Current status of magnetic resonance imaging radiomics in hepatocellular carcinoma:A quantitative review with Radiomics Quality Score

BACKGROUND Radiomics is a promising tool that may increase the value of magnetic resonance imaging(MRI)for different tasks related to the management of patients with hepatocellular carcinoma(HCC).However,its implementation in clinical practice is still far,with many issues related to the methodological quality of radiomic studies.AIM To systematically review the current status of MRI radiomic studies concerning HCC using the Radiomics Quality Score(RQS).METHODS A systematic literature search of PubMed,Google Scholar,and Web of Science databases was performed to identify original articles focusing on the use of MRI radiomics for HCC management published between 2017 and 2023.The methodological quality of radiomic studies was assessed using the RQS tool.Spearman’s correlation(ρ)analysis was performed to explore if RQS was correlated with journal metrics and characteristics of the studies.The level of statistical significance was set at P<0.05.RESULTS One hundred and twenty-seven articles were included,of which 43 focused on HCC prognosis,39 on prediction of pathological findings,16 on prediction of the expression of molecular markers outcomes,18 had a diagnostic purpose,and 11 had multiple purposes.The mean RQS was 8±6.22,and the corresponding percentage was 24.15%±15.25%(ranging from 0.0% to 58.33%).RQS was positively correlated with journal impact factor(IF;ρ=0.36,P=2.98×10^(-5)),5-years IF(ρ=0.33,P=1.56×10^(-4)),number of patients included in the study(ρ=0.51,P<9.37×10^(-10))and number of radiomics features extracted in the study(ρ=0.59,P<4.59×10^(-13)),and time of publication(ρ=-0.23,P<0.0072).CONCLUSION Although MRI radiomics in HCC represents a promising tool to develop adequate personalized treatment as a noninvasive approach in HCC patients,our study revealed that studies in this field still lack the quality required to allow its introduction into clinical practice.

A holistic visualization for quality of Chinese materia medica: Structural and metabolic visualization by magnetic resonance imaging

The quality of Chinese materia medica(CMM)is a challenging and focused topic in the modernization of traditional Chinese medicine(TCM).A profound comprehension of the morphology,structure,active constituents,and dynamic changes during the whole process of CMM growth is essential,which needs highly precise contemporary techniques for in-depth elucidation.Magnetic resonance imaging(MRI)is a cutting-edge tool integrating the benefits of both nuclear magnetic resonance(NMR)spectroscopy and imaging technology.With real-time,non-destructive,and in situ detection capabilities,MRI has been previously used for monitoring internal and external structures of plants alongside compounds during physiological processes in vivo.Here,factors involved in the holistic quality evaluation of CMMs were investigated.Given the applications of MRI in various plants,several representative CMMs were used as examples to demonstrate a methodology of quality visualization by MRI,embodying holistically monitoring the real-time macroscopic morphology,mesoscopic structure,and microscopic metabolites nondestructively in situ.Taken together,the review not only presents a pioneering application mode for utilizing MRI for CMM quality visualization but also holds promise for advancing the quality control and evaluation of CMMs.

Influence of random phase modulation on the imaging quality of computational ghost imaging

In this paper, we investigated phase modulation-based computational ghost imaging. According to the results of numerical simulations, we found that the range of the random phase affects the quality of the reconstructed image. Besides,compared with those amplitude modulation-based computational ghost imaging schemes, introducing random phase modulation into the computational ghost imaging scheme could significantly improve the spatial resolution of the reconstructed image, and also extend the field of view.

Recent advancement of chemical imaging in pharmaceutical quality control:From final product testing to industrial utilization

Chemical imaging(CI)possesses a strong ability of pharmaceutical analysis.Its great strength relies on the integration of traditional spectroscopy(one dimension)and imaging technique(two dimensions)to generate three-dimensional data hypercubes.Data pre-processing or processing methods are proposed to analyze vast data matrixes and thereby realizing different research objectives.In this review paper,various pharmaceutical applications of quality control over the past few years are summed up in two groups of final product test and industrial utilization.The scope of"quality control"here includes traditional analytical use,process understanding and manufactural control.Finally,two major challenges about undesirable sample geometry and lengthy acquisition time are discussed for prospective commercial or industrial application.

