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Nuclear magnetic resonance spectroscopy (MRS) allows for the determination ofatomic structures and concentrations of different chemicals in a biochemicalsample of interest. MRS is used in vivo clinically to aid in the diagnosis ofseveral pathologies that affect metabolic pathways in the body. Typically, thisexperiment produces a one dimensional (1D) 1H spectrum containing several peaksthat are well associated with biochemicals, or metabolites. However, since manyof these peaks overlap, distinguishing chemicals with similar atomic structuresbecomes much more challenging. One technique capable of overcoming this issueis the localized correlated spectroscopy (L-COSY) experiment, which acquires asecond spectral dimension and spreads overlapping signal across this seconddimension. Unfortunately, the acquisition of a two dimensional (2D)spectroscopy experiment is extremely time consuming. Furthermore, quantitationof a 2D spectrum is more complex. Recently, artificial intelligence has emergedin the field of medicine as a powerful force capable of diagnosing disease,aiding in treatment, and even predicting treatment outcome. In this study, weutilize deep learning to: 1) accelerate the L-COSY experiment and 2) quantifyL-COSY spectra. We demonstrate that our deep learning model greatly outperformscompressed sensing based reconstruction of L-COSY spectra at higheracceleration factors. Specifically, at four-fold acceleration, our method hasless than 5% normalized mean squared error, whereas compressed sensing yields20% normalized mean squared error. We also show that at low SNR (25% noisecompared to maximum signal), our deep learning model has less than 8%normalized mean squared error for quantitation of L-COSY spectra. These pilotsimulation results appear promising and may help improve the efficiency andaccuracy of L-COSY experiments in the future.

Histological staining is a vital step in diagnosing various diseases and has been used for more than a century to provide contrast in tissue sections, rendering the tissue constituents visible for microscopic analysis by medical experts. However, this process is time consuming, labour intensive, expensive and destructive to the specimen. Recently, the ability to virtually stain unlabelled tissue sections, entirely avoiding the histochemical staining step, has been demonstrated using tissue-stain-specific deep neural networks. Here, we present a new deep-learning-based framework that generates virtually stained images using label-free tissue images, in which different stains are merged following a micro-structure map defined by the user. This approach uses a single deep neural network that receives two different sources of information as its input: (1) autofluorescence images of the label-free tissue sample and (2) a "digital staining matrix", which represents the desired microscopic map of the different stains to be virtually generated in the same tissue section. This digital staining matrix is also used to virtually blend existing stains, digitally synthesizing new histological stains. We trained and blindly tested this virtual-staining network using unlabelled kidney tissue sections to generate micro-structured combinations of haematoxylin and eosin (H&E), Jones' silver stain, and Masson's trichrome stain. Using a single network, this approach multiplexes the virtual staining of label-free tissue images with multiple types of stains and paves the way for synthesizing new digital histological stains that can be created in the same tissue cross section, which is currently not feasible with standard histochemical staining methods.

Cardiac 18F-FDG PET has been used in clinics to assess myocardial glucose metabolism. Its ability for imaging myocardial glucose transport, however, has rarely been exploited in clinics. Using the dynamic FDG-PET scans of ten patients with coronary artery disease, we investigate in this paper appropriate dynamic scan and kinetic modeling protocols for efficient quantification of myocardial glucose transport. Three kinetic models and the effect of scan duration were evaluated by using statistical fit quality, assessing the impact on kinetic quantification, and analyzing the practical identifiability. The results show that the kinetic model selection depends on the scan duration. The reversible two-tissue model was needed for a one-hour dynamic scan. The irreversible two-tissue model was optimal for a scan duration of around 10-15 minutes. If the scan duration was shortened to 2-3 minutes, a one-tissue model was the most appropriate. For global quantification of myocardial glucose transport, we demonstrated that an early dynamic scan with a duration of 10-15 minutes and irreversible kinetic modeling was comparable to the full one-hour scan with reversible kinetic modeling. Myocardial glucose transport quantification provides an additional physiological parameter on top of the existing assessment of glucose metabolism and has the potential to enable single tracer multiparametric imaging in the myocardium.

