Thin-slice 2D MR Imaging of the Shoulder Joint Using Denoising Deep Learning Reconstruction Provides Higher Image Quality Than 3D MR Imaging.

Purpose: This study was conducted to evaluate whether thin-slice 2D fat-saturated proton density-weighted images of the shoulder joint in three imaging planes combined with parallel imaging, partial Fourier technique, and denoising approach with deep learning-based reconstruction (dDLR) are more useful than 3D fat-saturated proton density multi-planar voxel images.

Methods: Eighteen patients who underwent MRI of the shoulder joint at 3T were enrolled. The denoising effect of dDLR in 2D was evaluated using coefficient of variation (CV).

Thin-slice reverse encoding distortion correction DWI facilitates visualization of non-functioning pituitary neuroendocrine tumor (PitNET)/pituitary adenoma and surrounding normal structures.

Background: To evaluate the clinical usefulness of thin-slice echo-planar imaging (EPI)-based diffusion-weighted imaging (DWI) with an on-console distortion correction technique, termed reverse encoding distortion correction DWI (RDC-DWI), in patients with non-functioning pituitary neuroendocrine tumor (PitNET)/pituitary adenoma.

Methods: Patients with non-functioning PitNET/pituitary adenoma who underwent 3-T RDC-DWI between December 2021 and September 2022 were retrospectively enrolled. Image quality was compared among RDC-DWI, DWI with correction for distortion induced by B inhomogeneity alone (B-corrected-DWI), and original EPI-based DWI with anterior-posterior phase-encoding direction (AP-DWI).

F-FDG PET/CT in left atrial undifferentiated pleomorphic sarcoma with osteosarcomatous differentiation.

Unlabelled: Primary cardiac sarcomas are rare and sometimes difficult to discern from benign tumors and intracardiac thrombi. We describe the ultrasound, computed tomography (CT), magnetic resonance imaging (MRI), and F-fluorodeoxyglucose (F-FDG) positron emission tomography (PET)/CT findings in a case of left atrial undifferentiated pleomorphic sarcoma with osteosarcomatous differentiation, presenting with severe mitral regurgitation and pulmonary hypertension. The tumor presented as a broad-base mass protruding into the cardiac lumen, accompanied by punctate calcification-like high attenuation on CT.

Multiparametric Approach to Breast Cancer With Emphasis on Magnetic Resonance Imaging in the Era of Personalized Breast Cancer Treatment.

A multiparametric approach to breast cancer imaging offers the advantage of integrating the diverse contributions of various parameters. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is the most important MRI sequence for breast imaging. The vascularity and permeability of lesions can be estimated through the use of semiquantitative and quantitative parameters.

Evaluating Reproducibility of the ADC and Distortion in Diffusion-weighted Imaging (DWI) with Reverse Encoding Distortion Correction (RDC).

Purpose: To compare image distortion and reproducibility of quantitative values between reverse encoding distortion correction (RDC) diffusion-weighted imaging (DWI) and conventional DWI techniques in a phantom study and in healthy volunteers.

Methods: This prospective study was conducted with the approval of our institutional review board. Written informed consent was obtained from each participant.

Clinical Applications of Dedicated Breast Positron Emission Tomography.

Breast-specific positron imaging systems provide higher sensitivity than whole-body PET for breast cancer detection. The clinical applications for breast-specific positron imaging are similar to breast MRI including preoperative local staging and neoadjuvant therapy response assessment. Breast-specific positron imaging may be an alternative for patients who cannot undergo breast MRI.

F-labeled PEGylated exendin-4 imaging noninvasively differentiates insulinoma from an accessory spleen: the first case report of [18F]FB(ePEG12)12-exendin-4 positron emission tomography/computed tomography for insulinoma.

Background: Insulinomas are the most common functioning pancreatic neuroendocrine neoplasms, and these tumors induce hypoglycemia due to hyperinsulinemia. Hypoglycemia caused by insulinomas can cause seizures, coma or death due to the delayed diagnosis. The only curative treatment is surgical resection.

Noninvasive evaluation of donor and native pancreases following simultaneous pancreas-kidney transplantation using positron emission tomography/computed tomography.