Prediction of Fresh Pork Quality using Hyperspectral Scattering Imaging(HSI) Technique

In this study, fresh pork tenderness, drip-loss, pH value and color parameters （ CIE, a ＊ , b ＊ and L ＊ values） were simultaneously predicted using hyperspectral scattering imaging （HSI） technique. The hyperspectral scattering images of dO fresh pork samples were collected at the wavelength of 400 -I 100 nm, and the scattering profiles were fitted via Lorontzian distribution （ LD ） function to give three parameters a （ asymptotic value ）, b （peak value ） and c （ full width at b/2）. Stepwise discrimination was performed to determine the optimal wavelengths combinations. The LD parameters combinations （a, b and c） of optimal wavelengths were used to establish multi-linear regression （MLR） models to predict the pork attributes. The models were able to predict pork with high correlation coefficients of 0.92 for drip-loss, 0.94, 0.92 and 0.98 respectively for color parameters （ a ＊ , b＊ and L ＊ ）, and for tenderness and pH value the models gave the correlation coefficients of 0.69 and 0.76, respectively. These results showed that the hyperspectral scattering technique was capable of predicting quality parameters of perk. The study provides an efficient means for rapid and nondestructive determination of pork quality simultaneously.

What benefit can be obtained from magnetic resonance imaging diagnosis with artificial intelligence in prostate cancer compared with clinical assessments?

The present study aimed to explore the potential of artificial intelligence(AI)methodology based on magnetic resonance(MR)images to aid in the management of prostate cancer(PCa).To this end,we reviewed and summarized the studies comparing the diagnostic and predictive performance for PCa between AI and common clinical assessment methods based on MR images and/or clinical characteristics,thereby investigating whether AI methods are generally superior to common clinical assessment methods for the diagnosis and prediction fields of PCa.First,we found that,in the included studies of the present study,AI methods were generally equal to or better than the clinical assessment methods for the risk assessment of PCa,such as risk stratification of prostate lesions and the prediction of therapeutic outcomes or PCa progression.In particular,for the diagnosis of clinically significant PCa,the AI methods achieved a higher summary receiver operator characteristic curve(SROC-AUC)than that of the clinical assessment methods(0.87 vs.0.82).For the prediction of adverse pathology,the AI methods also achieved a higher SROC-AUC than that of the clinical assessment methods(0.86 vs.0.75).Second,as revealed by the radiomics quality score(RQS),the studies included in the present study presented a relatively high total average RQS of 15.2(11.0–20.0).Further,the scores of the individual RQS elements implied that the AI models in these studies were constructed with relatively perfect and standard radiomics processes,but the exact generalizability and clinical practicality of the AI models should be further validated using higher levels of evidence,such as prospective studies and open-testing datasets.

Image quality of the proton imaging from computer-simulated data

In this paper,the proton image quality of three designed phantoms is studied by the Monte Carlo simulation tool FLUKA,and is compared with that of the photon,and electron imaging.The passages of parallel beams were simulated by the phantoms for protons,electrons and X-rays,and the fluence and energy spectra of exiting particles are recorded.The proton energy loss imaging has a high quality under low transmission proton fluence,and its quality is superior to that of electron and photon fluence.The results demonstrate that proton energy loss imaging is especially suitable for the samples of hundreds of nano-thicknesses.

