Diagnostic value of deep learning reconstruction for radiation dose reduction at abdominal ultra-high-resolution CT.

OBJECTIVES: We evaluated lower dose (LD) hepatic dynamic ultra-high-resolution computed tomography (U-HRCT) images reconstructed with deep learning reconstruction (DLR), hybrid iterative reconstruction (hybrid-IR), or model-based IR (MBIR) in comparison with standard-dose (SD) U-HRCT images reconstructed with hybrid-IR as the reference standard to identify the method that allowed for the greatest radiation dose reduction while preserving the diagnostic value. METHODS: Evaluated were 72 patients who had undergone hepatic dynamic U-HRCT; 36 were scanned with the standard radiation dose (SD group) and 36 with 70% of the SD (lower dose [LD] group). Hepatic arterial and equilibrium phase (HAP, EP) images were reconstructed with hybrid-IR in the SD group, and with hybrid-IR, MBIR, and DLR in the LD group. One radiologist recorded the standard deviation of attenuation in the paraspinal muscle as the image noise. The overall image quality was assessed by 3 other radiologists; they used a 5-point confidence scale ranging from 1 (unacceptable) to 5 (excellent). Superiority and equivalence with prespecified margins were assessed. RESULTS: With respect to the image noise, in the HAP and EP, LD DLR and LD MBIR images were superior to SD hybrid-IR images; LD hybrid-IR images were neither superior nor equivalent to SD hybrid-IR images. With respect to the quality scores, only LD DLR images were superior to SD hybrid-IR images. CONCLUSIONS: DLR preserved the quality of abdominal U-HRCT images even when scanned with a reduced radiation dose. KEY POINTS: • Lower dose DLR images were superior to the standard-dose hybrid-IR images quantitatively and qualitatively at abdominal U-HRCT. • Neither hybrid-IR nor MBIR may allow for a radiation dose reduction at abdominal U-HRCT without compromising the image quality. • Because DLR allows for a reduction in the radiation dose and maintains the image quality even at the thinnest slice section, DLR should be applied to abdominal U-HRCT scans.

Utility of Radial Scanning for the Identification of Arterial Hypervascularity of Hepatocellular Carcinoma on Gadoxetic Acid-Enhanced Magnetic Resonance Images.

OBJECTIVES: This study aimed to compare the accuracy of assessing the arterial hypervascularity of hepatocellular carcinoma (HCC) on dynamic computed tomography (CT) scans and gadoxetic acid (EOB)-enhanced magnetic resonance imaging (MRI) scans performed with radial sampling. METHODS: We studied the images of 40 patients with hypervascular HCC. A radiologist recorded the standard deviation of the attenuation (or the signal intensity [SI]) in subcutaneous fat tissue as the image noise (N) and calculated the contrast-to-noise ratio (CNR) as follows: (CNR) = (n-ROIT - n-ROIL)/N, where n-ROIT is the mean attenuation (or SI) of the tumor divided by the mean attenuation (or SI) of the aorta and n-ROIL is the mean attenuation (or SI) of the liver parenchyma divided by the mean attenuation (or SI) of the aorta. RESULTS: The CNR was significantly higher on EOB-enhanced MRI than on dynamic CT scans. CONCLUSIONS: For the assessment of HCC vascularity, EOB-enhanced MRI scans acquired with radial sampling were more accurate than dynamic CT images.

Advanced CT techniques for assessing hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is the sixth-most common cancer in the world, and hepatic dynamic CT studies are routinely performed for its evaluation. Ongoing studies are examining advanced imaging techniques that may yield better findings than are obtained with conventional hepatic dynamic CT scanning. Dual-energy CT-, perfusion CT-, and artificial intelligence-based methods can be used for the precise characterization of liver tumors, the quantification of treatment responses, and for predicting the overall survival rate of patients. In this review, the advantages and disadvantages of conventional hepatic dynamic CT imaging are reviewed and the general principles of dual-energy- and perfusion CT, and the clinical applications and limitations of these technologies are discussed with respect to HCC. Finally, we address the utility of artificial intelligence-based methods for diagnosing HCC.

