Publications by authors named "Motonori Akagi"
Article Synopsis
- - Hepatocellular carcinoma (HCC) is a major global health issue, ranking as the sixth-most common cancer, with hepatic dynamic CT scans being commonly used for its evaluation.
- - Current research is exploring advanced imaging techniques, such as dual-energy CT, perfusion CT, and artificial intelligence methods, which may improve the accuracy of liver tumor characterization, treatment response assessment, and patient survival predictions.
- - This review discusses the pros and cons of traditional hepatic dynamic CT, the fundamentals and clinical uses of new imaging technologies, and the role of AI in HCC diagnosis.
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.
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.
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.
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.
Purpose: In recent years, it has been reported that use of F-FDG PET-CT can reveal the degree of hepatocellular carcinoma malignancy. We evaluate the ability of a preoperative F-FDG PET-CT to predict the recurrence of extrahepatic metastasis of HCC after surgery.
Methods: We retrospectively examined 67 patients who received F-FDG PET-CT prior to curative hepatic resection for HCC between April 2010 and March 2016.
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.
View Article and Find Full Text PDFUpper 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.
View Article and Find Full Text PDFThe 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.
View Article and Find Full Text PDFObjectives: 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.
Article Synopsis
- The study focuses on improving the quality of hepatobiliary-phase (HBP) MRI images using pseudo-random acquisition techniques to reduce motion artifacts, which can hinder diagnostic accuracy due to intestinal movement during scans.
- Through computer simulations, phantom experiments, and a clinical study involving 62 patients, researchers compared standard circular trajectory imaging with pseudo-random trajectory imaging.
- Results indicated that pseudo-random imaging significantly reduced image noise and improved scores related to motion artifacts and overall image quality, suggesting it is a more effective method for HBP MRI with gadoxetic acid.
Article Synopsis
- Diffusion-weighted imaging (DW imaging) is a vital magnetic resonance imaging technique used primarily for diagnosing acute cerebral infarctions and for oncologic assessments.
- Computed DW imaging synthesizes enhanced DW images from existing measurements, resulting in images with a stronger diffusion effect and higher signal-to-noise ratios while also reducing imaging time.
- The accuracy of computed DW images depends on using the correct mathematical models (mono-, bi-, or triexponential) and ensuring proper alignment of the input data to minimize artifacts.
Tubulocystic renal cell carcinoma (TC-RCC) has been classified as an independent subtype according to the 2016 World Health Organization (WHO) classification. It is a rare subtype that predominantly affects men. Although few in number, radiological imaging reports have suggested that TC-RCC is characterized by multilocular cystic lesions, which are categorized as the Bosniak classification II-IV, with signature pathological characteristics comprising numerous small cysts or a tubular structure.
View Article and Find Full Text PDFObjective: To compare the utility of high-precision computed diffusion-weighted imaging (hc-DWI) and conventional computed DWI (cc-DWI) for the diagnosis of hepatocellular carcinoma (HCC) at 3 T.
Methods: We subjected 75 HCC patients to DWI (b-value 150 and 600 s/mm). To generate hc-DWI we applied non-rigid image registration to avoid the mis-registration of images obtained with different b-values.