Reference 3 T MRI parameters of the normal human eye

Physica Medica 47 (2018) 50–57

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Technical note

Reference 3 T MRI parameters of the normal human eye

T

Laura Fanea Department of Radiology, Cluj County Emergency Hospital, 3-5 Clinicilor Street, 400006 Cluj-Napoca, Romania

A R T I C L E I N F O

A B S T R A C T

Keywords: Ophthalmology Retina/choroid complex Retinal layer thickness MRI Relaxometry Geometrico-physicochemical ocular physioanatomy

The purpose of this study is to establish magnetic resonance imaging (MRI) standard normative reference quantitative markers for future possible diagnosis in Ophthalmology based on relaxation times (T1 and T2) and retina/choroid complex (RCC) layer thickness values measured in vivo in normal human eyes. This research followed the tenets of the Declaration of Helsinki and was approved by the local Ethical Committee. 15 healthy subjects volunteered to undergo MRI of both eyes. 3 T MRI was performed using a circular surface detector coil with a 15 min acquisition protocol for each eye. The most important normal human eye structures were visualized and characterized geometrico-physicochemically by the 35 MRI standard normative reference markers (20 RCC thicknesses, 8 T1 and 7 T2) calculated. Future possible pathology management could be based on the relative-to-normal differences between the standard normative reference MRI markers calculated in this study and the corresponding MRI markers calculated in the future in disease-suspected eyes. In conclusion, this research demonstrates that ocular MRI at 3 T, performed without contrast agents, brings useful additional multiparametric quantitative information for future possible automated medical diagnosis, staging and evaluation of ocular disease mechanisms.

1. Introduction Most of the major health and economic issues with profound socioeconomic consequences worldwide caused by blindness and visual impairment can be avoided, prevented or treated if appropriate programs are to be implemented [34]. Such programs could potentially focus on the implementation of MRI techniques in Ophthalmology [20] due to lower energy deposition in the tissue imaged [19]; [23], no requirement for a transparent light path through the eye during image acquisition [5,30], and deep tissue penetration, allowing for the visualization of both superficial and internal pathophysiology with a wide ranging coverage of physicochemical properties [5]. MRI techniques offer excellent anatomical visualization of various eye structures, in addition to the quantitative information of structural and geometrico-physicochemical properties of the eye in a slice-by-slice manner in a scan time of only a few minutes [11]. To date, this information consists of: detailed eye anatomy, including 3 layers in the RCC region with signal uniformity over the posterior eye segment [32,33], geometrical characterization of the full 3D retinal shape [2], measurements of thicknesses of the layers visualized in the RCC region [32,33], ocular volumes, and sphericity [17,29] along with physicochemical properties of the eye through estimated parameters characterizing: diffusion [16], blood flow ([24,28], and T1 [12,22,26], and T2 [12,22] relaxometry.

This study moves ocular MRI one step closer to routine clinical implementation in Ophthalmology and proposes a strategy for ocular MRI at 3 T based on the design of a clinically feasible imaging acquisition protocol, with MRI signal uniformity extended over the entire eye volume and the extraction of statistical quantitative information from as many ocular structures as possible. The main eye structures visualized in this study were: the lens, aqueous humor, iris, cornea, ciliary body, vitreous humor, RCC, sclera, optic nerve, and also 3 layers in the RCC region. The normal human eye was analyzed geometrico-physicochemically through 35 MRI parameters: 20 thicknesses (geometrical) and 15 relaxation times (physicochemical). Physicochemical analysis of ocular physiology was performed through 15 parameters, representing the 8 and 7 mean absolute and relative-to-water T1 and T2 measurements in the following normal human eye structures: cornea, lens, ciliary body, aqueous humor, vitreous humor, RCC, sclera (exception: T2), and optic nerve. Human eye physio-anatomy was assessed geometrically through 20 mean absolute and relative-to-eye axis length thicknesses of the internal (L1), medial (L2), external (L3), and overall (O) layers visualized, measured in five different regions along the length of the RCC: labelled R1 to R5. 2. Materials and methods This research followed the tenets of the Declaration of Helsinki. All

E-mail address: [email protected]. https://doi.org/10.1016/j.ejmp.2018.02.007 Received 15 June 2017; Received in revised form 30 November 2017; Accepted 9 February 2018 Available online 24 February 2018 1120-1797/ © 2018 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.