Possibility of Deep Learning in Medical Imaging Focusing Improvement of Computed Tomography Image Quality.

Deep learning (DL), part of a broader family of machine learning methods, is based on learning data representations rather than task-specific algorithms. Deep learning can be used to improve the image quality of clinical scans with image noise reduction. We review the ability of DL to reduce the image noise, present the advantages and disadvantages of computed tomography image reconstruction, and examine the potential value of new DL-based computed tomography image reconstruction.

Sequential Scalp Assessment in Hair Regeneration Therapy Using an Adipose-Derived Stem Cell-Conditioned Medium.

BACKGROUND: An adipose-derived stem cell-conditioned medium (ADSC-CM) reportedly exerts skin-rejuvenating and hair growth-promoting effects. In the therapeutic application of ADSC-CM for alopecia, changes to the interfollicular scalp remain unclear although some evidence has indicated hair growth-promoting effects. OBJECTIVE: To evaluate the effects of ADSC-CM not only on hair follicles, but also on the interfollicular scalp. METHODS: Forty patients (21 men, 19 women; age range, 23-74 years) with alopecia were treated by intradermal injection of ADSC-CM every month for 6 months. Eighty fixed sites on patients were investigated by trichograms, physiological examinations, and ultrasonographic examinations at 4 time points (before treatment and 2, 4, and 6 months after the initial treatment). RESULTS: Hair density and anagen hair rate increased significantly. As physiological parameters, transepidermal water loss value gradually increased, with significant differences at 4 and 6 months after the initial treatment, but hydration state of the stratum corneum and skin surface lipid level showed no obvious changes. As ultrasonographic parameters, dermal thickness and dermal echogenicity were increased significantly. CONCLUSION: Intradermal administration of ADSC-CM on the scalp has strong potential to provide regenerative effects for hair follicles and the interfollicular scalp. An adipose-derived stem cell-conditioned medium offers a promising prospect as an alternative treatment for alopecia.

Deep learning reconstruction improves image quality of abdominal ultra-high-resolution CT.

OBJECTIVES: Deep learning reconstruction (DLR) is a new reconstruction method; it introduces deep convolutional neural networks into the reconstruction flow. This study was conducted in order to examine the clinical applicability of abdominal ultra-high-resolution CT (U-HRCT) exams reconstructed with a new DLR in comparison to hybrid and model-based iterative reconstruction (hybrid-IR, MBIR). METHODS: Our retrospective study included 46 patients seen between December 2017 and April 2018. A radiologist recorded the standard deviation of attenuation in the paraspinal muscle as the image noise and calculated the contrast-to-noise ratio (CNR) for the aorta, portal vein, and liver. The overall image quality was assessed by two other radiologists and graded on a 5-point confidence scale ranging from 1 (unacceptable) to 5 (excellent). The difference between CT images subjected to hybrid-IR, MBIR, and DLR was compared. RESULTS: The image noise was significantly lower and the CNR was significantly higher on DLR than hybrid-IR and MBIR images (p < 0.01). DLR images received the highest and MBIR images the lowest scores for overall image quality. CONCLUSIONS: DLR improved the quality of abdominal U-HRCT images. KEY POINTS: • The potential degradation due to increased noise may prevent implementation of ultra-high-resolution CT in the abdomen. • Image noise and overall image quality for hepatic ultra-high-resolution CT images improved with deep learning reconstruction as compared to hybrid- and model-based iterative reconstruction.

Correction to: Deep learning reconstruction improves image quality of abdominal ultra-high-resolution CT.

The original version of this article, published on 11 April 2019, unfortunately, contained a mistake. The following correction has therefore been made in the original: The image in Fig. 3c was wrong. The corrected figure is given below. The original article has been corrected.

Clinical staging of upper urinary tract urothelial carcinoma for T staging: Review and pictorial essay.