PurposeThe balanced steady-state free precession sequence has been previously explored to improve the efficient use of nonrecoverable hyperpolarized 13C magnetization, but suffers from poor spectral selectivity and long acquisition time. The purpose of this study was to develop a novel metabolite-specific 3D bSSFP ("MS-3DSSFP") sequence with stack-of-spiral readouts for improved lactate imaging in hyperpolarized [1-13 C]pyruvate studies on a clinical 3T scanner.MethodsSimulations were performed to evaluate the spectral response of the MS-3DSSFP sequence. Thermal 13C phantom experiments were performed to validate the MS-3DSSFP sequence. In vivo hyperpolarized [1-13 C], pyruvate studies were performed to compare the MS-3DSSFP sequence with metabolite-specific gradient echo ("MS-GRE") sequences for lactate imaging.ResultsSimulations, phantom, and in vivo studies demonstrate that the MS-3DSSFP sequence achieved spectrally selective excitation on lactate while minimally perturbing other metabolites. Compared with MS-GRE sequences, the MS-3DSSFP sequence showed approximately a 2.5-fold SNR improvement for lactate imaging in rat kidneys, prostate tumors in a mouse model, and human kidneys.ConclusionsImproved lactate imaging using the MS-3DSSFP sequence in hyperpolarized [1-13 C]pyruvate studies was demonstrated in animals and humans. The MS-3DSSFP sequence could be applied for other clinical applications such as in the brain or adapted for imaging other metabolites such as pyruvate and bicarbonate.

PurposeThe balanced steady-state free precession sequence has been previously explored to improve the efficient use of nonrecoverable hyperpolarized 13C magnetization, but suffers from poor spectral selectivity and long acquisition time. The purpose of this study was to develop a novel metabolite-specific 3D bSSFP ("MS-3DSSFP") sequence with stack-of-spiral readouts for improved lactate imaging in hyperpolarized [1-13 C]pyruvate studies on a clinical 3T scanner.MethodsSimulations were performed to evaluate the spectral response of the MS-3DSSFP sequence. Thermal 13C phantom experiments were performed to validate the MS-3DSSFP sequence. In vivo hyperpolarized [1-13 C], pyruvate studies were performed to compare the MS-3DSSFP sequence with metabolite-specific gradient echo ("MS-GRE") sequences for lactate imaging.ResultsSimulations, phantom, and in vivo studies demonstrate that the MS-3DSSFP sequence achieved spectrally selective excitation on lactate while minimally perturbing other metabolites. Compared with MS-GRE sequences, the MS-3DSSFP sequence showed approximately a 2.5-fold SNR improvement for lactate imaging in rat kidneys, prostate tumors in a mouse model, and human kidneys.ConclusionsImproved lactate imaging using the MS-3DSSFP sequence in hyperpolarized [1-13 C]pyruvate studies was demonstrated in animals and humans. The MS-3DSSFP sequence could be applied for other clinical applications such as in the brain or adapted for imaging other metabolites such as pyruvate and bicarbonate.

Purpose: Proximal femur image analyses based on quantitative computedtomography (QCT) provide a method to quantify the bone density and evaluateosteoporosis and risk of fracture. We aim to develop a deep-learning-basedmethod for automatic proximal femur segmentation. Methods and Materials: Wedeveloped a 3D image segmentation method based on V-Net, an end-to-end fullyconvolutional neural network (CNN), to extract the proximal femur QCT imagesautomatically. The proposed V-net methodology adopts a compound loss function,which includes a Dice loss and a L2 regularizer. We performed experiments toevaluate the effectiveness of the proposed segmentation method. In theexperiments, a QCT dataset which included 397 QCT subjects was used. For theQCT image of each subject, the ground truth for the proximal femur wasdelineated by a well-trained scientist. During the experiments for the entirecohort then for male and female subjects separately, 90% of the subjects wereused in 10-fold cross-validation for training and internal validation, and toselect the optimal parameters of the proposed models; the rest of the subjectswere used to evaluate the performance of models. Results: Visual comparisondemonstrated high agreement between the model prediction and ground truthcontours of the proximal femur portion of the QCT images. In the entire cohort,the proposed model achieved a Dice score of 0.9815, a sensitivity of 0.9852 anda specificity of 0.9992. In addition, an R2 score of 0.9956 (p

