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\"With the development of technology, living standards rise and people's expectations increase. This situation makes itself felt strikingly especially in the medical field. The use of medical devices is rapidly increasing to protect human health. It is very important to quickly evaluate the images obtained from these medical imaging devices. For this purpose, artificial intelligence (AI) methods are used. While hand-crafted methods were preferred in the past, more advanced methods are preferred today. CNN architectures are one of the most effective AI methods today. This book contains applications for the use of CNN methods for medical applications. The content of the book, in which different CNN methods are applied to various medical image processing problems, is quite extensive. Readers will be able to comprehensively analyze the effects of CNN methods presented in the book on medical applications\"-- Provided by publisher.

Objectives To prospectively compare the diagnostic performance of reduced-dose (RD) contrast-enhanced CT (CECT) with standard-dose (SD) CECT for detection of low-contrast liver lesions. Methods Seventy adults with non-liver primary malignancies underwent abdominal SD-CECT immediately followed by RD-CECT, aggressively targeted at 60-70 % dose reduction. SD series were reconstructed using FBP. RD series were reconstructed with FBP, ASIR, and MBIR (Veo). Three readers—blinded to clinical history and comparison studies—reviewed all series, identifying liver lesions ≥4 mm. Non-blinded review by two experienced abdominal radiologists—assessing SD against available clinical and radiologic information—established the reference standard. Results RD-CECT mean effective dose was 2.01 ± 1.36 mSv (median, 1.71), a 64.1 ± 8.8 % reduction. Pooled per-patient performance data were (sensitivity/specificity/PPV/NPV/accuracy) 0.91/0.78/0.60/0.96/0.81 for SD-FBP compared with RD-FBP 0.79/0.75/0.54/0.91/0.76; RD-ASIR 0.84/0.75/0.56/0.93/0.78; and RD-MBIR 0.84/0.68/0.49/0.92/0.72. ROC AUC values were 0.896/0.834/0.858/0.854 for SD-FBP/RD-FBP/RD-ASIR/RD-MBIR, respectively. RD-FBP ( P  = 0.002) and RD-MBIR ( P  = 0.032) AUCs were significantly lower than those of SD-FBP; RD-ASIR was not ( P  = 0.052). Reader confidence was lower for all RD series ( P  < 0.001) compared with SD-FBP, especially when calling patients entirely negative. Conclusions Aggressive CT dose reduction resulted in inferior diagnostic performance and reader confidence for detection of low-contrast liver lesions compared to SD. Relative to RD-ASIR, RD-FBP showed decreased sensitivity and RD-MBIR showed decreased specificity. Key Points • Reduced-dose CECT demonstrates inferior diagnostic performance for detecting low-contrast liver lesions . • Reader confidence is lower with reduced-dose CECT compared to standard-dose CECT . • Overly aggressive dose reduction may result in misdiagnosis, regardless of reconstruction algorithm . • Careful consideration of perceived risks versus benefits of dose reduction is crucial .

A pathological evaluation is one of the most important methods for the diagnosis of malignant lymphoma. A standardized diagnosis is occasionally difficult to achieve even by experienced hematopathologists. Therefore, established procedures including a computer-aided diagnosis are desired. This study aims to classify histopathological images of malignant lymphomas through deep learning, which is a computer algorithm and type of artificial intelligence (AI) technology. We prepared hematoxylin and eosin (H&E) slides of a lesion area from 388 sections, namely, 259 with diffuse large B-cell lymphoma, 89 with follicular lymphoma, and 40 with reactive lymphoid hyperplasia, and created whole slide images (WSIs) using a whole slide system. WSI was annotated in the lesion area by experienced hematopathologists. Image patches were cropped from the WSI to train and evaluate the classifiers. Image patches at magnifications of ×5, ×20, and ×40 were randomly divided into a test set and a training and evaluation set. The classifier was assessed using the test set through a cross-validation after training. The classifier achieved the highest levels of accuracy of 94.0%, 93.0%, and 92.0% for image patches with magnifications of ×5, ×20, and ×40, respectively, in comparison to diffuse large B-cell lymphoma, follicular lymphoma, and reactive lymphoid hyperplasia. Comparing the diagnostic accuracies between the proposed classifier and seven pathologists, including experienced hematopathologists, using the test set made up of image patches with magnifications of ×5, ×20, and ×40, the best accuracy demonstrated by the classifier was 97.0%, whereas the average accuracy achieved by the pathologists using WSIs was 76.0%, with the highest accuracy reaching 83.3%. In conclusion, the neural classifier can outperform pathologists in a morphological evaluation. These results suggest that the AI system can potentially support the diagnosis of malignant lymphoma. This study aims to classify histopathological images of malignant lymphoma through deep learning. The classifier achieved the high levels of accuracy in comparison to diffuse large B-cell lymphoma, follicular lymphoma, and reactive lymphoid hyperplasia, which were higher than those of pathologists. Artificial intelligence can potentially support diagnosis of malignant lymphoma.