Upper urinary tract urothelial carcinoma is staged using the TNM classification of malignant tumors. Preoperative TNM is important for treatment planning. Computed tomography urography is now widely used for clinical survey of upper urinary tract carcinoma because of its diagnostic accuracy. Computed tomography urography is recommended as the first-line imaging procedure in several guidelines. Several reports stated that computed tomography urography is also useful for staging. However, no educational and practical reviews detailing the T staging of upper urinary tract urothelial carcinomas using imaging are available. We discuss the scanning protocol, T staging using computed tomography urography, limitations, magnetic resonance imaging, computed tomography comparison and pitfalls in imaging of upper urinary tract urothelial carcinoma. A recent study reported the high diagnostic accuracy of computed tomography urography with respect to T3 or higher stage tumors. To date, images that show a Tis-T2 stage have not been reported, but various studies are ongoing. Although magnetic resonance imaging has lower spatial resolution than computed tomography urography, magnetic resonance imaging can be carried out without radiation exposure or contrast agents. Magnetic resonance imaging also offers the unique ability of diffusion-weighted imaging without contrast agent use. Some researchers reported that diffusion-weighted imaging is useful not only for detecting lesions, but for predicting the T stage and tumor grade. We recommend the appropriate use of computed tomography and magnetic resonance while considering the limitations of each modality and the pitfalls in upper urinary tract urothelial carcinoma imaging.

Implantation of Thickened Artificial Bone for Reduction of Dead Space and Prevention of Infection Between Implant and Dura in Secondary Reconstruction of the Skull.

For the treatment of skull defect compensation after neurosurgery, a customized artificial bone is often employed owing to its toughness and the relative ease of producing cosmetically good result. However, implants are vulnerable to infection and removal of implant is sometimes necessary. Several other treatment options such as autologous bone graft or free flap are likely to be considered for the secondary reconstruction to avoid reinfection; however, reimplantation of artificial bone is beneficial for the patients, being not concerned with donor site morbidity. The authors consider one of risk factors of infection of artificial bone as dead space between the implant and dura. To attain reduction of the dead space, we have employed thickened artificial bone.Between 2010 and 2014, 6 patients underwent implantation of thickened artificial bone for the secondary reconstruction.First, the infected artificial material was removed with proper debridement. More than 3 months after the closure of the infected wound, tissue expander was inserted beneath the surrounding scalp to ensure the coverage of subsequently implanted artificial bone without skin tension. The thickened artificial bone was designed from the computed tomography findings so as not to leave any dead space between the implant and dura. After optimal expansion of the scalp, the artificial bone was implanted.Postoperative courses were uneventful and the appearance of the cranial vault was satisfactory in all patients.The authors consider the use of the thickened artificial bone is easier and more suitable for patients having a skull defect, particularly in secondary reconstruction.

Comparison of Nasal Septum and Ear Cartilage as a Graft for Lower Eyelid Reconstruction.

In lower eyelid reconstruction, several types of grafts from the nasal septum, ear cartilage, buccal mucosa, and hard palate mucosa have been used for an inner layer of the lower eyelid, but there have been no studies comparing these grafts. The authors retrospectively reviewed our cases of lower eyelid reconstruction, and compared chondromucosal grafts from the nasal septum (N = 8) and ear cartilage grafts (N = 10) for an inner layer of the lower eyelid. The authors observed no significant difference in operative time, blood loss, or length of hospital stay between the "nasal septum" and "ear cartilage" groups. The final results were aesthetically and functionally satisfactory in both groups. In the nasal septum group, 1 patient suffered from perforation of the nasal septum and another patient suffered from nasal bleeding postoperatively. There were no donor site complications in the ear cartilage group. These findings indicate that both a chondromucosal graft from the nasal septum and an ear cartilage graft are good grafts for an inner layer of the lower eyelid. Regarding the donor site, however, an ear cartilage graft has the advantage of a lower complication rate.

Development of a deep-learning algorithm for age estimation on CT images of the vertebral column.

