Application of quantitative imaging in oncologic management

Medical imaging, such as computed tomography （CT）, magnetic resonance imaging （MRI） and positron emissiontomography （PET）, plays a vital role for the decision-making in oncologic management. In clinical practice, imaging-derivedtumor metrics are routinely applied in oncologic management as an imaging biomarker. For example, the ResponseEvaluation Criteria in Solid Tumors （RECIST） are commonly used for tumor treatment response evaluation based on thedynamic changes in tumor size. However, the current cross-sectional images are interpreted qualitatively for lesioncharacterization, treatment response evaluation and prognostic prediction by highly trained radiologists, which hasincreasingly apparent limitations. Therefore, there is a demanding shift toward more quantitative imaging interpretation.

Deep learning assisted variational Hilbert quantitative phase imaging

We propose a high-accuracy artifacts-free single-frame digital holographic phase demodulation scheme for relatively lowcarrier frequency holograms-deep learning assisted variational Hilbert quantitative phase imaging(DL-VHQPI).The method,incorporating a conventional deep neural network into a complete physical model utilizing the idea of residual compensation,reliably and robustly recovers the quantitative phase information of the test objects.It can significantly alleviate spectrum-overlapping-caused phase artifacts under the slightly off-axis digital holographic system.Compared to the conventional end-to-end networks(without a physical model),the proposed method can reduce the dataset size dramatically while maintaining the imaging quality and model generalization.The DL-VHQPI is quantitatively studied by numerical simulation.The live-cell experiment is designed to demonstrate the method's practicality in biological research.The proposed idea of the deep learning-assisted physical model might be extended to diverse computational imaging techniques.

Left ventricular regional and global diastolic function assessed using Quantitative Tissue velocity Imaging in patients with hypertrophic cardiomyopathy

Objectives The study was performed to assess the left ventricular (LV) regional and global diastolic function、left ventricular wall motion features in patients with Hypertrophic cardiomyopathy by Quantitative Tissue Velocity Imaging (QTVI). Methods 42 patients with hypertrophic cardiomyopathy and 36 age-matched normal subjects underwent QTVI study. Off-line LV regional muscular tissue velocity Imaging along LV apical long-axis view were obtained. Regional diastolic function was assessed in using peak tissue velocities of LV regional muscular tissue during early diastole (Ve)and LA contraction (Va), Ve/Va ratio, derived from Tissue Velocity Imaging. Global diastolic function was reflected by isovolumic relaxation time(IRT) and mitral valve peak flow velocity ( E/A ) calculated with pulsed wave doppler. The end-diastolic interventricular septal thickness (ⅣSt) was measured by conventional 2 - dimension echocardiography. Results ① Ve、 Va、 Ve/Va in the segments of hypertrophic interventricular septum (IVS) reduced wlhile E/A ratio significantly reduced and IRT markedly prolonged in HCM patients than in normal subjects。 ② Ve、 Ve/Va were significant reduced in the segments of hypertrophic interventricular septum compared with other LV segments in HCM patients . ③ There was a correlation between Ve/Va and E/A in HCM patients with abnormal E/A ratio (r = 0. 70). ④ There was a negative correlation between Ve/Va and ⅣSt in non -obstruction HCM patients (B group , r = -0.61 ) Conclusions QTVI offers a newer method in clinical practice which has a higher sensibility and accuracy in evaluating the LV regional and global diastolic function in HCM patients .

Future of prostate imaging:Artificial intelligence in assessing prostatic magnetic resonance imaging

Prostate cancer(Pca;adenocarcinoma)is one of the most common cancers in adult males and one of the leading causes of death in both men and women.The diagnosis of Pca requires substantial experience,and even then the lesions can be difficult to detect.Moreover,although the diagnostic approach for this disease has improved significantly with the advent of multiparametric magnetic resonance,that technology has certain unresolved limitations.In recent years artificial intelligence(AI)has been introduced to the field of radiology,providing new software solutions for prostate diagnostics.Precise mapping of the prostate has become possible through AI and this has greatly improved the accuracy of biopsy.AI has also allowed for certain suspicious lesions to be attributed to a given group according to the Prostate Imaging-Reporting&Data System classification.Finally,AI has facilitated the combination of data obtained from clinical,laboratory(prostate-specific antigen),imaging(magnetic resonance),and biopsy examinations,and in this way new regularities can be found which at the moment remain hidden.Further evolution of AI in this field is inevitable and it is almost certain to significantly expand the efficacy,accuracy and efficiency of diagnosis and treatment of Pca.