It is crucial to develop practical and noninvasive methods to assess the functional beta-cell mass in a donor pancreas, in which monitoring and precise evaluation is challenging. A patient with type 1 diabetes underwent noninvasive imaging following simultaneous kidney-pancreas transplantation with positron emission tomography/computed tomography (PET/CT) using an exendin-based probe, [ F]FB(ePEG12)12-exendin-4. Following transplantation, PET imaging with [ F]FB(ePEG12)12-exendin-4 revealed simultaneous and distinct accumulations in the donor and native pancreases.

Prognostic value of pretreatment FDG PET/CT in uterine cervical cancer according to two major histologic types: squamous cell carcinoma and adenocarcinoma.

Objectives: The aim of this study was to assess the prognostic value of pretreatment Positron emission tomography / computed tomography using F-fluorodeoxyglucose (FDG-PET/CT) in cervical cancer according to two major histologic types.

Methods: Eighty-three squamous cell carcinoma (SCC) patients and 35 adenocarcinoma (AC) patients who underwent pretreatment FDG-PET/CT were retrospectively analyzed. Maximum standardized uptake value (SUV), mean standardized uptake value (SUV), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) of the primary tumor were calculated.

Feasibility of Dedicated Breast Positron Emission Tomography Image Denoising Using a Residual Neural Network.

Objectives: This study aimed to create a deep learning (DL)-based denoising model using a residual neural network (Res-Net) trained to reduce noise in ring-type dedicated breast positron emission tomography (dbPET) images acquired in about half the emission time, and to evaluate the feasibility and the effectiveness of the model in terms of its noise reduction performance and preservation of quantitative values compared to conventional post-image filtering techniques.

Methods: Low-count (LC) and full-count (FC) PET images with acquisition durations of 3 and 7 minutes, respectively, were reconstructed. A Res-Net was trained to create a noise reduction model using fifteen patients' data.

Performance of dedicated breast PET in breast cancer screening: comparison with digital mammography plus digital breast tomosynthesis and ultrasound.

Objective: To compare the diagnostic performance of dedicated breast positron emission tomography (dbPET) in breast cancer screening with digital mammography plus digital breast tomosynthesis (DM-DBT) and breast ultrasound (US).

Methods: Women who participated in opportunistic whole-body PET/computed tomography cancer screening programs with breast examinations using dbPET, DM-DBT, and US between 2016-2020, whose results were determined pathologically or by follow-up for at least 1 year, were included. DbPET, DM-DBT, and US assessments were classified into four diagnostic categories: A (no abnormality), B (mild abnormality), C (need for follow-up), and D (recommend further examination).

Denoising approach with deep learning-based reconstruction for neuromelanin-sensitive MRI: image quality and diagnostic performance.

Purpose: Neuromelanin-sensitive MRI (NM-MRI) has proven useful for diagnosing Parkinson's disease (PD) by showing reduced signals in the substantia nigra (SN) and locus coeruleus (LC), but requires a long scan time. The aim of this study was to assess the image quality and diagnostic performance of NM-MRI with a shortened scan time using a denoising approach with deep learning-based reconstruction (dDLR).

Materials And Methods: We enrolled 22 healthy volunteers, 22 non-PD patients and 22 patients with PD who underwent NM-MRI, and performed manual ROI-based analysis.

Evaluation of breast lesions based on modified BI-RADS using high-resolution readout-segmented diffusion-weighted echo-planar imaging and T2/T1-weighted image.

Purpose: To evaluate the diagnostic performance of a non-contrast magnetic resonance imaging (MRI) protocol combining high-resolution diffusion-weighted images (HR-DWI) using readout-segmented echo planar imaging, T1-weighted imaging (T1WI), and T2-weighted imaging (T2WI), using our modified Breast Imaging-Reporting and Data System (modified BI-RADS).

Methods: Two experienced radiologists, blinded to the final pathological diagnosis, categorized a total of 108 breast lesions (61 malignant and 47 benign) acquired with the above protocol using the modified BI-RADS with a diagnostic decision tree. The decision tree included subcategories of category 4, as in mammography (categories 2, 3, 4A, 4B, 4C, and 5).

Reproducibility assessment of uptake on dedicated breast PET for noise discrimination.