Quality Improvement in Cardiovascular Imaging

Implementation of quality improvement(QI)activities is growing in health care settings across the world.Within cardiovascular imaging,areas for improvement include consistency of result reporting,greater patient safety through reductions in radiation exposure,and greater effi ciency of care delivery by elimination of wasteful practices and elimination of low-value or rarely appropriate testing.QI in health care is often driven through one of several endorsed frameworks,such as Lean,Six Sigma,and the Model for Improvement.Multiple examples of how to teach QI to medical trainees and physicians have been published.In this narrative review,we explore the growth and impact of QI in cardiovascular imaging,providing specifi c examples of successful projects,barriers to conducting QI,and rewards of persistent effort toward improving care.

Multispectral Imaging for Authenticity Identification and Quality Evaluation of <i>Flos carthami</i>

The identification and quality evaluation of Flos carthami were studied using tunable liquid spectral imaging instrument, to discuss the application range and advantages of spectral imaging technology in Chinese medicine identification and quality control field. The Flos carthami was indentified by extracting the normalized characteristic spectral curves of Flos carthami, Crocus sativus and Dendranthema morifolium, which were standard samples supplied by National Institute for Drug Control. The qualities of Flos carthamies collecting from different pharmacies were evaluated by extracting their normalized characteristic spectral curves. The imaging spectrum testing system was designed independently. The spectral resolution was 5 nm, and the spectral range was from 400 nm to 680 nm. The results showed that the normalized characteristic spectral curve of Flos carthami was significantly different from those of Crocus sativus’ and Dendranthema morifolium’s, and the fluorescence intensity of Flos carthami from different commercial sources were different. Spectral imaging technology could be used to identify and evaluate Flos carthami, and operation method was rapid, convenient and non-destructive.

Effect of Signal Filtering on Image Quality of Projection-Based Magnetic Particle Imaging

Purpose: Magnetic particle imaging (MPI) allows for imaging of the spatial distribution of magnetic nanoparticles (MNPs) in positive contrast, with high sensitivity, high spatial resolution, and high imaging speed. It is necessary to increase the signal-to-noise ratio to enhance the reliability of MPI. The purpose of this study was to investigate the effect of signal filtering on the image quality and quantitativity in projection-based MPI using phantoms. Materials and Methods: We fabricated two kinds of phantom (cylindrical tube phantom with a diameter of 6 mm and A-shaped phantom) and evaluated the effect of signal filtering in terms of root-mean-square (RMS) granularity and the correlation coefficient between iron concentrations of MNPs and average MPI values for four filter modes (THRU, BPF, BEF, and LPF). In the THRU mode, the signal input was output without passing through the filter. In the BPF mode, only the third-harmonic signal was passed using a band-pass filter (central frequency: 1200 Hz, band width: 1/3 octave). In the BEF mode, the first-harmonic signal was eliminated using a band-elimination filter (central frequency: 400 Hz, band width: 1/3 octave). In the LPF mode, only the signal with a frequency less than the third-harmonic frequency was passed using a low-pass filter (cut-off frequency: 1200 Hz, -24 ± 2 dB/octave). The RMS granularity was obtained by calculating standard deviations of the pixel values in the MPI image without MNPs, whereas average MPI values were obtained by drawing a circular region of interest with a diameter of 6 mm on the MPI image of the cylindrical tube phantom. Results: When using the filtered back-projection (FBP) method with a ramp filter for image reconstruction, the RMS granularity and correlation coefficient decreased in the order of THRU, BPF, BEF, and LPF. In the BPF mode, however, some artifacts were observed. When using the maximum likelihood-expectation maximization (ML-EM) algorithm with an iteration number of 15, the correlation coefficient decreased in the order of THRU, BPF, BEF, and LPF, whereas the RMS granularity did not largely depend on the filter mode and was significantly (p Conclusion: The BEF mode is adequate for the FBP method in projection-based MPI, whereas THRU is a best option in use of the ML-EM algorithm.