PurposeTo investigate the feasibility of automatic identification and classification of hip fractures using deep learning, which may improve outcomes by reducing diagnostic errors and decreasing time to operation.Materials and methodsHip and pelvic radiographs from 1118 studies were reviewed, and 3026 hips were labeled via bounding boxes and classified as normal, displaced femoral neck fracture, nondisplaced femoral neck fracture, intertrochanteric fracture, previous open reduction and internal fixation, or previous arthroplasty. A deep learning-based object detection model was trained to automate the placement of the bounding boxes. A Densely Connected Convolutional Neural Network (or DenseNet) was trained on a subset of the bounding box images, and its performance was evaluated on a held-out test set and by comparison on a 100-image subset with two groups of human observers: fellowship-trained radiologists and orthopedists; senior residents in emergency medicine, radiology, and orthopedics.ResultsThe binary accuracy for detecting a fracture of this model was 93.7% (95% confidence interval [CI]: 90.8%, 96.5%), with a sensitivity of 93.2% (95% CI: 88.9%, 97.1%) and a specificity of 94.2% (95% CI: 89.7%, 98.4%). Multiclass classification accuracy was 90.8% (95% CI: 87.5%, 94.2%). When compared with the accuracy of human observers, the accuracy of the model achieved an expert-level classification, at the very least, under all conditions. Additionally, when the model was used as an aid, human performance improved, with aided resident performance approximating unaided fellowship-trained expert performance in the multiclass classification.ConclusionA deep learning model identified and classified hip fractures with expert-level performance, at the very least, and when used as an aid, improved human performance, with aided resident performance approximating that of unaided fellowship-trained attending physicians.Supplemental material is available for this article.© RSNA, 2020.

A stacked-target of natural lanthanum foils (99.9119% 139La) was irradiated using a 60 MeV proton beam at the LBNL 88-Inch Cyclotron. 139La(p,x) cross sections are reported between 35–60 MeV for nine product radionuclides. The primary motivation for this measurement was the need to quantify the production of 134Ce. As a positron-emitting analogue of the promising medical radionuclide 225Ac, 134Ce is desirable for in vivo applications of bio-distribution assays for this emerging radio-pharmaceutical. The results of this measurement were compared to the nuclear model codes TALYS, EMPIRE and ALICE (using default parameters), which showed significant deviation from the measured values.

Currently in HDR brachytherapy planning, a manual fine-tuning of an objective function is necessary to obtain case-specific valid plans. This study intends to facilitate this process by proposing a patient-specific inverse planning algorithm for HDR prostate brachytherapy: GPU-based multi-criteria optimization (gMCO). Two GPU-based optimization engines including simulated annealing (gSA) and a quasi-Newton optimizer (gL-BFGS) were implemented to compute multiple plans in parallel. After evaluating the equivalence and the computation performance of these two optimization engines, one preferred optimization engine was selected for the gMCO algorithm. Five hundred sixty-two previously treated prostate HDR cases were divided into validation set (100) and test set (462). In the validation set, the number of Pareto optimal plans to achieve the best plan quality was determined for the gMCO algorithm. In the test set, gMCO plans were compared with the physician-approved clinical plans. Our results indicated that the optimization process is equivalent between gL-BFGS and gSA, and that the computational performance of gL-BFGS is up to 67 times faster than gSA. Over 462 cases, the number of clinically valid plans was 428 (92.6%) for clinical plans and 461 (99.8%) for gMCO plans. The number of valid plans with target [Formula: see text] coverage greater than 95% was 288 (62.3%) for clinical plans and 414 (89.6%) for gMCO plans. The mean planning time was 9.4 s for the gMCO algorithm to generate 1000 Pareto optimal plans. In conclusion, gL-BFGS is able to compute thousands of SA equivalent treatment plans within a short time frame. Powered by gL-BFGS, an ultra-fast and robust multi-criteria optimization algorithm was implemented for HDR prostate brachytherapy. Plan pools with various trade-offs can be created with this algorithm. A large-scale comparison against physician approved clinical plans showed that treatment plan quality could be improved and planning time could be significantly reduced with the proposed gMCO algorithm.

PurposeAcquisition timing and B1 calibration are two key factors that affect the quality and accuracy of hyperpolarized 13 C MRI. The goal of this project was to develop a new approach using regional bolus tracking to trigger Bloch-Siegert B1 mapping and real-time B1 calibration based on regional B1 measurements, followed by dynamic imaging of hyperpolarized 13 C metabolites in vivo.MethodsThe proposed approach was implemented on a system which allows real-time data processing and real-time control on the sequence. Real-time center frequency calibration upon the bolus arrival was also added. The feasibility of applying the proposed framework for in vivo hyperpolarized 13 C imaging was tested on healthy rats, tumor-bearing mice and a healthy volunteer on a clinical 3T scanner following hyperpolarized [1-13 C]pyruvate injection. Multichannel receive coils were used in the human study.ResultsAutomatic acquisition timing based on either regional bolus peak or bolus arrival was achieved with the proposed framework. Reduced blurring artifacts in real-time reconstructed images were observed with real-time center frequency calibration. Real-time computed B1 scaling factors agreed with real-time acquired B1 maps. Flip angle correction using B1 maps results in a more consistent quantification of metabolic activity (i.e, pyruvate-to-lactate conversion, kPL ). Experiment recordings are provided to demonstrate the real-time actions during the experiment.ConclusionsThe proposed method was successfully demonstrated on animals and a human volunteer, and is anticipated to improve the efficient use of the hyperpolarized signal as well as the accuracy and robustness of hyperpolarized 13 C imaging.

