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.

Multi-prior physics-enhanced neural network enables pixel super-resolution and twin-imagefree phase retrieval from single-shot hologram

Digital in-line holographic microscopy(DIHM)is a widely used interference technique for real-time reconstruction of living cells’morphological information with large space-bandwidth product and compact setup.However,the need for a larger pixel size of detector to improve imaging photosensitivity,field-of-view,and signal-to-noise ratio often leads to the loss of sub-pixel information and limited pixel resolution.Additionally,the twin-image appearing in the reconstruction severely degrades the quality of the reconstructed image.The deep learning(DL)approach has emerged as a powerful tool for phase retrieval in DIHM,effectively addressing these challenges.However,most DL-based strategies are datadriven or end-to-end net approaches,suffering from excessive data dependency and limited generalization ability.Herein,a novel multi-prior physics-enhanced neural network with pixel super-resolution(MPPN-PSR)for phase retrieval of DIHM is proposed.It encapsulates the physical model prior,sparsity prior and deep image prior in an untrained deep neural network.The effectiveness and feasibility of MPPN-PSR are demonstrated by comparing it with other traditional and learning-based phase retrieval methods.With the capabilities of pixel super-resolution,twin-image elimination and high-throughput jointly from a single-shot intensity measurement,the proposed DIHM approach is expected to be widely adopted in biomedical workflow and industrial measurement.

Quantitative magnetic resonance imaging in prostate cancer:A review of current technology

Prostate cancer(PCa)imaging forms an important part of PCa clinical management.Magnetic resonance imaging is the modality of choice for prostate imaging.Most of the current imaging assessment is qualitative i.e.,based on visual inspection and thus subjected to inter-observer disagreement.Quantitative imaging is better than qualitative assessment as it is more objective,and standardized,thus improving interobserver agreement.Apart from detecting PCa,few quantitative parameters may have potential to predict disease aggressiveness,and thus can be used for prognosis and deciding the course of management.There are various magnetic resonance imaging-based quantitative parameters and few of them are already part of PIRADS v.2.1.However,there are many other parameters that are under study and need further validation by rigorous multicenter studies before recommending them for routine clinical practice.This review intends to discuss the existing quantitative methods,recent developments,and novel techniques in detail.

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 .

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.

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.

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.

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.

Quantitative T2＊-Mapping of the Knee Using a Spoiled Gradient Echo Sequence at 3 Tesla： Preliminary Results

Purpose： To evaluate a me-vTE-SPGR （multi echo variable TE Spoiled Gradient Echo Sequence） approach for quantitative T2＊ mapping of the ME （menisci）, the PT （patellar tendon）, the ACL （anterior cruciate ligament）, the PCL （posterior cruciate ligament） and to compare the results between normal and pathological tissue of the ME in the knee joint at 3T （3 Tesla）. Methods： Eighteen consecutive knee patients （35.7± 11.6 years） were examined on 3T. In addition to standard morphological MRI, T2＊-maps were derived from a 0.7 mm isotropic me-vTE-SPGR scan. T2＊-values were assessed by two independent observers using an ROI analysis for the ME （4 different regions： posterior and anterior horn of the medial and lateral meniscus）, PT, ACL and PCL. Intra-class correlation between readers was calculated. Results： On morphological MRI, the PT, ACL and PCL were diagnosed as normal in all cases. Degenerative meniscus and meniscal tears were diagnosed in 13 cases and 9 cases, respectively. T2＊-values of the menisci on me-vTE-SPGR scans, in relation to morphological imaging, were normal （N = 50; 6.0 ±0.9 ms）; degenerative meniscus （N = 13; 8.0± 1.6 ms）; meniscal tears （N = 9; 12.9 ±3.9 ms）, with significant differences between all groups （P 〈 0.05）/ significantly higher T2＊-values in degenerative meniscus and meniscal tears. Mean T2＊ relaxation times for the PT, ACL and PCL were 2.9± 0.8 ms, 8.4 ± 1.6 ms and 8.9 ＋ 1.3 ms respectively. Intra-class correlation values between readers for the ME, PT, ACL and PCL were R2 = 0.962, R2 = 0.927, R2 = 0.594 and R2= 0.648, respectively. Conclusion： Isotropic 3D （three-dimensional） me vTE-SPGR imaging is able to quantify T2＊ values of multiple tissues in the knee joint with short T2 relaxation times.

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.

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.

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.

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.

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.

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.

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.

Towards precision medicine: from quantitative imaging to radiomics

Radiology（imaging） and imaging-guided interventions, which provide multi-parametric morphologic and functional information, are playing an increasingly significant role in precision medicine. Radiologists are trained to understand the imaging phenotypes, transcribe those observations（phenotypes） to correlate with underlying diseases and to characterize the images. However, in order to understand and characterize the molecular phenotype（to obtain genomic information） of solid heterogeneous tumours, the advanced sequencing of those tissues using biopsy is required. Thus, radiologists image the tissues from various views and angles in order to have the complete image phenotypes, thereby acquiring a huge amount of data. Deriving meaningful details from all these radiological data becomes challenging and raises the big data issues. Therefore, interest in the application of radiomics has been growing in recent years as it has the potential to provide significant interpretive and predictive information for decision support. Radiomics is a combination of conventional computer-aided diagnosis, deep learning methods, and human skills, and thus can be used for quantitative characterization of tumour phenotypes. This paper discusses the overview of radiomics workflow, the results of various radiomics-based studies conducted using various radiological images such as computed tomography（CT）, magnetic resonance imaging（MRI）, and positron-emission tomography（PET）, the challenges we are facing, and the potential contribution of radiomics towards precision medicine.

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.

Quantitative phase imaging(QPI)through random diffusers using a diffractive optical network

Quantitative phase imaging(QPI)is a label-free computational imaging technique used in various fields,including biology and medical research.Modern QPI systems typically rely on digital processing using iterative algorithms for phase retrieval and image reconstruction.Here,we report a diffractive optical network trained to convert the phase information of input objects positioned behind random diffusers into intensity variations at the output plane,all-optically performing phase recovery and quantitative imaging of phase objects completely hidden by unknown,random phase diffusers.This QPI diffractive network is composed of successive diffractive layers,axially spanning in total~70λ,where is the illumination wavelength;unlike existing digital image reconstruction and phase retrieval methods,it forms an all-optical processor that does not require external power beyond the illumination beam to complete its QPI reconstruction at the speed of light propagation.This all-optical diffractive processor can provide a low-power,high frame rate and compact alternative for quantitative imaging of phase objects through random,unknown diffusers and can operate at different parts of the electromagnetic spectrum for various applications in biomedical imaging and sensing.The presented QPI diffractive designs can be integrated onto the active area of standard CCD/CMOS-based image sensors to convert an existing optical microscope into a diffractive QPI microscope,performing phase recovery and image reconstruction on a chip through light diffraction within passive structured layers.

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.