Image quality evaluation of linear plastic scintillating fiber array detector for X-ray imaging

It is important to assess image quality, in order to ensure that the imaging system is performing optimally and also identify the weak points in an imaging system. Three parameters mostly leading to image degradation are contrast, spatial resolution and noise. There is always a trade-off between spatial resolution and signal to noise ratio, but in scintillating fiber array detectors spatial resolution is not as important as signal to noise ratio, so we paid more attention to contrast and SNR of the system. By using GEANT4 Monte Carlo detector simulation toolkit, some effec- tive parameters of the linear plastic scintillating fiber (PSF) array as an imaging detector were investigated. Finally we show that it is possible to use this kind of detector to take CT and DR (Digital Radiography) image under certain conditions.

Sacrum magnetic resonance imaging for low back and tail bone pain:A quality initiative to evaluate and improve imaging utility

As quality and cost effectiveness become essential in clinical practice,an evidencebased evaluation of the utility of imaging orders becomes an important consideration for radiology’s value in patient care.We report an institutional quality improvement project including a retrospective review of utility of sacrum magnetic resonance(MR)imaging for low back pain at our institution over a four-year period and follow-up results after physician education intervention.Sacral MR imaging for low back pain and tailbone pain were only positive for major findings in 2/98(2%)cases,and no major changes in patient management related to imaging findings occurred over this period,resulting in almost$500000 cost without significant patient benefit.We distributed these results to the Family Medicine department and clinics that frequently placed this order.An approximately 83%drop in ordering rate occurred over the ensuing 3 mo follow-up period.Sacrum MR imaging for low back pain and tail bone pain has not been a cost-effective diagnostic tool at our institution.Physician education was a useful tool in reducing overutilization of this study,with a remarkable drop in such studies after sharing these findings with primary care physicians at the institution.In conclusion,sacrum MR imaging rarely elucidates the cause of low back/tail pain diagnosed in a primary care setting and is even less likely to result in major changes in management.The practice can be adopted in other institutions for the benefit of their patients and improve cost efficiency.

Image quality assessment based on perceptual grouping

To further explore the human visual system（ HVS）,the perceptual grouping（ PG）, which has been proven to play an important role in the HVS, is adopted to design an effective image quality assessment（ IQA） model. Compared with the existing fixed-window-based models, the proposed one is an adaptive window-like model that introduces the perceptual grouping strategy into the IQA model. It works as follows： first,it preprocesses the images by clustering similar pixels into a group to the greatest extent; then the structural similarity is used to compute the similarity of the superpixels between reference and distorted images; finally, it integrates all the similarity of superpixels of an image to yield a quality score. Experimental results on three databases（ LIVE, IVC and MICT） showthat the proposed method yields good performance in terms of correlation with human judgments of visual quality.

Image Quality Assessment through FSIM, SSIM, MSE and PSNR—A Comparative Study

Quality is a very important parameter for all objects and their functionalities. In image-based object recognition, image quality is a prime criterion. For authentic image quality evaluation, ground truth is required. But in practice, it is very difficult to find the ground truth. Usually, image quality is being assessed by full reference metrics, like MSE (Mean Square Error) and PSNR (Peak Signal to Noise Ratio). In contrast to MSE and PSNR, recently, two more full reference metrics SSIM (Structured Similarity Indexing Method) and FSIM (Feature Similarity Indexing Method) are developed with a view to compare the structural and feature similarity measures between restored and original objects on the basis of perception. This paper is mainly stressed on comparing different image quality metrics to give a comprehensive view. Experimentation with these metrics using benchmark images is performed through denoising for different noise concentrations. All metrics have given consistent results. However, from representation perspective, SSIM and FSIM are normalized, but MSE and PSNR are not;and from semantic perspective, MSE and PSNR are giving only absolute error;on the other hand, SSIM and PSNR are giving perception and saliency-based error. So, SSIM and FSIM can be treated more understandable than the MSE and PSNR.

