Exploiting the Potential of Photon-Counting CT in Abdominal Imaging.

ABSTRACT: Photon-counting computed tomography (PCCT) imaging uses a new detector technology to provide added information beyond what can already be obtained with current CT and MR technologies. This review provides an overview of PCCT of the abdomen and focuses specifically on applications that benefit the most from this new imaging technique. We describe the requirements for a successful abdominal PCCT acquisition and the challenges for clinical translation. The review highlights work done within the last year with an emphasis on new protocols that have been tested in clinical practice. Applications of PCCT include imaging of cystic lesions, sources of bleeding, and cancers. Photon-counting CT is positioned to move beyond detection of disease to better quantitative staging of disease and measurement of treatment response.

Liver fat quantification in photon counting CT in head to head comparison with clinical MRI - First experience.

PURPOSE: To compare liver fat quantification between MRI and photon-counting CT (PCCT). METHOD: A cylindrical phantom with inserts containing six concentrations of oil (0, 10, 20, 30, 50 and 100%) and oil-iodine mixtures (0, 10, 20, 30 and 50% fat +3 mg/mL iodine) was imaged with a PCCT (NAEOTOM Alpha) and a 1.5 T MRI system (MR 450w, IDEAL-IQ sequence), using clinical parameters. An IRB-approved prospective clinical evaluation included 12 obese adult patients with known fatty liver disease (seven women, mean age: 61.5 ± 13 years, mean BMI: 30.3 ± 4.7 kg/m2). Patients underwent a same-day clinical MRI and PCCT of the abdomen. Liver fat fractions were calculated for four segments (I, II, IVa and VII) using in- and opposed-phase on MRI ((Meanin - Meanopp)/2*Meanin) and iodine-fat, tissue decomposition analysis in PCCT (Syngo.Via VB60A). CT and MRI Fat fractions were compared using two-sample t-tests with equal variance. Statistical analysis was performed using RStudio (Version1.4.1717). RESULTS: Phantom results showed no significant differences between the known fat fractions (P = 0.32) or iodine (P = 0.6) in comparison to PCCT-measured concentrations, and no statistically significant difference between known and MRI-measured fat fractions (P = 0.363). In patients, the mean fat signal fraction measured on MRI and PCCT was 13.1 ± 9.9% and 12.0 ± 9.0%, respectively, with an average difference of 1.1 ± 1.9% between the modalities (P = 0.138). CONCLUSION: First experience shows promising accuracy of liver fat fraction quantification for PCCT in obese patients. This method may improve opportunistic screening for CT in the future.

Is it possible to use low-dose deep learning reconstruction for the detection of liver metastases on CT routinely?

OBJECTIVES: To compare the image quality and hepatic metastasis detection of low-dose deep learning image reconstruction (DLIR) with full-dose filtered back projection (FBP)/iterative reconstruction (IR). METHODS: A contrast-detail phantom consisting of low-contrast objects was scanned at five CT dose index levels (10, 6, 3, 2, and 1 mGy). A total of 154 participants with 305 hepatic lesions who underwent abdominal CT were enrolled in a prospective non-inferiority trial with a three-arm design based on phantom results. Data sets with full dosage (13.6 mGy) and low dosages (9.5, 6.8, or 4.1 mGy) were acquired from two consecutive portal venous acquisitions, respectively. All images were reconstructed with FBP (reference), IR (control), and DLIR (test). Eleven readers evaluated phantom data sets for object detectability using a two-alternative forced-choice approach. Non-inferiority analyses were performed to interpret the differences in image quality and metastasis detection of low-dose DLIR relative to full-dose FBP/IR. RESULTS: The phantom experiment showed the dose reduction potential from DLIR was up to 57% based on the reference FBP dose index. Radiation decreases of 30% and 50% resulted in non-inferior image quality and hepatic metastasis detection with DLIR compared to full-dose FBP/IR. Radiation reduction of 70% by DLIR performed inferiorly in detecting small metastases (< 1 cm) compared to full-dose FBP (difference: -0.112; 95% confidence interval [CI]: -0.178 to 0.047) and full-dose IR (difference: -0.123; 95% CI: -0.182 to 0.053) (p < 0.001). CONCLUSION: DLIR enables a 50% dose reduction for detecting low-contrast hepatic metastases while maintaining comparable image quality to full-dose FBP and IR. KEY POINTS: • Non-inferiority study showed that deep learning image reconstruction (DLIR) can reduce the dose to oncological patients with low-contrast lesions without compromising the diagnostic information. • Radiation dose levels for DLIR can be reduced to 50% of full-dose FBP and IR for detecting low-contrast hepatic metastases, while maintaining comparable image quality. • The reduction of radiation by 70% by DLIR is clinically acceptable but insufficient for detecting small low-contrast hepatic metastases (< 1 cm).