Left Ventricular Regional Systolic Function in Patient with Hypertrophic Cardiomyopathy by Quantitative Tissue Velocity Imaging

The left ventricular regional systolic functions in patients with hypertrophic cardiomyopathy （HCM） were assessed by using quantitative tissue velocity imaging （QTVI）. Left ventricular （LV） regional myocardial velocity along long- and short-axis in 31 HCM patients and 20 healthy subjects were analyzed by QTVI, and the regional myocardial systolic peak velocities （MVS） were measured. Mean MVS at each level including mitral annular, basal, middle and apical segments were calculated. The ratio of MVS along long-axis to that along short-axis （Ri） at basal and middle segments of the LV posterior wall and ventricular septum were calculated. The results showed that mean MVS was slower at each level including mitral annular, basal, middle and apical segments in the HCM patients than that in the healthy subjects （P〈0.01）. There were no significant differences in mean MVS between obstructive and non-obstructive groups in HCM patients. MVS of all regional myocardial segments along long-axis in the HCM patients were significantly slower than that in the healthy subjects （P〈0.05）, but there was no significant difference in MVS of all regional myocardial segments along long-axis between hypertrophied and non-hypertrophied group in the HCM patients. Ri was significantly lower in the HCM patients than that in the healthy subjects. The LV regional myocardial contractility along long-axis was impaired not only in the hypertrophied wall but also in the non-hypertrophied one in patients with HCM, suggesting that QTVI can assess accurately LV regional systolic function in patient with HCM and provides a novel means for an early diagnosis before and independent of hypertrophy.

High-resolution bone microstructure imaging based on ultrasonic frequency-domain full-waveform inversion

The main challenge in bone ultrasound imaging is the large acoustic impedance contrast and sound velocity differences between the bone and surrounding soft tissue. It is difficult for conventional pulse-echo modalities to give accurate ultrasound images for irregular bone boundaries and microstructures using uniform sound velocity assumption rather than getting a prior knowledge of sound speed. To overcome these limitations, this paper proposed a frequency-domain fullwaveform inversion(FDFWI) algorithm for bone quantitative imaging utilizing ultrasonic computed tomography(USCT).The forward model was calculated in the frequency domain by solving the full-wave equation. The inverse problem was solved iteratively from low to high discrete frequency components via minimizing a cost function between the modeled and measured data. A quasi-Newton method called the limited-memory Broyden–Fletcher–Goldfarb–Shanno algorithm(L-BFGS) was utilized in the optimization process. Then, bone images were obtained based on the estimation of the velocity and density. The performance of the proposed method was verified by numerical examples, from tubular bone phantom to single distal fibula model, and finally with a distal tibia-fibula pair model. Compared with the high-resolution peripheral quantitative computed tomography(HR-p QCT), the proposed FDFWI can also clearly and accurately presented the wavelength scaled pores and trabeculae in bone images. The results proved that the FDFWI is capable of reconstructing high-resolution ultrasound bone images with sub-millimeter resolution. The parametric bone images may have the potential for the diagnosis of bone disease.

Imaging in multiple myeloma: Computed tomography or magnetic resonance imaging?

Multiple myeloma(MM)is the second most common type of hematological disease with its incidence rising in the elderly.In MM,the extent of the bone disease increases both morbidity and mortality.The detection of lytic bone lesions on imaging,especially computerized tomography(CT)and magnetic resonance imaging(MRI)is crucial to separate asymptomatic from symptomatic MM patients even when no clinical symptoms are present.Although radiology is essential in the staging and management of patients with MM there is still high variability in the choice between MRI and CT.In addition,there is still suboptimal agreement among readers.The potential of medical imaging in MM is largely under-evaluated:artificial intelligence,radiomics and new quantitative methods to report CT and MRI will improve imaging usage.

