Quality assurance and temporal stability of a 1.5 T MRI scanner for MR-guided Photon and Particle Therapy

PURPOSE

To describe performance measurements, adaptations and time stability over 20 months of a diagnostic MR scanner for integration into MR-guided photon and particle radiotherapy.

MATERIAL AND METHODS

For realization of MR-guided photon and particle therapy (MRgRT/MRgPT), a 1.5 T MR scanner was installed at the Heidelberg Ion Beam Therapy Center. To integrate MRI into the treatment process, a flat tabletop and dedicated coil holders for flex coils were used, which prevent deformation of the patient external contour and allow for the use of immobilization tools for reproducible positioning. The signal-to-noise ratio (SNR) was compared for the diagnostic and therapy-specific setup using the flat couch top and flexible coils for the a) head & neck and b) abdominal region as well as for different bandwidths and clinical pulse sequences. Additionally, a quality assurance (QA) protocol with monthly measurements of the ACR phantom and measurement of geometric distortions for a large field-of-view (FOV) was implemented to assess the imaging quality parameters of the device over the course of 20 months.

RESULTS

The SNR measurements showed a decreased SNR for the RT-specific as compared to the diagnostic setup of (a) 26% to 34% and (b) 11% to 33%. No significant bandwidth dependency for this ratio was found. The longitudinal assessment of the image quality parameters with the ACR and distortion phantom confirmed the long-term stability of the MRI device.

CONCLUSION

A diagnostic MRI was commissioned for use in MR-guided particle therapy. Using a radiotherapy specific setup, a high geometric accuracy and signal homogeneity was obtained after some adaptions and the measured parameters were shown to be stable over a period of 20 months.

Microrobotic Swarms for Intracellular Measurement with Enhanced Signal-to-Noise Ratio

In cell biology, fluorescent dyes are routinely used for biochemical measurements. The traditional global dye treatment method suffers from low signal-to-noise ratios (SNR), especially when used for detecting a low concentration of ions, and increasing the concentration of fluorescent dyes causes more severe cytotoxicity. Here, we report a robotic technique that controls how a low amount of fluorescent-dye-coated magnetic nanoparticles accurately forms a swarm and increases the fluorescent dye concentration in a local region inside a cell for intracellular measurement. Different from existing magnetic micromanipulation systems that generate large swarms (several microns and above) or that cannot move the generated swarm to an arbitrary position, our system is capable of generating a small swarm (e.g., 1 μm) and accurately positioning the swarm inside a single cell (position control accuracy: 0.76 μm). In experiments, the generated swarm inside the cell showed an SNR 10 times higher than the traditional global dye treatment method. The high-SNR robotic swarm enabled intracellular measurements that had not been possible to achieve with traditional global dye treatment. The robotic swarm technique revealed an apparent pH gradient in a migrating cell and was used to measure the intracellular apparent pH in a single oocyte of living C. elegans. With the position control capability, the swarm was also applied to measure calcium changes at the perinuclear region of a cell before and after mechanical stimulation. The results showed a significant calcium increase after mechanical stimulation, and the calcium increase was regulated by the mechanically sensitive ion channel, PIEZO1.

Analysis and Evaluation of a Deep Learning Reconstruction Approach with Denoising for Orthopedic MRI

PURPOSE

To evaluate two settings (noise reduction of 50% or 75%) of a deep learning (DL) reconstruction model relative to each other and to conventional MR image reconstructions on clinical orthopedic MRI datasets.

MATERIALS AND METHODS

This retrospective study included 54 patients who underwent two-dimensional fast spin-echo MRI for hip (n = 22; mean age, 44 years ± 13 [standard deviation]; nine men) or shoulder (n = 32; mean age, 56 years ± 17; 17 men) conditions between March 2019 and June 2020. MR images were reconstructed with conventional methods and the vendor-provided and commercially available DL model applied with 50% and 75% noise reduction settings (DL 50 and DL 75, respectively). Quantitative analytics, including relative anatomic edge sharpness, relative signal-to-noise ratio (rSNR), and relative contrast-to-noise ratio (rCNR) were computed for each dataset. In addition, the image sets were randomized, blinded, and presented to three board-certified musculoskeletal radiologists for ranking based on overall image quality and diagnostic confidence. Statistical analysis was performed with a nonparametric hypothesis comparing derived quantitative metrics from each reconstruction approach. In addition, inter- and intrarater agreement analysis was performed on the radiologists' rankings.

RESULTS

Both denoising settings of the DL reconstruction showed improved edge sharpness, rSNR, and rCNR relative to the conventional reconstructions. The reader rankings demonstrated strong agreement, with both DL reconstructions outperforming the conventional approach (Gwet agreement coefficient = 0.98). However, there was lower agreement between the readers on which DL reconstruction denoising setting produced higher-quality images (Gwet agreement coefficient = 0.31 for DL 50 and 0.35 for DL 75).