CT dose and image quality in the last three scanner generations

AIM: To compare the computed tomography(CT) dose and image quality with the filtered back projection against the iterative reconstruction and CT with a minimal electronic noise detector. METHODS: A lung phantom(Chest Phantom N1 by Kyoto Kagaku) was scanned with 3 different CT scanners: the Somatom Sensation, the Definition Flash and the Definition Edge(all from Siemens, Erlangen, Germany). The scan parameters were identical to the Siemens presetting for THORAX ROUTINE(scan length 35 cm and FOV 33 cm). Nine different exposition levels were examined(reference mAs/peek voltage): 100/120, 100/100, 100/80, 50/120, 50/100, 50/80, 25/120, 25/100 and 25 mAs/80 kVp. Images from the SOMATOM Sensation were reconstructed using classic filtered back projection. Iterative reconstruction(SAFIRE, level 3) was performed for the two other scanners. A Stellar detector was used with the Somatom Definition Edge. The CT doses were represented by the dose length products(DLPs)(mGycm) provided by the scanners. Signal, contrast, noise and subjective image quality were recorded by two different radiologists with 10 and 3 years of experience in chest CT radiology. To determine the average dose reduction between two scanners, the integral of the dose difference was calculated from the lowest to the highest noise level. RESULTS: When using iterative reconstruction(IR) instead of filtered back projection(FBP), the average dose reduction was 30%, 52% and 80% for bone, soft tissue and air, respectively, for the same image quality(P 【 0.0001). The recently introduced Stellar detector(Sd) lowered the radiation dose by an additional 27%, 54% and 70% for bone, soft tissue and air, respectively(P 【 0.0001). The benefit of dose reduction was larger at lower dose levels. With the same radiation dose, an average of 34%(22%-37%) and 25%(13%-46%) more contrast to noise was achieved by changing from FBP to IR and from IR to Sd, respectively. For the same contrast to noise level, an average of 59%(46%-71%) and 51%(38%-68%) dose reduction was produced for IR and Sd, respectively. For the same subjective image quality, the dose could be reduced by 25%(2%-42%) and 44%(33%-54%) using IR and Sd, respectively. CONCLUSION: This study showed an average dose reduction between 27% and 70% for the new Stellar detector, which is equivalent to using IR instead of FBP.

Computed tomography scan imaging in diagnosing acute uncomplicated pancreatitis: Usefulness vs cost

BACKGROUND Literature has suggested that imaging is over-utilized in the diagnosis of pancreatitis. If the diagnosis of acute pancreatitis(AP) is established with abdominal pain and increased serum amylase or lipase activity without systemic signs of severe disease, computed tomography(CT) imaging may not be necessary. We hypothesize that among patients with uncomplicated acute pancreatitis(AUP), there is a significant number of unwarranted CT imaging studies. This imposes increased expenditure and cost in our healthcare system and does not improve hospital stay or management of AUP.AIM To assess the overutilization and associated cost of CT imaging among patients meeting diagnostic criteria for AUP.METHODS In this Institutional Review Board-approved retrospective, single-center study,we identified all adult patients admitted with AP from January 1, 2012 through October 1, 2017. Patients were identified via International Classification of Diseases(ICD-9) code for AP(577.0) and ICD-10 codes for different etiological AP(K85.9 unspecified, K85.0 idiopathic, K85.1 biliary, K85.2 alcohol-induced, K85.3 drug-induced, and K85.8 other). Diagnosis was confirmed by chart review using established non-imaging diagnostic criteria(presence of typical abdominal pain and elevated lipase or amylase greater than 3 times upper limit of normal).Ranson criteria and BISAP scores on presentation were calculated and patients that met scores less than or equal to 2 for both were included to suggest AUP.The utilization and cost of imaging in these patients were recorded.RESULTS Between January 2012 and October 2017, 1305 patients presented to the emergency department with AP, and 405 patients(31%) met our inclusion criteria for AUP(201 males, 204 females; mean age 49 years, range 18-98). Of those, 210 patients(51.85%) underwent CT imaging. One patient(0.47%) had evidence of pancreatic necrosis, one patient had cyst formation(0.47%), and the remaining208 patients(99.05%) had either normal CT scan imaging or findings consistent with mild AP without necrosis. The average cost of CT scan imaging was $4510 with a total cost of $947056. Median length of hospitalization stay was 3 d among both groups. Combining Ranson's Criteria and BISAP score identified AUP in our patient population with an accuracy of 99.5%.CONCLUSION CT imaging is unnecessary when AUP is diagnosed clinically and biochemically.Reducing overuse of diagnostic CT scans will decrease healthcare expenditure and radiation exposure to patients.