PurposeAcquisition timing and B1 calibration are two key factors that affect the quality and accuracy of hyperpolarized 13 C MRI. The goal of this project was to develop a new approach using regional bolus tracking to trigger Bloch-Siegert B1 mapping and real-time B1 calibration based on regional B1 measurements, followed by dynamic imaging of hyperpolarized 13 C metabolites in vivo.MethodsThe proposed approach was implemented on a system which allows real-time data processing and real-time control on the sequence. Real-time center frequency calibration upon the bolus arrival was also added. The feasibility of applying the proposed framework for in vivo hyperpolarized 13 C imaging was tested on healthy rats, tumor-bearing mice and a healthy volunteer on a clinical 3T scanner following hyperpolarized [1-13 C]pyruvate injection. Multichannel receive coils were used in the human study.ResultsAutomatic acquisition timing based on either regional bolus peak or bolus arrival was achieved with the proposed framework. Reduced blurring artifacts in real-time reconstructed images were observed with real-time center frequency calibration. Real-time computed B1 scaling factors agreed with real-time acquired B1 maps. Flip angle correction using B1 maps results in a more consistent quantification of metabolic activity (i.e, pyruvate-to-lactate conversion, kPL ). Experiment recordings are provided to demonstrate the real-time actions during the experiment.ConclusionsThe proposed method was successfully demonstrated on animals and a human volunteer, and is anticipated to improve the efficient use of the hyperpolarized signal as well as the accuracy and robustness of hyperpolarized 13 C imaging.

Carotid atherosclerosis is a focal disease at the bifurcations of the carotidartery. To quantitatively monitor the local changes in thevessel-wall-plus-plaque thickness (VWT) and compare the VWT distributions fordifferent patients or for the same patients at different ultrasound scanningsessions, a mapping technique is required to adjust for the geometricvariability of different carotid artery models. In this work, we propose anovel method called density-equalizing reference map (DERM) for mapping 3Dcarotid surfaces to a standardized 2D carotid template, with an emphasis onpreserving the local geometry of the carotid surface by minimizing the localarea distortion. The initial map was generated by a previously describedarc-length scaling (ALS) mapping method, which projects a 3D carotid surfaceonto a 2D non-convex L-shaped domain. A smooth and area-preserving flattenedmap was subsequently constructed by deforming the ALS map using the proposedalgorithm that combines the density-equalizing map and the reference maptechniques. This combination allows, for the first time, one-to-one mappingfrom a 3D surface to a standardized non-convex planar domain in anarea-preserving manner. Evaluations using 20 carotid surface models show thatthe proposed method reduced the area distortion of the flattening maps by over80% as compared to the ALS mapping method.

Low-dose Proton Computed Tomography (pCT) is an evolving imaging modalitythat is used in proton therapy planning which addresses the range uncertaintyproblem. The goal of pCT is generating a 3D map of Relative Stopping Power(RSP) measurements with high accuracy within clinically required time frames.Generating accurate RSP values within the shortest amount of time is considereda key goal when developing a pCT software. The existing pCT softwares havesuccessfully met this time frame and even succeeded this time goal, butrequiring clusters with hundreds of processors. This paper describes a novel reconstruction technique using two GraphicsProcessing Unit (GPU) cores, such as is available on a single Nvidia P100. Theproposed reconstruction technique is tested on both simulated and experimentaldatasets and on two different systems namely Nvidia K40 and P100 GPUs from IBMand Cray. The experimental results demonstrate that our proposed reconstructionmethod meets both the timing and accuracy with the benefit of having reasonablecost, and efficient use of power.