CONCLUSION

The vendor-provided DL MRI reconstruction showed higher edge sharpness, rSNR, and rCNR in comparison with conventional methods; however, optimal levels of denoising may need to be further assessed.Keywords: MRI Reconstruction Method, Deep Learning, Image Analysis, Signal-to-Noise Ratio, MR-Imaging, Neural Networks, Hip, Shoulder, Physics, Observer Performance, Technology Assessment Supplemental material is available for this article. © RSNA, 2021.

Tinnitus: An Abstract View Emphasizing Signal, Noise, and Their Discrimination

Most research on the hearing process and its pathologies is biological in nature. However, engineers work on similar problems, in particular signal detection, efficient information processing and noise suppression. On a more abstract level, hearing can also be treated as a discrimination task, something which is well known to statistics. The goal of this note is to explore the implications of these more abstract ideas: It merges that the signal-to-noise ratio and the construction of a suitable discrimination function are crucial elements of the hearing process. Hopefully such a theoretical point of view may facilitate a deeper understanding of the mechanisms involved. Perception fallacies such as phantom sounds or optical illusions are not just interesting in their own right; they open up a direct path to the deeper workings of perceptual processes. In order to be accessible to researchers in the field, this note is rather non-technical.

Effect of wireless remote microphone application on speech discrimination in noise in children with cochlear implants

OBJECTIVES

Although cochlear implantation has significantly contributed to the speech perception of cochlear implant (CI) users, these individuals still have significant difficulty in understanding speech, especially in noisy environments and keeping track of the target speaker in the presence of speech sounds of others. This study was aimed to evaluate the effect of wireless Remote Microphones (RM) on speech discrimination scores in noise in child CI users.

MATERIALS AND METHODS

Twenty children with unilateral cochlear implantation were enrolled in this study with mean ± SD age of 5.8 ± 0.83 years who have undergone CI for at least one year. Speech discrimination scores in noise were assessed using the Words-in-Noise (WIN) test at a constant signal-to-noise ratio (SNR) of 0 dB, in the presence and absence of a wireless RM. Three loudspeakers were placed at a distance of 1 m in front of the child to present the speech and babble noise. The wireless microphone was placed on a base with a height equivalent to the height of the middle speech loudspeaker at a distance of 30 cm from it.

FINDINGS

The mean speech discrimination score in noise in the absence of wireless RM in all children was obtained 34% (6.8 words out of 20 words), with minimum and maximum of 15% and 50% words. Findings revealed the mean speech discrimination score in noise in the presence of wireless RMs is equivalent to 65% (13 words out of 20 words), with the minimum and maximum scores of 35% and 95%, respectively. The result showed that speech discrimination scores in noise improved in the presence of wireless RM.

CONCLUSION

The significant improvement was observed in speech discrimination in noise in all cochlear implanted children when the wireless RM was used, as compared to the absence of a wireless RM, which suggests the usefulness of this hearing aid accessory in CI users.

A New Method to Segment the Multiple Sclerosis Lesions on Brain Magnetic Resonance Images

Automatic segmentation of multiple sclerosis (MS) lesions in brain magnetic resonance imaging (MRI) has been widely investigated in the recent years with the goal of helping MS diagnosis and patient follow-up. In this research work, Gaussian mixture model (GMM) has been used to segment the MS lesions in MRIs, including T1-weighted (T1-w), T2-w, and T2-fluid attenuation inversion recovery. Usually, GMM is optimized by using expectation-maximization (EM) algorithm. The drawbacks of this optimization method are, it does not converge to optimal maximum or minimum and furthermore, there are some voxels, which do not fit the GMM model and have to be rejected. So, GMM is time-consuming and not too much efficient. To overcome these limitations, in this research study, at the first step, GMM was applied to segment only T1-w images by using 100 various starting points when the maximum number of iterations was considered to be 50. Then segmentation results were used to calculate the parameters of the other two images. Furthermore, FAST-trimmed likelihood estimator algorithm was applied to determine which voxels should be rejected. The output result of the segmentation was classified in three classes; White and Gray matters, cerebrospinal fluid, and some rejected voxels which prone to be MS. In the next phase, MS lesions were detected by using some heuristic rules. This new method was applied on the brain MRIs of 25 patients from two hospitals. The automatic segmentation outputs were scored by two specialists and the results show that our method has the capability to segment the MS lesions with dice similarity coefficient score of 0.82. The results showed a better performance for the proposed approach, in comparison to those of previous works with less time-consuming.

Computed Tomography Images De-noising using a Novel Two Stage Adaptive Algorithm

In this paper, an optimal algorithm is presented for de-noising of medical images. The presented algorithm is based on improved version of local pixels grouping and principal component analysis. In local pixels grouping algorithm, blocks matching based on L (2) norm method is utilized, which leads to matching performance improvement. To evaluate the performance of our proposed algorithm, peak signal to noise ratio (PSNR) and structural similarity (SSIM) evaluation criteria have been used, which are respectively according to the signal to noise ratio in the image and structural similarity of two images. The proposed algorithm has two de-noising and cleanup stages. The cleanup stage is carried out comparatively; meaning that it is alternately repeated until the two conditions based on PSNR and SSIM are established. Implementation results show that the presented algorithm has a significant superiority in de-noising. Furthermore, the quantities of SSIM and PSNR values are higher in comparison to other methods.