Investigation on Positive Correlation of Increased Brain Iron Deposition with Cognitive Impairment in Alzheimer Disease by Using Quantitative MR R2' Mapping

Brain iron deposition has been proposed to play an important role in the pathophysiology of Alzheimer disease（AD）.The aim of this study was to investigate the correlation of brain iron accumulation with the severity of cognitive impairment in patients with AD by using quantitative MR relaxation rate R2＇ measurements.Fifteen patients with AD,15 age-and sex-matched healthy controls,and 30 healthy volunteers underwent 1.5T MR multi-echo T2 mapping and T2＊ mapping for the measurement of transverse relaxation rate R2＇（R2＇=R2＊-R2）.We statistically analyzed the R2＇ and iron concentrations of bilateral hippocampus（HP）,parietal cortex（PC）,frontal white matter（FWM）,putamen（PU）,caudate nucleus（CN）,thalamus（TH）,red nucleus（RN）,substantia nigra（SN）,and dentate nucleus（DN） of the cerebellum for the correlation with the severity of dementia.Two-tailed t-test,Student-Newman-Keuls test（ANOVA） and linear correlation test were used for statistical analysis.In 30 healthy volunteers,the R2＇ values of bilateral SN,RN,PU,CN,globus pallidus（GP）,TH,and FWM were measured.The correlation with the postmortem iron concentration in normal adults was analyzed in order to establish a formula on the relationship between regional R2＇ and brain iron concentration.The iron concentration of regions of interest（ROI） in AD patients and controls was calculated by this formula and its correlation with the severity of AD was analyzed.Regional R2＇ was positively correlated with regional brain iron concentration in normal adults（r=0.977,P0.01）.Iron concentrations in bilateral HP,PC,PU,CN,and DN of patients with AD were significantly higher than those of the controls（P0.05）;Moreover,the brain iron concentrations,especially in parietal cortex and hippocampus at the early stage of AD,were positively correlated with the severity of patients＇ cognitive impairment（P0.05）.The higher the R2＇ and iron concentrations were,the more severe the cognitive impairment was.Regional R2＇ and iron concentration in parietal cortex and hippocampus were positively correlated with the severity of AD patients＇ cognitive impairment,indicating that it may be used as a biomarker to evaluate the progression of AD.

Improved image resolution on thoracic carcinomas by quantitative 18F-FDG coincidence SPECT/CT in comparison to 18F-FDG PET/CT

Currently,18F-FDG coincidence SPECT(Co-SPECT)/CT scan still serves as an important tool for diagnosis,staging,and evaluation of cancer treatment in developing countries.We implemented full physical corrections(FPC) to Co-SPECT(quantitative Co-SPECT) to improve the image resolution and contrast along with the capability for image quantitation.FPC included attenuation,scatter,resolution recovery,and noise reduction.A standard NEMA phantom filled with 10:1 F-18 activity concentration ratio in spheres and background was utilized to evaluate image performance.Subsequently,15 patients with histologically confirmed thoracic carcinomas were included to undergo a 18 F-FDG Co-SPECT/CT scan followed by a 18 F-FDG PET/CT scan.Functional parameters as SUVmax,SUVmean,SULpeak,and MTV from both quantitative Co-SPECT and PET were analyzed.Image resolution of Co-SPECT for NEMA phantom was improved to reveal the smallest sphere from a diameter of 28 mm to 22 mm(17 mm for PET).The image contrast was enhanced from 1.7 to 6.32(6.69 for PET) with slightly degraded uniformity in background(3.1% vs.6.7%)(5.6% for PET).Patients’ SUVmax,SUVmean,SULpeak,and MTV measured from quantitative Co-SPECT were overall highly correlated with those from PET(r=0.82-0.88).Adjustment of the threshold of SUVmax and SUV to determine SUVmean and MTV did not further change the correlations with PET(r=0.81-0.88).Adding full physical corrections to Co-SPECT images can significantly improve image resolution and contrast to reveal smaller tumor lesions along with the capability to quantify functional parameters like PET/CT.

