient (ICC) was used to assess reproducibility. PCD images were also used to calculate iodine concentration maps. Spatial resolution, noise-power spectrum, and Hounsfield unit accuracy of the systems were estimated by using a CT phantom.In both systems, scores were similar for image quality (median score, 4; P = .19), noise (median score, 3; P = .30), and artifact (median score, 1; P = .17), with good interrater agreement (image quality, noise, and artifact ICC: 0.84, 0.88, and 0.74, respectively). Hounsfield unit values, spatial resolution, and noise-power spectrum were also similar with the exception of mean Hounsfield unit value in the spinal canal, which was lower in the PCD than the EID images because of beam hardening (20 HU vs 36.5 HU; P.001). Contrast-to-noise ratio of enhanced kidney tissue was improved with PCD iodine mapping compared with EID (5.2 ± 1.3 vs 4.0 ± 1.3; P.001).The performance of PCD showed no statistically significant difference compared with EID when the abdomen was evaluated in a conventional scan mode. PCD provides spectral information, which may be used for material decomposition.

BACKGROUND: Identification of markers to aid in understanding the growth kinetics of Von Hippel–Lindau (VHL)-associated clear cell renal cell carcinoma (ccRCC) has the potential to allow individualization of patient care, thereby helping prevent unnecessary screening and optimizing intervention. PURPOSE: To determine whether the degree of restricted diffusion at baseline MRI holds predictive potential for the growth rate of VHL-associated ccRCC. MATERIALS AND METHODS: Patients with VHL disease who underwent surgical resection of tumors between November 2014 and October 2017 were analyzed retrospectively in this HIPAA-compliant study. The change in ccRCC volume between two time points and apparent diffusion coefficient (ADC) at baseline was calculated by using segmentations by two readers at nephrographic-phase CT and diffusion-weighted MRI, respectively. Intraclass correlation coefficient was used to assess agreement between readers. Repeated-measures correlation was used to investigate relationships between ADC (histogram parameters) and tumor size at baseline with growth rate and volume doubling time (VDT). Predictive performance of the ADC parameter with highest correlation and tumor size at baseline was reviewed to differentiate tumors based on their VDT (≤1 year or >1 year). RESULTS: Forty-six patients (mean age, 46 years ± 7 [standard deviation]; 25 women) with 100 ccRCCs were evaluated. Interreader agreement resulted in mean κ scores of 0.89, 0.82, and 0.93 for mean ADC, baseline tumor volume, and follow-up tumor volume, respectively. ADC percentiles correlated negatively with tumor growth rate but correlated positively with VDT. Lower ADC values demonstrated stronger correlations. The 25th percentile ADC had the strongest correlation with growth rate (ρ = −0.52, P < .001) and VDT (ρ = 0.60, P < .001) and enabled prediction of VDT (≤1 year or >1 year) with an area under the receiver operating characteristic curve of 0.86 (sensitivity, 67%; specificity, 89%) (P < .001). CONCLUSION: Apparent diffusion coefficient at baseline was negatively correlated with tumor growth rate. Diffusion-weighted MRI may be useful to identify clear cell renal cell carcinomas with higher growth rates. © RSNA, 2020 See also the editorial by Goh and Prezzi in this issue.

There are at least two categories of errors in radiology screening that can lead to suboptimal diagnostic decisions and interventions:(i)human fallibility and (ii)complexity of visual search. Computer aided diagnostic (CAD) tools are developed to help radiologists to compensate for some of these errors. However, despite their significant improvements over conventional screening strategies, most CAD systems do not go beyond their use as second opinion tools due to producing a high number of false positives, which human interpreters need to correct. In parallel with efforts in computerized analysis of radiology scans, several researchers have examined behaviors of radiologists while screening medical images to better understand how and why they miss tumors, how they interact with the information in an image, and how they search for unknown pathology in the images. Eye-tracking tools have been instrumental in exploring answers to these fundamental questions. In this paper, we aim to develop a paradigm shift CAD system, called collaborative CAD (C-CAD), that unifies both of the above mentioned research lines: CAD and eye-tracking. We design an eye-tracking interface providing radiologists with a real radiology reading room experience. Then, we propose a novel algorithm that unifies eye-tracking data and a CAD system. Specifically, we present a new graph based clustering and sparsification algorithm to transform eye-tracking data (gaze) into a signal model to interpret gaze patterns quantitatively and qualitatively. The proposed C-CAD collaborates with radiologists via eye-tracking technology and helps them to improve diagnostic decisions. The C-CAD learns radiologists' search efficiency by processing their gaze patterns. To do this, the C-CAD uses a deep learning algorithm in a newly designed multi-task learning platform to segment and diagnose cancers simultaneously., Submitted to Medical Image Analysis Journal (MedIA)

Purpose To investigate whether photon-counting detector (PCD) technology can improve dose-reduced chest computed tomography (CT) image quality compared with that attained with conventional energy-integrating detector (EID) technology in vivo. Materials and Methods This was a HIPAA-compliant institutional review board-approved study, with informed consent from patients. Dose-reduced spiral unenhanced lung EID and PCD CT examinations were performed in 30 asymptomatic volunteers in accordance with manufacturer-recommended guidelines for CT lung cancer screening (120-kVp tube voltage, 20-mAs reference tube current-time product for both detectors). Quantitative analysis of images included measurement of mean attenuation, noise power spectrum (NPS), and lung nodule contrast-to-noise ratio (CNR). Images were qualitatively analyzed by three radiologists blinded to detector type. Reproducibility was assessed with the intraclass correlation coefficient (ICC). McNemar, paired t, and Wilcoxon signed-rank tests were used to compare image quality. Results Thirty study subjects were evaluated (mean age, 55.0 years ± 8.7 [standard deviation]; 14 men). Of these patients, 10 had a normal body mass index (BMI) (BMI range, 18.5-24.9 kg/m

In this study, we developed a novel system, called Gaze2Segment, integrating biological and computer vision techniques to support radiologists’ reading experience with an automatic image segmentation task. During diagnostic assessment of lung CT scans, the radiologists’ gaze information were used to create a visual attention map. Next, this map was combined with a computer-derived saliency map, extracted from the gray-scale CT images. The visual attention map was used as an input for indicating roughly the location of a region of interest. With computer-derived saliency information, on the other hand, we aimed at finding foreground and background cues for the object of interest found in the previous step. These cues are used to initiate a seed-based delineation process. The proposed Gaze2Segment achieved a dice similarity coefficient of 86% and Hausdorff distance of 1.45 mm as a segmentation accuracy. To the best of our knowledge, Gaze2Segment is the first true integration of eye-tracking technology into a medical image segmentation task without the need for any further user-interaction.