PURPOSE: The accurate age estimation of cadavers is essential for their identification. However, conventional methods fail to yield adequate age estimation especially in elderly cadavers. We developed a deep learning algorithm for age estimation on CT images of the vertebral column and checked its accuracy. METHOD: For the development of our deep learning algorithm, we included 1,120 CT data of the vertebral column of 140 patients for each of 8 age decades. The deep learning model of regression analysis based on Visual Geometry Group-16 (VGG16) was improved in its estimation accuracy by bagging. To verify its accuracy, we applied our deep learning algorithm to estimate the age of 219 cadavers who had undergone postmortem CT (PMCT). The mean difference and the mean absolute error (MAE), the standard error of the estimate (SEE) between the known- and the estimated age, were calculated. Correlation analysis using the intraclass correlation coefficient (ICC) and Bland-Altman analysis were performed to assess differences between the known- and the estimated age. RESULTS: For the 219 cadavers, the mean difference between the known- and the estimated age was 0.30 years; it was 4.36 years for the MAE, and 5.48 years for the SEE. The ICC (2,1) was 0.96 (95 % confidence interval: 0.95-0.97, p < 0.001). Bland-Altman analysis showed that there were no proportional or fixed errors (p = 0.08 and 0.41). CONCLUSIONS: Our deep learning algorithm for estimating the age of 219 cadavers on CT images of the vertebral column was more accurate than conventional methods and highly useful.

Iodine maps derived from sparse-view kV-switching dual-energy CT equipped with a deep learning reconstruction for diagnosis of hepatocellular carcinoma.

Deep learning-based spectral CT imaging (DL-SCTI) is a novel type of fast kilovolt-switching dual-energy CT equipped with a cascaded deep-learning reconstruction which completes the views missing in the sinogram space and improves the image quality in the image space because it uses deep convolutional neural networks trained on fully sampled dual-energy data acquired via dual kV rotations. We investigated the clinical utility of iodine maps generated from DL-SCTI scans for assessing hepatocellular carcinoma (HCC). In the clinical study, dynamic DL-SCTI scans (tube voltage 135 and 80 kV) were acquired in 52 patients with hypervascular HCCs whose vascularity was confirmed by CT during hepatic arteriography. Virtual monochromatic 70 keV images served as the reference images. Iodine maps were reconstructed using three-material decomposition (fat, healthy liver tissue, iodine). A radiologist calculated the contrast-to-noise ratio (CNR) during the hepatic arterial phase (CNRa) and the equilibrium phase (CNRe). In the phantom study, DL-SCTI scans (tube voltage 135 and 80 kV) were acquired to assess the accuracy of iodine maps; the iodine concentration was known. The CNRa was significantly higher on the iodine maps than on 70 keV images (p < 0.01). The CNRe was significantly higher on 70 keV images than on iodine maps (p < 0.01). The estimated iodine concentration derived from DL-SCTI scans in the phantom study was highly correlated with the known iodine concentration. It was underestimated in small-diameter modules and in large-diameter modules with an iodine concentration of less than 2.0 mgI/ml. Iodine maps generated from DL-SCTI scans can improve the CNR for HCCs during hepatic arterial phase but not during equilibrium phase in comparison with virtual monochromatic 70 keV images. Also, when the lesion is small or the iodine concentration is low, iodine quantification may result in underestimation.

Utility of Wavelet Denoising with Geometry Factor Weighting for Gadoxetic Acid-enhanced Hepatobiliary-phase MR Imaging.

PURPOSE: The wavelet denoising with geometry factor weighting (g-denoising) method can reduce the image noise by adapting to spatially varying noise levels induced by parallel imaging. The aim of this study was to investigate the clinical applicability of g-denoising on hepatobiliary-phase (HBP) images with gadoxetic acid. METHODS: We subjected 53 patients suspected of harboring hepatic neoplastic lesions to gadoxetic acid-enhanced HBP imaging with and without g-denoising (g+HBP and g-HBP). The matrix size was reduced for g+HBP images to avoid prolonging the scanning time. Two radiologists calculated the SNR, the portal vein-, and paraspinal muscle contrast-to-noise ratio (CNR) relative to the hepatic parenchyma (liver-to-portal vein- and liver-to-muscle CNR). Two other radiologists independently graded the sharpness of the liver edge, the visibility of intrahepatic vessels, the image noise, the homogeneity of liver parenchyma, and the overall image quality using a 5-point scale. Differences between g-HBP and g+HBP images were determined with the two-sided Wilcoxon signed-rank test. RESULTS: The liver-to-portal- and liver-to-muscle CNR and the SNR were significantly higher on g+HBP- than g-HBP images (P < 0.01), as was the qualitative score for the image noise, homogeneity of liver parenchyma, and overall image quality (P < 0.01). Although there were no significant differences in the scores for the sharpness of the liver edge or the score assigned for the visibility of intrahepatic vessels (P = 0.05, 0.43), with g+HBP the score was lower in three patients for the sharpness of the liver edge and in six patients for the visibility of intrahepatic vessels. CONCLUSION: At gadoxetic acid-enhanced HBP imaging, g-denoising yielded a better image quality than conventional HBP imaging although the anatomic details may be degraded.

