Whole‐body diffusion‐weighted magnetic resonance imaging: Diagnosis and follow up of prostate cancer and beyond

A Self-Powered Lactate Sensor Based on the Piezoelectric Effect for Assessing Tumor Development

The build-up of lactate in solid tumors stands as a crucial and early occurrence in malignancy development, and the concentration of lactate in the tumor microenvironment may be a more sensitive indicator for analyzing primary tumors. In this study, we designed a self-powered lactate sensor for the rapid analysis of tumor samples, utilizing the coupling between the piezoelectric effect and enzymatic reaction. This lactate sensor is fabricated using a ZnO nanowire array modified with lactate oxidase (LOx). The sensing process does not require an external power source or batteries. The device can directly output electric signals containing lactate concentration information when subjected to external forces. The lactate concentration detection upper limit of the sensor is at least 27 mM, with a limit of detection (LOD) of approximately 1.3 mM and a response time of around 10 s. This study innovatively applied self-powered technology to the in situ detection of the tumor microenvironment and used the results to estimate the growth period of the primary tumor. The availability of this application has been confirmed through biological experiments. Furthermore, the sensor data generated by the device offer valuable insights for evaluating the likelihood of remote tumor metastasis. This study may expand the research scope of self-powered technology in the field of medical diagnosis and offer a novel perspective on cancer diagnosis.

Clinical Impact of Deep Learning Reconstruction in MRI

Deep learning has been recognized as a paradigm-shifting tool in radiology. Deep learning reconstruction (DLR) has recently emerged as a technology used in the image reconstruction process of MRI, which is an essential procedure in generating MR images. Denoising, which is the first DLR application to be realized in commercial MRI scanners, improves signal-to-noise ratio. When applied to lower magnetic field-strength scanners, the signal-to-noise ratio can be increased without extending the imaging time, and image quality is comparable to that of higher-field-strength scanners. Shorter imaging times decrease patient discomfort and reduce MRI scanner running costs. The incorporation of DLR into accelerated acquisition imaging techniques, such as parallel imaging or compressed sensing, shortens the reconstruction time. DLR is based on supervised learning using convolutional layers and is divided into the following three categories: image domain, k-space learning, and direct mapping types. Various studies have reported other derivatives of DLR, and several have shown the feasibility of DLR in clinical practice. Although DLR efficiently reduces Gaussian noise from MR images, denoising makes image artifacts more prominent, and a solution to this problem is desired. Depending on the training of the convolutional neural network, DLR may change the imaging features of lesions and obscure small lesions. Therefore, radiologists may need to adopt the habit of questioning whether any information has been lost on images that appear clean. ^©RSNA, 2023 Quiz questions for this article are available in the supplemental material.

Suppression of background body signals in whole-body diffusion-weighted imaging for detection of bony metastases: a pilot study

Background

Whole-body diffusion-weighted magnetic resonance is being developed as a tool for assessing tumor spread. Patients with known primary tumors require meticulous evaluation to assess metastasis for better staging; we attempted to detect bony metastasis without radiation exposure. Our study's goal was to use whole-body diffusion-weighted imaging with background body signal suppression (WB-DWBIS) to evaluate bony metastasis in confirmed patients who have primary tumors.

Results

Our study included 90 patients with known primary cancer, 10 patients were excluded as they had no bony metastasis, from 80 patients: 36 (45.0%) having one site of metastasis, 36 (45%) having two sites of metastasis, and 8 (10.0%) having three sites of metastasis. 56 (70.0%) of the metastasis sites were bony metastasis, and 76 were mixed both bony and non-bony, including 32(40.0%) lung, 16 (20.0%) liver, and 28 (35%) lymph nodes. Sensitivity of bone scanning in detecting metastasis was as follows: 95.1% sensitivity and 92.0% accuracy, while that of whole-body diffusion-weighted image with background signals suppression was 94.8% sensitivity and 91.7% accuracy, WB-DWBIS inter-observer agreement in the detection of bony metastatic deposits in cancer patients was good (0.7 45, agreement = 93.2%).

Conclusions

Using WB-DWBIS images, bone lesion identification and characterization (site and number) were improved, producing outcomes similar to bone scanning without the use of ionizing radiation.

Novel quantitative software for automatically excluding red bone marrow on whole‐body magnetic resonance imaging in patients with metastatic prostate cancer: A pilot study

OBJECTIVES

To establish a novel quantitative method that automatically excludes the red bone marrow and accurately quantifies the tumor volume on whole-body magnetic resonance imaging using updated imaging software. To also evaluate the association between the quantified tumor volume and the prognosis of patients with metastatic prostate cancer.