Pancreatic imaging:Current status of clinical practices and small animal studies

Different causative factors acting on the pancreas can result in diseases such as pancreatitis, diabetes and pancreatic tumors. The high incidence and mortality of pancreatic diseases have placed diagnostic imaging in a crucial position in daily clinical practice. In this minireview article different pancreatic imaging techniques are discussed, from the standard clinical imaging modalities and state of the art clinical magnetic resonance imaging techniques to current situations in pre-clinical pancreatic imaging studies. In particular, the challenges of pre-clinical rodent pancreatic imaging are addressed, with both the image acquisition techniques and the post-processing methods for rodent pancreatic imaging elaborated.

Iterative projection meets sparsity regularization:towards practical single-shot quantitative phase imaging with in-line holography

Holography provides access to the optical phase.The emerging compressive phase retrieval approach can achieve in-line holographic imaging beyond the information-theoretic limit or even from a single shot by exploring the signal priors.However,iterative projection methods based on physical knowledge of the wavefield suffer from poor imaging quality,whereas the regularization techniques sacrifice robustness for fidelity.In this work,we present a unified compressive phase retrieval framework for in-line holography that encapsulates the unique advantages of both physical constraints and sparsity priors.In particular,a constrained complex total variation(CCTV)regularizer is introduced that explores the well-known absorption and support constraints together with sparsity in the gradient domain,enabling practical high-quality in-line holographic imaging from a single intensity image.We developed efficient solvers based on the proximal gradient method for the non-smooth regularized inverse problem and the corresponding denoising subproblem.Theoretical analyses further guarantee the convergence of the algorithms with prespecified parameters,obviating the need for manual parameter tuning.As both simulated and optical experiments demonstrate,the proposed CCTV model can characterize complex natural scenes while utilizing physically tractable constraints for quality enhancement.This new compressive phase retrieval approach can be extended,with minor adjustments,to various imaging configurations,sparsifying operators,and physical knowledge.It may cast new light on both theoretical and empirical studies.

Developing global image feature analysis models to predict cancer risk and prognosis

In order to develop precision or personalized medicine,identifying new quantitative imaging markers and building machine learning models to predict cancer risk and prognosis has been attracting broad research interest recently.Most of these research approaches use the similar concepts of the conventional computer-aided detection schemes of medical images,which include steps in detecting and segmenting suspicious regions or tumors,followed by training machine learning models based on the fusion of multiple image features computed from the segmented regions or tumors.However,due to the heterogeneity and boundary fuzziness of the suspicious regions or tumors,segmenting subtle regions is often difficult and unreliable.Additionally,ignoring global and/or background parenchymal tissue characteristics may also be a limitation of the conventional approaches.In our recent studies,we investigated the feasibility of developing new computer-aided schemes implemented with the machine learning models that are trained by global image features to predict cancer risk and prognosis.We trained and tested several models using images obtained from full-field digital mammography,magnetic resonance imaging,and computed tomography of breast,lung,and ovarian cancers.Study results showed that many of these new models yielded higher performance than other approaches used in current clinical practice.Furthermore,the computed global image features also contain complementary information from the features computed from the segmented regions or tumors in predicting cancer prognosis.Therefore,the global image features can be used alone to develop new case-based prediction models or can be added to current tumor-based models to increase their discriminatory power.