Conventional beam orientation optimization (BOO) algorithms for IMRT assume that the same set of beam angles is used for all treatment fractions. In this paper we present a BOO formulation based on group sparsity that simultaneously optimizes non-coplanar beam angles for all fractions, yielding a fraction-variant (FV) treatment plan. Beam angles are selected by solving a multi-fraction fluence map optimization problem involving 500-700 candidate beams per fraction, with an additional group sparsity term that encourages most candidate beams to be inactive. The optimization problem is solved using the fast iterative shrinkage-thresholding algorithm. Our FV BOO algorithm is used to create five-fraction treatment plans for digital phantom, prostate, and lung cases as well as a 30-fraction plan for a head and neck case. A homogeneous PTV dose coverage is maintained in all fractions. The treatment plans are compared with fraction-invariant plans that use a fixed set of beam angles for all fractions. The FV plans reduced OAR mean dose and D 2 values on average by 3.3% and 3.8% of the prescription dose, respectively. Notably, mean OAR dose was reduced by 14.3% of prescription dose (rectum), 11.6% (penile bulb), 10.7% (seminal vesicle), 5.5% (right femur), 3.5% (bladder), 4.0% (normal left lung), 15.5% (cochleas), and 5.2% (chiasm). D 2 was reduced by 14.9% of prescription dose (right femur), 8.2% (penile bulb), 12.7% (proximal bronchus), 4.1% (normal left lung), 15.2% (cochleas), 10.1% (orbits), 9.1% (chiasm), 8.7% (brainstem), and 7.1% (parotids). Meanwhile, PTV homogeneity defined as D 95/D 5 improved from .92 to .95 (digital phantom), from .95 to .98 (prostate case), and from .94 to .97 (lung case), and remained constant for the head and neck case. Moreover, the FV plans are dosimetrically similar to conventional plans that use twice as many beams per fraction. Thus, FV BOO offers the potential to reduce delivery time for non-coplanar IMRT.

Due to the low x-ray photon utilization efficiency and low measurement sensitivity of the electron multiplying charge coupled device camera setup, the collimator-based narrow beam x-ray luminescence computed tomography (XLCT) usually requires a long measurement time. We, for the first time, report a focused x-ray beam-based XLCT imaging system with measurements by a single optical fiber bundle and a photomultiplier tube (PMT). An x-ray tube with a polycapillary lens was used to generate a focused x-ray beam whose x-ray photon density is 1200 times larger than a collimated x-ray beam. An optical fiber bundle was employed to collect and deliver the emitted photons on the phantom surface to the PMT. The total measurement time was reduced to 12.5 min. For numerical simulations of both single and six fiber bundle cases, we were able to reconstruct six targets successfully. For the phantom experiment, two targets with an edge-to-edge distance of 0.4 mm and a center-to-center distance of 0.8 mm were successfully reconstructed by the measurement setup with a single fiber bundle and a PMT.

Dual-energy X-ray absorptiometry (DEXA) has been widely applied to measurebone mineral density (BMD) and soft-tissue composition of human body. However,the use of DEXA is greatly limited for low-Z materials such as soft tissues dueto their weak absorption. While X-ray phase-contrast imaging (XPCI) showssignificantly improved contrast in comparison with the conventional standardabsorption-based X-ray imaging for soft tissues. In this paper, we propose anovel X-ray phase-contrast method to measure the area density of low-Zmaterials, including a single-energy method and a dual-energy method. Thesingle-energy method is for the area density calculation of one low-Z material,while the dual-energy method is aiming to calculate the area densities of twolow-Z materials simultaneously. Comparing the experimental and simulationresults with the theoretic ones, the new method proves to have the potential toreplace DEXA in area density measurement. The new method sets the prerequisitesfor future precise and low-dose area density calculation method of low-Zmaterials.

Proton computed tomography (pCT) is a novel medical imaging modality formapping the distribution of proton relative stopping power (RSP) in medicalobjects of interest. Compared to conventional X-ray computed tomography, whererange uncertainty margins are around 3.5%, pCT has the potential to providemore accurate measurements to within 1%. This improved efficiency will bebeneficial to proton-therapy planning and pre-treatment verification. Aprototype pCT imaging device has recently been developed capable of rapidlyacquiring low-dose proton radiographs of head-sized objects. We have alsodeveloped an advanced, fast image reconstruction software based on distributedcomputing that utilizes parallel processors and graphical processing units. Thecombination of fast data acquisition and fast image reconstruction will enablethe availability of RSP images within minutes for use in clinical settings. Theperformance of our image reconstruction software has been evaluated using datacollected by the prototype pCT scanner from several phantoms.