Effect of deep learning image reconstruction in the prediction of resectability of pancreatic cancer: Diagnostic performance and reader confidence.

OBJECTIVE: To assess the diagnostic performance and reader confidence in determining the resectability of pancreatic cancer at computed tomography (CT) using a new deep learning image reconstruction (DLIR) algorithm. METHODS: A retrospective review was conduct of on forty-seven patients with pathologically confirmed pancreatic cancers who underwent baseline multiphasic contrast-enhanced CT scan. Image data sets were reconstructed using filtered back projection (FBP), hybrid model-based adaptive statistical iterative reconstruction (ASiR-V) 60 %, and DLIR "TrueFidelity" at low(L), medium(M), and high strength levels(H). Four board-certified abdominal radiologists reviewed the CT images and classified cancers as resectable, borderline resectable, or unresectable. Diagnostic performance and reader confidence for categorizing the resectability of pancreatic cancer were evaluated based on the reference standards, and the interreader agreement was assessed using Fleiss k statistics. RESULTS: For prediction of margin-negative resections(ie, R0), the average area under the receiver operating characteristic curve was significantly higher with DLIR-H (0.91; 95 % confidence interval [CI]: 0.79, 0.98) than FBP (0.75; 95 % CI:0.60, 0.86) and ASiR-V (0.81; 95 % CI:0.67, 0.91) (p = 0.030 and 0.023 respectively). Reader confidence scores were significantly better using DLIR compared to FBP and ASiR-V 60 % and increased linearly with the increase of DLIR strength level (all p < 0.001). Among the image reconstructions, DLIR-H showed the highest interreader agreement in the resectability classification and lowest subject variability in the reader confidence. CONCLUSIONS: The DLIR-H algorithm may improve the diagnostic performance and reader confidence in the CT assignment of the local resectability of pancreatic cancer while reducing the interreader variability.

Split-Bolus, Single-Acquisition, Dual-Phase Abdominopelvic CT Angiography for the Evaluation of Lung Transplant Candidates: Image Quality and Resource Utilization.

OBJECTIVE. The purpose of this study was to assess the image quality and resource utilization of single-injection, split-bolus, dual-enhancement abdominopelvic CT angiography (hereafter referred to as dual-enhancement CTA) performed for combined vascular and solid organ assessment compared with those of single-injection, single-enhancement abdominopelvic CT angiography (hereafter referred to as single-enhancement CTA) for vascular assessment in combination with additional examinations (CT, MRI, and US) performed to assess for malignancy in lung transplant candidates. MATERIALS AND METHODS. We retrospectively reviewed 100 patients who underwent abdominopelvic CTA examinations before lung transplant. Cohort A (n = 50) underwent dual-enhancement CTA and cohort B (n = 50) underwent single-enhancement CTA. Contrast opacification of the vasculature was assessed along the abdominal aorta through the right femoral artery. Solid organ enhancement was assessed in the right lobe of the liver and the right renal cortex. Measurements of mean radiation dose, contrast exposure, and cost of the studies (in U.S. dollars) were compared. RESULTS. Mean (± SD) vascular enhancement on dual-enhancement CTA and single-enhancement CTA was 334.2 ± 26.5 HU (coefficient of variation, 8.3%) and 340.0 ± 21.6 HU (coefficient of variation, 6.5%) (p = 0.23), respectively. For dual-enhancement CTA and single-enhancement CTA, mean liver enhancement was 125.8 ± 30.5 HU and 60.4 ± 6.9 HU (p < 0.01), respectively, whereas mean renal cortical enhancement was 260.3 ± 62.2 HU and 133.4 ± 38.6 HU (p < 0.01), respectively. The mean IV contrast volume was 150 mL for dual-enhancement CTA and 75 mL for single-enhancement CTA. Cohort A underwent six additional imaging studies (one of which was a CT colonography study with an effective dose of 19.0 mSv) at a total cost of $9840 per patient. Cohort B underwent 44 additional imaging studies (mean effective dose, 12.7 ± 6.5 mSv) at a total cost of $12,846 per patient (resulting in a 30.6% reduction in cost for dual-enhancement CTA studies; p < 0.0001). CONCLUSION. Dual-enhancement abdominopelvic CTA allows combined vascular and abdominopelvic solid organ assessment with improved image quality and a lower cost compared with traditional imaging pathways.