Comparison Thigh Skeletal Muscles between Snowboarding Halfpipe Athletes and Healthy Volunteers Using Quantitative Multi-Parameter Magnetic Resonance Imaging at Rest

Background：Magnetic resonance （MR） imaging provides a unique,noninvasive diagnostic platform to quantify the physiological and biochemical variables of skeletal muscle at rest.This study was to investigate the difference in thigh skeletal muscles between snowboarding halfpipe athletes and healthy volunteers via multiparametric MR imaging.Methods：A comparative study was conducted between 12 healthy volunteers and 14 snowboarding halfpipe athletes.MR scanning targeted the left leg at the level of the proximal thigh on a 3.0T MR system.The measured parameters compared between the two groups included T1,T2,T2＊ relaxation times,fat fraction （FF）,and cross-sectional area （CSA） of the quadriceps femoris and the hamstring muscles.Statistical analysis was carried out using independent sample t-test.Interrater reliability was also assessed with intraclass correlation coefficients （ICCs）.Results：It was statistically equivalent between two groups in age,body mass index,thigh circumference,calf circumference,systolic blood pressure,and resting heart rate （all P ＞ 0.05）.However,the T1 and T2 values of the hamstring muscles in the athlete group were found to be significantly shorter than those in control group （T1：1063.3 ± 24.1 ms vs.1112.0 ± 38.2 ms in biceps femoris,1050.4 ± 31.2 ms vs.1095.0 ± 39.5 ms in semitendinosus,1053.1 ± 31.7 ms vs.1118.4 ± 40.0 ms in semimembranosus,respectively;T2：33.4 ± 0.7 ms vs.36.1 ± 1.9 ms in biceps femoris,34.6 ± 2.0 ms vs.37.0 ± 1.9 ms in semitendinosus,36.9 ± 1.5 ms vs.38.9 ± 2.4 ms in semimembranosus,respectively;all P ＜ 0.05） although T2＊ relaxation time was detected with no significant difference.The FF of the hamstring muscles was obviously less than the control group （5.5 ± 1.9％ vs.10.7 ± 4.7％,P ＜ 0.001）.In addition,the quadriceps＇ CSA in the athlete group was substantially larger than the control group （8039.0 ± 1072.3 vs.6258.2 ± 852.0 mm2,P ＜ 0.001）.Interrater reliability was excellent （ICC：0.758-0.994）.Conclusion：Multiple MR imaging parameters indicated significant differences between snowboarding halfpipe athletes and healthy volunteers in the thigh skeletal muscles.

Combining quantitative and qualitative magnetic resonance imaging features to differentiate anorectal malignant melanoma from low rectal cancer

Background:Distinguishing anorectal malignant melanoma from low rectal cancer remains challenging because of the overlap of clinical symptoms and imaging findings.We aim to investigate whether combining quantitative and qualitative magnetic resonance imaging(MRI)features could differentiate anorectal malignant melanoma from low rectal cancer.Methods:Thirty-seven anorectal malignant melanoma and 98 low rectal cancer patients who underwent preoperative rectal MRI from three hospitals were retrospectively enrolled.All patients were divided into the primary cohort(N=84)and validation cohort(N=51).Quantitative image analysiswas performed on T1-weighted(T1WI),T2-weighted(T2WI),and contrast-enhanced T1-weighted imaging(CE-T1WI).The subjective qualitative MRI findings were evaluated by two radiologists in consensus.Multivariable analysis was performed using stepwise logistic regression.The discrimination performance was assessed by the area under the receiver operating characteristic curve(AUC)with a 95%confidence interval(CI).Results:The skewness derived from T2WI(T2WI-skewness)showed the best discrimination performance among the entire quantitative image features for differentiating anorectal malignant melanoma from low rectal cancer(primary cohort:AUC=0.852,95%CI 0.788–0.916;validation cohort:0.730,0.645–0.815).Multivariable analysis indicated that T2WI-skewness and the signal intensity of T1WI were independent factors,and incorporating both factors achieved good discrimination performance in two cohorts(primary cohort:AUC=0.913,95%CI 0.868–0.958;validation cohort:0.902,0.844–0.960).Conclusions:Incorporating T2WI-skewness and the signal intensity of T1WI achieved good performance for differentiating anorectal malignant melanoma from low rectal cancer.The quantitative image analysis helps improve diagnostic accuracy.