Understanding CT imaging findings based on the underlying pathophysiology in patients with small bowel ischemia.

Because acute small bowel ischemia has a high mortality rate, it requires rapid intervention to avoid unfavorable outcomes. Computed tomography (CT) examination is important for the diagnosis of bowel ischemia. Acute small bowel ischemia can be the result of small bowel obstruction or mesenteric ischemia, including mesenteric arterial occlusion, mesenteric venous thrombosis, and non-occlusive mesenteric ischemia. The clinical significance of each CT finding is unique and depends on the underlying pathophysiology. This review describes the definition and mechanism(s) of bowel ischemia, reviews CT findings suggesting bowel ischemia, details factors involved in the development of small bowel ischemia, and presents CT findings with respect to the different factors based on the underlying pathophysiology. Such knowledge is needed for accurate treatment decisions.

Deep learning reconstruction of equilibrium phase CT images in obese patients.

PURPOSE: To compare abdominal equilibrium phase (EP) CT images of obese and non-obese patients to identify the reconstruction method that preserves the diagnostic value of images obtained in obese patients. METHODS: We compared EP images of 50 obese patients whose body mass index (BMI) exceeded 25 (group 1) with EP images of 50 non-obese patients (BMI < 25, group 2). Group 1 images were subjected to deep learning reconstruction (DLR), hybrid iterative reconstruction (hybrid-IR), and model-based IR (MBIR), group 2 images to hybrid-IR; group 2 hybrid-IR images served as the reference standard. A radiologist recorded the standard deviation of attenuation in the paraspinal muscle as the image noise. The overall image quality was assessed by 3 other radiologists; they used a confidence scale ranging from 1 (unacceptable) to 5 (excellent). Non-inferiority and potential superiority were assessed. RESULTS: With respect to the image noise, group 1 DLR- were superior to group 2 hybrid-IR images; group 1 hybrid-IR- and MBIR images were neither superior nor non-inferior to group 2 hybrid-IR images. The quality scores of only DLR images in group 1 were superior to hybrid-IR images of group 2 while the quality scores of group 1 hybrid-IR- and MBIR images were neither superior nor non-inferior to group 2 hybrid-IR images. CONCLUSIONS: DLR preserved the quality of EP images obtained in obese patients.

Deep learning reconstruction of drip-infusion cholangiography acquired with ultra-high-resolution computed tomography.

PURPOSE: Deep learning reconstruction (DLR) introduces deep convolutional neural networks into the reconstruction flow. We examined the clinical applicability of drip-infusion cholangiography (DIC) acquired on an ultra-high-resolution CT (U-HRCT) scanner reconstructed with DLR in comparison to hybrid and model-based iterative reconstruction (hybrid-IR, MBIR). METHODS: This retrospective, single-institution study included 30 patients seen between January 2018 and November 2019. A radiologist recorded the standard deviation of attenuation in the paraspinal muscle as the image noise and calculated the contrast-to-noise ratio (CNR) in the common bile duct. The overall visual image quality of the bile duct on thick-slab maximum intensity projections was assessed by two other radiologists and graded on a 5-point confidence scale ranging from 1 (not delineated) to 5 (clearly delineated). The difference among hybrid-IR, MBIR, and DLR images was compared. RESULTS: The image noise was significantly lower on DLR than hybrid-IR and MBIR images and the CNR and the overall visual image quality of the bile duct were significantly higher on DLR than on hybrid-IR and MBIR images (all: p < 0.001). CONCLUSION: DLR resulted in significant quantitative and qualitative improvement of DIC acquired with U-HRCT.