\"Data mining has rapidly emerged as an enabling, robust, and scalable technique to analyze data for novel patterns, trends, anomalies, structures, and features that can be employed for a variety of biomedical and clinical domains. Approaching the techniques and challenges of image mining from a multidisciplinary perspective, this book presents data mining techniques, methodologies, algorithms, and strategies to analyze biomedical signals and images. Written by experts, the text addresses data mining paradigms for the development of biomedical systems. It also includes special coverage of knowledge discovery in mammograms and emphasizes both the diagnostic and therapeutic fields of eye imaging\"--Provided by publisher.

Objectives To explore the use of 70-kVp tube voltage combined with high-strength deep learning image reconstruction (DLIR-H) in reducing radiation and contrast doses in coronary CT angiography (CCTA) in patients with body mass index (BMI) < 26 kg/m 2 , in comparison with the conventional scan protocol using 120 kVp and adaptive statistical iterative reconstruction (ASIR-V). Methods A total of 100 patients referred to CCTA were prospectively enrolled and randomly divided into two groups: low-dose group ( n  = 50) with 70 kVp, Smart mA for noise index (NI) of 36HU, contrast dose rate of 16mgI/kg/s, and DLIR-H, and conventional group ( n  = 50) with 120 kV, Smart mA for NI of 25HU, contrast dose rate of 32mgI/kg/s, and 60%ASIR-V. Radiation and contrast dose, subjective image quality score, and objective image quality measurement (image noise, contrast-noise-ratio (CNR), and signal–noise-ratio (SNR) for vessel) were compared between the two groups. Results Low-dose group used significantly reduced contrast dose (23.82 ± 3.69 mL, 50.6% reduction) and radiation dose (0.75 ± 0.14 mSv, 54.5% reduction) compared to the conventional group (48.23 ± 6.38 mL and 1.65 ± 0.66 mSv, respectively) (all p  < 0.001). Both groups had similar enhancement in vessels. However, the low-dose group had lower background noise (23.57 ± 4.74 HU vs. 35.04 ± 8.41 HU), higher CNR in RCA (48.63 ± 10.76 vs. 29.32 ± 5.52), LAD (47.33 ± 10.20 vs. 29.27 ± 5.12), and LCX (46.74 ± 9.76 vs. 28.58 ± 5.12) (all p  < 0.001) compared to the conventional group. Conclusions The use of 70-kVp tube voltage combined with DLIR-H for CCTA in normal size patients significantly reduces radiation dose and contrast dose while further improving image quality compared with the conventional 120-kVp tube voltage with 60%ASIR-V. Key Points •  The combination of 70-kVp tube voltage and high-strength deep learning image reconstruction (DLIR-H) algorithm protocol reduces approximately 50% of radiation and contrast doses in coronary computed tomography angiography (CCTA) compared with the conventional scan protocol . •  CCTA of normal size (BMI < 26 kg/m 2 ) patients acquired at sub-mSv radiation dose and 24 mL contrast dose through the combination of 70-kVp tube voltage and DLIR-H algorithm achieves excellent diagnostic image quality with a good inter-rater agreement . •  DLIR-H algorithm shows a higher capacity of significantly reducing image noise than adaptive statistical iterative reconstruction algorithm in CCTA examination .

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).

Hepatic steatosis droplet quantification with histology biopsies has high clinical significance for risk stratification and management of patients with fatty liver diseases and in the decision to use donor livers for transplantation. However, pathology reviewing processes, when conducted manually, are subject to a high inter- and intra-reader variability, due to the overwhelmingly large number and significantly varying appearance of steatosis instances. This process is challenging as there is a large number of overlapped steatosis droplets with either missing or weak boundaries. In this study, we propose a deep-learning-based region-boundary integrated network for precise steatosis quantification with whole slide liver histopathology images. The proposed model consists of two sequential steps: a region extraction and a boundary prediction module for foreground regions and steatosis boundary prediction, followed by an integrated prediction map generation. Missing steatosis boundaries are next recovered from the predicted map and assembled from adjacent image patches to generate results for the whole slide histopathology image. The resulting steatosis measures both at the pixel level and steatosis object-level present strong correlation with pathologist annotations, radiology readouts and clinical data. In addition, the segregated steatosis object count is shown as a promising alternative measure to the traditional metrics at the pixel level. These results suggest a high potential of artificial intelligence-assisted technology to enhance liver disease decision support using whole slide images. Accurate quantification of steatosis in liver biopsies is a key step in the treatment of patients with fatty liver diseases. To assist pathologists for such analysis tasks, we develop a novel deep learning-based framework to segment overlapped steatosis droplets in whole slide liver biopsy images. Quantitative measurements of steatosis at both pixel and object-level present strong correlation with clinical data, suggesting its potential for clinical decision support.