＂RADIOTRANSCRIPTOMICS＂： A synergy of imaging and transcriptomics in clinical assess- ment

Recent advances in quantitative imaging and ＂omics＂ technology have generated a wealth of mineable biological ＂big data＂. With the push towards a P4 ＂predictive, preventive, personalized, and participatory＂ approach to medicine, researchers began integrating complementary tools to further tune existing diagnostic and therapeutic models. The field of radiogenomics has long pioneered such multidisciplinary investigations in neuroscience and oncology, correlating genotypic and phenotypic signatures to study structural and functional changes in relation to altered molecular behavior. Given the innate dynamic nature of complex disorders and the role of environmental and epigenetic factors in pathogenesis, the transcriptome can further elucidate serial modifications undetected at the genome level. We therefore propose ＂radiotranscriptomics＂ as a new member of the P4 medicine initiative, combining transcriptome information, including gene expression and isoform variation, and quantitative image annotations.

Deep-learning-based prediction of living cells mitosis via quantitative phase microscopy

We present a deep learning approach for living cells mitosis classification based on label-free quantitative phase imaging with transport of intensity equation methods.In the approach,we applied a pretrained deep convolutional neural network using transfer learning for binary classification of mitosis and non-mitosis.As a validation,we demonstrated the performances of the network trained by phase images and intensity images,respectively.The convolutional neural network trained by phase images achieved an average accuracy of 98.9%on the validation data,which outperforms the average accuracy 89.6%obtained by the network trained by intensity images.We believe that the quantitative phase microscopy in combination with deep learning enables researchers to predict the mitotic status of living cells noninvasively and efficiently.

Enhanced quantitative X-ray phase-contrast images using Foucault differential filters

Several X-ray phase visualization methods are being real- ized for imaging of phase objects, such as biological and polymeric specimens. Grating-based phase-contrast imaging using a source-grating-attached X-ray tube that provides partially coherent X rays is one of the most successful methods in this field.

Radiomics/Radiogenomics in hepatocellular carcinoma: Applications and challenges in interventional management

Hepatocellular carcinoma is one of the leading causes of cancer-related death worldwide.Recently,radiomics and radiogenomics have been introduced as novel dimensions in oncology research.In the current review,we summarize the clinical applications of radiomics and radiogenomics in hepatocellular carcinoma.

Optically monitoring and controlling nanoscale topography during semiconductor etching

We present epi-diffraction phase microscopy(epi-DPM)as a non-destructive optical method for monitoring semiconductor fabrication processes in real time and with nanometer level sensitivity.The method uses a compact Mach–Zehnder interferometer to recover quantitative amplitude and phase maps of the field reflected by the sample.The low temporal noise of 0.6 nm per pixel at 8.93 frames per second enabled us to collect a three-dimensional movie showing the dynamics of wet etching and thereby accurately quantify non-uniformities in the etch rate both across the sample and over time.By displaying a gray-scale digital image on the sample with a computer projector,we performed photochemical etching to define arrays of microlenses while simultaneously monitoring their etch profiles with epi-DPM.

Integrated self-referencing single shot digital holographic microscope and optical tweezer

Digital holographic microscopy is a single-shot technique for quantitative phase imaging of samples,yielding thickness profiles of phase objects.It provides sample features based on their morphology,leading to their classification and identification.However,observing samples,especially cells,in fluids using holographic microscopes is difficult without immobilizing the object.Optical tweezers can be used for sample immobilization in fluids.The present manuscript provides an overview of our ongoing work on the development of a compact,low-cost microscopy system for digital holographic imaging of optically trapped samples.Integration of digital holographic microscopy system with tweezers is realized by using the optical pickup unit extracted from DVD burners to trap microsamples,which are then holographically imaged using a highly compact self-referencing interferometer along with a low-cost,in-house developed quadrant photodiode,providing morphological and spectral information of trapped particles.The developed integrated module was tested using polystyrene microspheres as well as human erythrocytes.The investigated system offers a multitude of sample features,including physical and mechanical parameters and corner frequency information of the sample.These features were used for sample classification.The proposed technique has vast potential in opening up new avenues for low-cost,digital holographic imaging and analysis of immobilized samples in fluids and their classification.