Pseudo-random Trajectory Scanning Suppresses Motion Artifacts on Gadoxetic Acid-enhanced Hepatobiliary-phase Magnetic Resonance Images.

PURPOSE: Hepatobiliary-phase (HBP) MRI with gadoxetic acid facilitates the differentiation between lesions with and without functional hepatocytes. Thus, high-quality HBP images are required for the detection and evaluation of hepatic lesions. However, the long scan time may increase artifacts due to intestinal peristalsis, resulting in the loss of diagnostic information. Pseudo-random acquisition order disperses artifacts into the background. The aim of this study was to investigate the clinical applicability of pseudo-random trajectory scanning for the suppression of motion artifacts on T1-weighted images including HBP. METHODS: Our investigation included computer simulation, phantom experiments, and a clinical study. For computer simulation and phantom experiments a region of interest (ROI) was placed on the area with motion artifact and the standard deviation inside the ROI was measured as image noise. For clinical study we subjected 62 patients to gadoxetic acid-enhanced hepatobiliary-phase imaging with a circular- and a pseudo-random trajectory (c-HBP and p-HBP); two radiologists graded the motion artifacts, sharpness of the liver edge, visibility of intrahepatic vessels, and overall image quality using a five-point scale where 1 = unacceptable and 5 = excellent. Differences in the qualitative scores were determined using the two-sided Wilcoxon signed-rank test. RESULTS: The image noise was higher on the circular image compared with pseudo-random image (101.0 vs 60.9 on computer simulation image, 91.2 vs 67.7 on axial, 95.5 vs 86.9 on reformatted sagittal image for phantom experiments). For clinical study the score for motion artifacts was significantly higher with p-HBP than c-HBP imaging (left lobe: mean 3.4 vs 3.2, P < 0.01; right lobe: mean 3.6 vs 3.4, P < 0.01) as was the qualitative score for the overall image quality (mean 3.6 vs 3.3, P < 0.01). CONCLUSION: At gadoxetic acid-enhanced hepatobiliary-phase imaging, p-HBP scanning suppressed motion artifacts and yielded better image quality than c-HBP scanning.

Hair Regeneration Therapy: Application of Adipose-Derived Stem Cells.

BACKGROUND: Adipose-derived stem cells secrete various cytokines that promote hair growth. OBJECTIVE: To describe our experience of hair regeneration therapy using adipose-derived stem cellconditioned medium. RESULTS: We performed the hair regeneration therapy in numerous Japanese patients and reported good results. We described characteristics of the commercialized conditioned medium, treatment methods, and future directions. CONCLUSION: Treatment using adipose-derived stem cell-conditioned medium is highly effective and may represent a new therapy for alopecia.

Preoperative asymmetry is a risk factor for reoperation in involutional blepharoptosis.

BACKGROUND: Patients with involutional blepharoptosis sometimes require reoperation because of functional or esthetic reasons after the primary operation. Few studies have analyzed the risk factors for reoperation in such cases. METHODS: We retrospectively analyzed the cases of 274 patients who underwent levator aponeurosis surgery for bilateral involutional blepharoptosis. We examined the risk factors for reoperation using univariate and multivariate analyses. RESULTS: Reoperation was performed for 89 of the 274 patients (32.5%). There was no significant difference in the rate of reoperation among surgeons. In the univariate analysis, patients with preoperative asymmetry, defined as a difference of >1 mm in the marginal reflex distance between the right and left sides, showed a significantly higher rate of reoperation (42.7%) than those without asymmetry (28.1%) (p = 0.018). Age, sex, and ptosis severity did not affect the rate of reoperation. The multivariate analysis with a logistic regression showed that preoperative asymmetry was a significant risk factor for reoperation, with an odds ratio of 1.90 (p = 0.019). CONCLUSION: In involutional blepharoptosis, patients with preoperative asymmetry should be informed of the higher risk of reoperation, and the balance between the right and left sides should be carefully adjusted intraoperatively.