Purpose To evaluate the quality of lung perfusion imaging obtained with photon-counting-detector CT (PCD-CT) in comparison with dual-source, dual-energy CT (DECT). Methods Seventy-one consecutive patients scanned with PCD-CT were compared to a paired population scanned with dual-energy on a 3rd-generation DS-CT scanner using (a) for DS-CT (Group 1): collimation: 64 × 0.6 × 2 mm; pitch: 0.55; (b) for PCD-CT (Group 2): collimation: 144 × 0.4 mm; pitch: 1.5; single-source acquisition. The injection protocol was similar in both groups with the reconstruction of perfusion images by subtraction of high- and low-energy virtual monoenergetic images. Results Compared to Group 1, Group 2 examinations showed: (a) a shorter duration of data acquisition (0.93 ± 0.1 s vs 3.98 ± 0.35 s; p  < 0.0001); (b) a significantly lower dose-length-product (172.6 ± 55.14 vs 339.4 ± 75.64 mGy·cm; p  < 0.0001); and (c) a higher level of objective noise ( p  < 0.0001) on mediastinal images. On perfusion images: (a) the mean level of attenuation did not differ ( p  = 0.05) with less subjective image noise in Group 2 ( p  = 0.049); (b) the distribution of scores of fissure visualization differed between the 2 groups ( p  < 0.0001) with a higher proportion of fissures sharply delineated in Group 2 ( n  = 60; 84.5% vs n  = 26; 26.6%); (c) the rating of cardiac motion artifacts differed between the 2 groups ( p  < 0.0001) with a predominance of examinations rated with mild artifacts in Group 2 ( n  = 69; 97.2%) while the most Group 1 examinations showed moderate artifacts ( n  = 52; 73.2%). Conclusion PCD-CT acquisitions provided similar morphologic image quality and superior perfusion imaging at lower radiation doses. Clinical relevance statement The improvement in the overall quality of perfusion images at lower radiation doses opens the door for wider applications of lung perfusion imaging in clinical practice. Key Points The speed of data acquisition with PCD-CT accounts for mild motion artifacts . Sharply delineated fissures are depicted on PCD-CT perfusion images . High-quality perfusion imaging was obtained with a 52% dose reduction .

Objectives To investigate the feasibility of low-radiation dose and low iodinated contrast medium (ICM) dose protocol combining low-tube voltage and deep-learning reconstruction (DLR) algorithm in thin-slice abdominal CT. Methods This prospective study included 148 patients who underwent contrast-enhanced abdominal CT with either 120-kVp (600 mgL/kg, n  = 74) or 80-kVp protocol (360 mgL/kg, n  = 74). The 120-kVp images were reconstructed using hybrid iterative reconstruction (HIR) (120-kVp-HIR), while 80-kVp images were reconstructed using HIR (80-kVp-HIR) and DLR (80-kVp-DLR) with 0.5 mm thickness. Size-specific dose estimate (SSDE) and iodine dose were compared between protocols. Image noise, CT attenuation, and contrast-to-noise ratio (CNR) were quantified. Noise power spectrum (NPS) and edge rise slope (ERS) were used to evaluate noise texture and edge sharpness, respectively. The subjective image quality was rated on a 4-point scale. Results SSDE and iodine doses of 80-kVp were 40.4% (8.1 ± 0.9 vs. 13.6 ± 2.7 mGy) and 36.3% (21.2 ± 3.9 vs. 33.3 ± 4.3 gL) lower, respectively, than those of 120-kVp (both, p  < 0.001). CT attenuation of vessels and solid organs was higher in 80-kVp than in 120-kVp images (all, p  < 0.001). Image noise of 80-kVp-HIR and 80-kVp-DLR was higher and lower, respectively than that of 120-kVp-HIR (both p  < 0.001). The highest CNR and subjective scores were attained in 80-kVp-DLR (all, p  < 0.001). There were no significant differences in average NPS frequency and ERS between 120-kVp-HIR and 80-kVp-DLR ( p  ≥ 0.38). Conclusion Compared with the 120-kVp-HIR protocol, the combined use of 80-kVp and DLR techniques yielded superior subjective and objective image quality with reduced radiation and ICM doses at thin-section abdominal CT. Clinical relevance statement Scanning at low-tube voltage (80-kVp) combined with the deep-learning reconstruction algorithm may enhance diagnostic efficiency and patient safety by improving image quality and reducing radiation and contrast doses of thin-slice abdominal CT. Key Points Reducing radiation and iodine doses is desirable; however, contrast and noise degradation can be detrimental. The 80-kVp scan with the deep-learning reconstruction technique provided better images with lower radiation and contrast doses. This technique may be efficient for improving diagnostic confidence and patient safety in thin-slice abdominal CT.