Objectives: Dedicated breast PET (dbPET) systems have improved the detection of small breast cancers but have increased false-positive diagnoses due to an increased chance of noise detection. This study examined whether reproducibility assessment using paired images helped to improve noise discrimination and diagnostic performance in dbPET.

Methods: This study included 21 patients with newly diagnosed breast cancer who underwent [F]FDG-dbPET and contrast-enhanced breast MRI.

Multiparametric imaging of breast cancer: An update of current applications.

Imaging plays an indispensable role in the diagnosis and treatment of breast cancer. Several new imaging tools are currently being developed for clinical use to improve diagnostic performance and tumor response evaluation. Abbreviated magnetic resonance imaging (MRI) allows shortening scanning time without penalizing diagnostic performances.

Super-resolution application of generative adversarial network on brain time-of-flight MR angiography: image quality and diagnostic utility evaluation.

Objectives: To develop a generative adversarial network (GAN) model to improve image resolution of brain time-of-flight MR angiography (TOF-MRA) and to evaluate the image quality and diagnostic utility of the reconstructed images.

Methods: We included 180 patients who underwent 1-min low-resolution (LR) and 4-min high-resolution (routine) brain TOF-MRA scans. We used 50 patients' datasets for training, 12 for quantitative image quality evaluation, and the rest for diagnostic validation.

Visual Evaluation of Ultrafast MRI in the Assessment of Residual Breast Cancer after Neoadjuvant Systemic Therapy: A Preliminary Study Association with Subtype.

The purpose of this study was to investigate the diagnostic performance of ultrafast DCE (UF-DCE) MRI after the completion of neoadjuvant systemic therapy (NST) in breast cancer. In this study, MR examinations of 55 post-NST breast cancers were retrospectively analyzed. Residual tumor sizes were measured in the 20th phase of UF-DCE MRI, early and delayed phases of conventional DCE MRI, and high spatial-resolution CE MRI (UF, early, delayed, and HR, respectively).

Diagnostic advantage of thin slice 2D MRI and multiplanar reconstruction of the knee joint using deep learning based denoising approach.

The purpose of this study is to evaluate whether thin-slice high-resolution 2D fat-suppressed proton density-weighted image of the knee joint using denoising approach with deep learning-based reconstruction (dDLR) with MPR is more useful than 3D FS-PD multi planar voxel image. Twelve patients who underwent MRI of the knee at 3T and 13 knees were enrolled. Denoising effect was quantitatively evaluated by comparing the coefficient of variation (CV) before and after dDLR.

Evaluation of pathological complete response after neoadjuvant systemic treatment of invasive breast cancer using diffusion-weighted imaging compared with dynamic contrast-enhanced based kinetic analysis.

Purpose: This study compared the performance of diffusion-weighted imaging (DWI) to dynamic contrast-enhanced (DCE)-MRI for diagnosing pathological complete response (pCR) before surgery.

Method: Overall, 133 lesions from 133 patients who underwent pre-surgical MRI evaluation after neoadjuvant systemic treatment were included. Two readers blinded to the pathological diagnosis evaluated the images.

Deep Learning-based Noise Reduction for Fast Volume Diffusion Tensor Imaging: Assessing the Noise Reduction Effect and Reliability of Diffusion Metrics.

To assess the feasibility of a denoising approach with deep learning-based reconstruction (dDLR) for fast volume simultaneous multi-slice diffusion tensor imaging of the brain, noise reduction effects and the reliability of diffusion metrics were evaluated with 20 patients. Image noise was significantly decreased with dDLR. Although fractional anisotropy (FA) of deep gray matter was overestimated when the number of image acquisitions was one (NAQ1), FA in NAQ1 with dDLR became closer to that in NAQ5.

Subcategory classifications of Breast Imaging and Data System (BI-RADS) category 4 lesions on MRI.

Purpose: Category 4 in BI-RADS for magnetic resonance imaging (MRI) has a wide range of probabilities of malignancy, extending from > 2 to < 95%. We classified category 4 lesions into three subcategories and analyzed the positive predictive value (PPV) of malignancy in a tertiary hospital.

Materials And Methods: This retrospective study included 346 breast MRIs with 434 category 2-5 lesions.