METHODS

This prospective analysis included patients diagnosed with metastatic hormone-sensitive or metastatic castration-resistant prostate cancer between 2017 and 2022. We developed an imaging software (Attractive BD_Score) that analyzed whole-body diffusion-weighted and in-phase and opposed-phase T1-weighted images to automatically exclude the red bone marrow. The quantified tumor volume was compared with that quantified by traditional whole-body diffusion-weighted imaging without red bone marrow exclusion. Prostate-specific antigen progression-free survival, time-to-pain progression, and overall survival were evaluated to assess the prognostic value of the quantified tumor volume.

RESULTS

The quantified tumor volume was significantly smaller than that quantified by the traditional method in metastatic hormone-sensitive (median: 81.0 ml vs. 149.4 ml) and metastatic castration-resistant (median: 29.4 ml vs. 63.5 ml) prostate cancer. A highly quantified tumor volume was associated with prostate-specific antigen progression-free survival (p = 0.030), time-to-pain progression (p = 0.003), and overall survival (p = 0.005) in patients with metastatic hormone-sensitive prostate cancer and with poor prostate-specific antigen progression-free survival (p = 0.001) and time-to-pain progression (p = 0.005) in patients with metastatic castration-resistant prostate cancer.

CONCLUSIONS

Our imaging method could accurately quantify the tumor volume in patients with metastatic prostate cancer. The quantified tumor volume can be clinically applied as a new prognostic biomarker for metastatic prostate cancer.

Feasibility of accelerated whole-body diffusion-weighted imaging using a deep learning-based noise-reduction technique in patients with prostate cancer

PURPOSE

To assess the possibility of reducing the image acquisition time for diffusion-weighted whole-body imaging with background body signal suppression (DWIBS) by denoising with deep learning-based reconstruction (dDLR).

METHODS

Seventeen patients with prostate cancer who underwent DWIBS by 1.5 T magnetic resonance imaging with a number of excitations of 2 (NEX2) and 8 (NEX8) were prospectively enrolled. The NEX2 image data were processed by dDLR (dDLR-NEX2), and the NEX2, dDLR-NEX2, and NEX8 image data were analyzed. In qualitative analysis, two radiologists rated the perceived coarseness, conspicuity of metastatic lesions (lymph nodes and bone), and overall image quality. The contrast-to-noise ratios (CNRs), contrast ratios, and mean apparent diffusion coefficients (ADCs) of metastatic lesions were calculated in a quantitative analysis.

RESULTS

The image acquisition time of NEX2 was 2.8 times shorter than that of NEX8 (3 min 30 s vs 9 min 48 s). The perceived coarseness and overall image quality scores reported by both readers were significantly higher for dDLR-NEX2 than for NEX2 (P = 0.005-0.040). There was no significant difference between dDLR-NEX2 and NEX8 in the qualitative analysis. The CNR of bone metastasis was significantly greater for dDLR-NEX2 than for NEX2 and NEX8 (P = 0.012 for both comparisons). The contrast ratios and mean ADCs were not significantly different among the three image types.

CONCLUSIONS

dDLR improved the image quality of DWIBS with NEX2. In the context of lymph node and bone metastasis evaluation with DWIBS in patients with prostate cancer, dDLR-NEX2 has potential to be an alternative to NEX8 and reduce the image acquisition time.

Low-Field Magnetic Resonance Imaging: Its History and Renaissance

ABSTRACT

Low-field magnetic resonance imaging (MRI) systems have seen a renaissance recently due to improvements in technology (both hardware and software). Originally, the performance of low-field MRI systems was rated lower than their actual clinical usefulness, and they were viewed as low-cost but poorly performing systems. However, various applications similar to high-field MRI systems (1.5 T and 3 T) have gradually become possible, culminating with high-performance low-field MRI systems and their adaptations now being proposed that have unique advantages over high-field MRI systems in various aspects. This review article describes the physical characteristics of low-field MRI systems and presents both their advantages and disadvantages for clinical use (past to present), along with their cutting-edge clinical applications.

Imaging in head and neck cancers: Update for non-radiologist

Head and neck cancer (HNC) is the fifth most frequent cancer worldwide and associated with significant morbidity. Along with clinical examination and endoscopic evaluation, imaging plays an important role in pre- and posttherapeutic evaluation of patients with HNC. Cross-sectional Imaging techniques such as computed tomography (CT), magnetic resonance imaging (MRI) and positron emission tomography / computed tomography (PET/CT) are routinely used in the assessment of these patients. This review provides an overview of the various cross-sectional imaging modalities used in the evaluation of HNC and will give a short summary of the latest imaging technologies regarding head and neck cancer diagnosis.