We are exploring low-dose proton radiography and computed tomography (pCT) astechniques to improve the accuracy of proton treatment planning and to provideartifact-free images for verification and adaptive therapy at the time oftreatment. Here we report on comprehensive beam test results with our prototypepCT head scanner. The detector system and data acquisition attain a sustainedrate of more than a million protons individually measured per second, allowinga full CT scan to be completed in six minutes or less of beam time. In order toassess the performance of the scanner for proton radiography as well ascomputed tomography, we have performed numerous scans of phantoms at theNorthwestern Medicine Chicago Proton Center including a custom phantom designedto assess the spatial resolution, a phantom to assess the measurement ofrelative stopping power, and a dosimetry phantom. Some images, performance, anddosimetry results from those phantom scans are presented together with adescription of the instrument, the data acquisition system, and the calibrationmethods.

In vivo spin spin relaxation time (T2) heterogeneity of hyperpolarized [13C,15N2]urea in the rat kidney was investigated. Selective quenching of the vascular hyperpolarized 13C signal with a macromolecular relaxation agent revealed that a long-T2 component of the [13C,15N2]urea signal originated from the renal extravascular space, thus allowing the vascular and renal filtrate contrast agent pools of the [13C,15N2]urea to be distinguished via multi-exponential analysis. The T2 response to induced diuresis and antidiuresis was performed with two imaging agents: hyperpolarized [13C,15N2]urea and a control agent hyperpolarized bis-1,1-(hydroxymethyl)-1-13C-cyclopropane-2H8. Large T2 increases in the inner-medullar and papilla were observed with the former agent and not the latter during antidiuresis. Therefore, [13C,15N2]urea relaxometry is sensitive to two steps of the renal urea handling process: glomerular filtration and the inner-medullary urea transporter (UT)-A1 and UT-A3 mediated urea concentrating process. Simple motion correction and subspace denoising algorithms are presented to aid in the multi exponential data analysis. Furthermore, a T2-edited, ultra long echo time sequence was developed for sub-2 mm3 resolution 3D encoding of urea by exploiting relaxation differences in the vascular and filtrate pools.

This study explores the hypothesis that Magnetic Resonance Imaging (MRI) can image the process of electrolysis by detecting pH fronts. The study has relevance to real time control of cell ablation with electrolysis. To investigate the hypothesis we compare the following MR imaging sequences: T1 weighted, T2 weighted and Proton Density (PD), with optical images acquired using pH-sensitive dyes embedded in a physiological saline agar solution phantom treated with electrolysis and discrete measurements with a pH microprobe. We further demonstrate the biological relevance of our work using a bacterial E. Coli model, grown on the phantom. The results demonstrate the ability of MRI to image electrolysis produced pH changes in a physiological saline phantom and show that these changes correlate with cell death in the E. Coli model grown on the phantom. The results are promising and invite further experimental research.

Electron tomography usually suffers from so-called “missing wedge” artifacts caused by limited tilt angle range. An equally sloped tomography (EST) acquisition scheme (which should be called the linogram sampling scheme) was recently applied to achieve 2.4-angstrom resolution. On the other hand, a compressive sensing inspired reconstruction algorithm, known as adaptive dictionary based statistical iterative reconstruction (ADSIR), has been reported for X-ray computed tomography. In this paper, we evaluate the EST, ADSIR, and an ordered-subset simultaneous algebraic reconstruction technique (OS-SART), and compare the ES and equally angled (EA) data acquisition modes. Our results show that OS-SART is comparable to EST, and the ADSIR outperforms EST and OS-SART. Furthermore, the equally sloped projection data acquisition mode has no advantage over the conventional equally angled mode in this context.

There is a growing need for biolabels that can be used in both optical and electron microscopies, are non-cytotoxic, and do not photobleach. Such biolabels could enable targeted nanoscale imaging of sub-cellular structures, and help to establish correlations between conjugation-delivered biomolecules and function. Here we demonstrate a sub-cellular multi-modal imaging methodology that enables localization of inert particulate probes, consisting of nanodiamonds having fluorescent nitrogen-vacancy centers. These are functionalized to target specific structures, and are observable by both optical and electron microscopies. Nanodiamonds targeted to the nuclear pore complex are rapidly localized in electron-microscopy diffraction mode to enable "zooming-in" to regions of interest for detailed structural investigations. Optical microscopies reveal nanodiamonds for in-vitro tracking or uptake-confirmation. The approach is general, works down to the single nanodiamond level, and can leverage the unique capabilities of nanodiamonds, such as biocompatibility, sensitive magnetometry, and gene and drug delivery.