Impact of dual energy cardiac CT for metal artefact reduction post aortic valve replacement.

PURPOSE: Assess image quality of dual-energy (DE) and single-energy (SE) cardiac multi-detector computed tomographic (MDCT) post aortic valve replacement (AVR) on a dual source MDCT scanner. METHODS: Eighty patients with cardiac MDCT acquisitions (ECG gated, dual-source) post-surgical and transcatheter AVR were retrospectively identified. Forty DE (cohort 1) and 40 SE acquisitions (cohort 2; 100 or 120 kVp) were reviewed. Metal artefact at valve coaptation (VC) and valve insertion site (VIS), and contrast enhancement were assessed. Valve leaflet edge definition was graded on a 4-point scale by three radiologists. RESULTS: The mean percentage valve area obscured by metal artifact differed between the cohorts; cohort 1 DE blended, high keV and low keV: 14.8 %, 11.1 % and 17.8 % at VC and 16.4 %, 13 %, 20.4 % at VIS respectively. Cohort 2: 25.8 % and 33.6 % (VC and VIS); each DE reconstruction vs SE: P < 0.0001. Average contrast opacification and coefficient of variance for cohort 1: 562.9 ± 144.7, 281.1 ± 60.3 and 1132.7 ± 300.8 Hounsfield Units (HU) and 9.6 %, 10 % and 8.9 %. For cohort 2: 437.2 ± 119.2 HU and 10.8 % (P < 0.01). Average leaflet edge definition cohort 1: 2.3 ± 0.4, 2.7 ± 0.2 and 2.3 ± 0.2, and cohort 2: 2.9 ± 0.2. CONCLUSION: DE high keV renderings can result in up to 17.2 % less metal artefact compared to standard SE acquisition for cardiac CT. Contrast opacification and homogeneity is higher for DE blended and low keV renderings compared to SE acquisition with leaflet visibility preferred for low keV and blended DE renderings.

Large-scale medical image annotation with crowd-powered algorithms.

Accurate segmentations in medical images are the foundations for various clinical applications. Advances in machine learning-based techniques show great potential for automatic image segmentation, but these techniques usually require a huge amount of accurately annotated reference segmentations for training. The guiding hypothesis of this paper was that crowd-algorithm collaboration could evolve as a key technique in large-scale medical data annotation. As an initial step toward this goal, we evaluated the performance of untrained individuals to detect and correct errors made by three-dimensional (3-D) medical segmentation algorithms. To this end, we developed a multistage segmentation pipeline incorporating a hybrid crowd-algorithm 3-D segmentation algorithm integrated into a medical imaging platform. In a pilot study of liver segmentation using a publicly available dataset of computed tomography scans, we show that the crowd is able to detect and refine inaccurate organ contours with a quality similar to that of experts (engineers with domain knowledge, medical students, and radiologists). Although the crowds need significantly more time for the annotation of a slice, the annotation rate is extremely high. This could render crowdsourcing a key tool for cost-effective large-scale medical image annotation.

Similar changes of gene expression in human skeletal muscle after resistance exercise and multiple fine needle biopsies.

Repeated biopsy sampling from one muscle is necessary to investigate muscular adaptation to different forms of exercise as adaptation is thought to be the result of cumulative effects of transient changes in gene expression in response to single exercise bouts. In a crossover study, we obtained four fine needle biopsies from one vastus lateralis muscle of 11 male subjects (25.9 ± 3.8 yr, 179.2 ± 4.8 cm, 76.5 ± 7.0 kg), taken before (baseline), 1, 4, and 24 h after one bout of squatting exercise performed as conventional squatting or as whole body vibration exercise. To investigate if the repeated biopsy sampling has a confounding effect on the observed changes in gene expression, four fine needle biopsies from one vastus lateralis muscle were also taken from 8 male nonexercising control subjects (24.5 ± 3.7 yr, 180.6 ± 1.2 cm, 81.2 ± 1.6 kg) at the equivalent time points. Using RT-PCR, we observed similar patterns of change in the squatting as well as in the control group for the mRNAs of interleukin 6 (IL-6), IL-6 receptor, insulin-like growth factor 1, p21, phosphofructokinase, and glucose transporter in relation to the baseline biopsy. In conclusion, multiple fine needle biopsies obtained from the same muscle region can per se influence the expression of marker genes induced by an acute bout of resistance exercise.