Performance Validation and Analysis for Multi-Method Fusion Based Image Quality Metrics in A New Image Database

Considering that there is no single full reference image quality assessment method that could give the best performance in all situations, some multi-method fusion metrics were proposed. Machine learning techniques are often involved in such multi-method fusion metrics so that its output would be more consistent with human visual perceptions. On the other hand, the robustness and generalization ability of these multi-method fusion metrics are questioned because of the scarce of images with mean opinion scores. In order to comprehensively validate whether or not the generalization ability of such multi-method fusion IQA metrics are satisfying, we construct a new image database which contains up to 60 reference images. The newly built image database is then used to test the generalization ability of different multi-method fusion IQA metrics. Cross database validation experiment indicates that in our new image database, the performances of all the multi-method fusion IQA metrics have no statistical significant different with some single-method IQA metrics such as FSIM and MAD. In the end, a thorough analysis is given to explain why the performance of multi-method fusion IQA framework drop significantly in cross database validation.

Multi-Modality Medical Image Fusion Based on Wavelet Analysis and Quality Evaluation

Multi-modality medical image fusion has more and more important applications in medical image analysis and understanding. In this paper, we develop and apply a multi-resolution method based on wavelet pyramid to fuse medical images from different modalities such as PET-MRI and CT-MRI. In particular, we evaluate the different fusion results when applying different selection rules and obtain optimum combination of fusion parameters.

Image Tampering Detection Using No-Reference Image Quality Metrics

In this paper,a new approach is proposed to determine whether the content of an image is authentic or modified with a focus on detecting complex image tampering.Detecting image tampering without any prior information of the original image is a challenging problem since unknown diverse manipulations may have different characteristics and so do various formats of images.Our principle is that image processing,no matter how complex,may affect image quality,so image quality metrics can be used to distinguish tampered images.In particular,based on the alteration of image quality in modified blocks,the proposed method can locate the tampered areas.Referring to four types of effective no-reference image quality metrics,we obtain 13 features to present an image.The experimental results show that the proposed method is very promising on detecting image tampering and locating the locally tampered areas especially in realistic scenarios.

Effect of erythromycin on image quality and transit time of capsule endoscopy: A two-center study

AIM: To compare the effect of oral erythromycin vs no preparation with prokinetics on the transit time and the image quality of capsule endoscopy (CE) in evaluating small bowel (SB) pathology. METHODS: We conducted a retrospective, blinded (to the type of preparation) review of 100 CE studies, 50 with no preparation with prokinetics from one medical center (Group A) and 50 from another center with administration of a single dose of 200 mg oral erythromycin 1 h prior to CE (Group B). Gastric, SB and total transit times were calculated, the presence of bile in the duodenum was scored, as was cleanliness within the proximal, middle and distal intestine. RESULTS: The erythromycin group had a slightly shorter gastric transit time (21 min vs 28 min, with no statistical significance). SB transit time was similar for both groups (all P > 0.05). Total transit time was almost identical in both groups. The rate of incomplete examination was 16% for Group A and 10% for Group B (P = 0.37). Bile and cleanliness scores in different parts of the intestine were similar for the two groups (P > 0.05). CONCLUSION: Preparation for capsule endoscopy with erythromycin does not affect SB or total transit time. It tends to reduce gastric transit time, but it does not increase the cecum-reaching rate. Erythromycin does not adversely affect image quality. We consider the routine use of oral erythromycin preparation as being unjustified, although it might be considered in patients with known prolonged gastric emptying time.