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Fig. 1. RCC thickness measurements. A slice from a representative eye image used for the retinal thickness measurements is shown in (a). The eye section was divided into five regions (R1–5) as described in the Methods section. R1 to R5 are defined by the short thick straight white lines superimposed, from the vitreous humor to sclera, on the end of each of the five eye axes in Fig. 1(a). Only two RCC layers were visualized in R1 of the eye presented in Fig. 1(a). A close up view of the retina in region R4 presenting the three RCC layers: (L1–3), and the line profile through the retinal layers in R4, with Gaussian fit to the top exterior lines are presented in (b) and (c). The method for determining the thickness of the central layer is described in the text.

using: MRIcro (www.cabiatl.com/mricro/mricro), ImageJ (www. imagej.nih.gov/ij), MIPAV (www.mipav.cit.nih.gov), and in-house codes developed in Matlab (The Mathworks, USA). Pixel-wise T1 and region of interest (ROI) based T2 mapping was performed using the DESPOT1 and DESPOT2 methods, respectively [7]. The automatic registration methods in MIPAV were tested on five selected pairs of MRI images used for T1 mapping. The T1 values measured using these registration methods were not significantly different than those measured using the non-registered images and, therefore image registration was not performed for T1 mapping. The two sets of MRI images used for T2 mapping were first registered using the B-spline automatic registration 2D/3D tool in MIPAV, with “Normalised Mutual Information” set as the similarity metric. Stronger susceptibility effects were present on the MRI images used to build the T2 maps. For this reason, the quality of the T2 maps was lower than that of the T1 maps and pixel-wise T2 mapping was not possible. A segmented approach was, therefore, necessary for the T2 mapping. ROIs were selected on the non-artefacted regions of the images used for T2 mapping and the mean values of the signal intensity measured in each of these ROIs was then fitted using the DESPOT2 method to obtain the T2 value corresponding to each anatomical region. Sclera was not visualized on the images used to obtain the T2 maps and, therefore, it was not possible to measure the T2 values in the sclera. The pixel values in the resultant parameter maps represent the T1 and T2 values, in ms, at each pixel location. The mean relaxation time values were then calculated in each ocular structure, based on ROI measurements [22,6]. Both absolute and relative-to-water relaxation time values were determined: the latter represent the percentage difference of the relaxation times in a given ocular structure relative to the corresponding relaxation time of the water. An image slice corresponding to the largest lens diameter and the best visualization of the eye structures, usually the central eye section, was selected for the thickness measurements. In this image, the eye was divided in distinct regions using two axes: one placed along the long diameter of the eye and the second axis placed perpendicular to the first one and crossing through the centre of the lens, effectively dividing the eye into four regions. In the lower left and right areas, two other axes were drawn at 45° and 22.5° relative to the second axis. These four axes thus divided the RCC into five different regions. This procedure is illustrated in Fig. 1(a). The RCC layer thicknesses were measured in these five regions as follows: for each region, RCC was divided in three layers: internal, medial, and external (Fig. 1b). A signal intensity curve with

scanning was performed with informed subject consent and approval from the local Ethical Committee. MRI images of both normal eyes of 15 study subjects were acquired using a 3 T MRI system (Achieva, Philips, the Netherlands) and a single loop surface detector coil (diameter 10 cm). Subjects with no personal history of eye disease were included in this study. From the total number of 30 eyes scanned, only 24 and 25 eyes were selected for relaxometry and RCC thickness measurements, respectively, due to magnetic susceptibility and/or motion/blinking artefacts. 8 T1 and 7 T2 measurements were performed in the following ocular structures: cornea, aqueous humor, ciliary body, lens, vitreous humor, RCC, sclera (exception: T2). The 20 RCC markers correspond to the four: O, L1, L2, and L3 RCC thickness measurements in each of the five: R1 to R5 RCC region defined (Fig. 1). A test tube filled with distilled water was attached to the surface coil and placed above the scanned eye, for use in the standardization of T1 and T2 values. The subjects were asked to keep their eyes closed and as relaxed as possible during image acquisition. The MRI acquisition protocol was developed on appropriate phantoms, and aimed for an optimal compromise between speed of acquisition and image quality. MRI images with spatial resolutions ranging between 0.30 × 0.30 × 1.60 mm3 and 0.45 × 0.45 × 2.00 mm3 were acquired in 57 to 128 s per eye, using several variants of 3D spoiled gradient recalled echo (SPGR) pulse sequences (Table 1). T1 and T2 maps were obtained using the driven-equilibrium techniques of DESPOT1 and DESPOT2, respectively, which have shown clinical utility in the brain and several other body areas [7]. Relaxometry and RCC layer thickness measurements were performed Table 1 Details of the MRI protocols. The MRI images in this study were acquired using the 3D SPGR pulse sequence and three imaging protocols: IP1 to IP3. The values of the repetition time (TR), echo time (TE), flip angle (FA), and spatio-temporal resolution for the ocular MRI images acquired for each IP are given below. The RCC and RCC layer thickness measurements were performed on the MRI images acquired using IP1. The T1 and T2 maps were generated using IP2 and IP3, respectively. Imaging protocols: IP

IP1 IP2 IP3

Image parameters TR

TE

FA

Spatiotemporal resolution

ms

ms

°

mm × mm × mm × s

13.0 5.4 8.3

3.3 2.8 4.1

5,10 2,5,15 50,60

0.30 × 0.30 × 1.60 × 128 0.45 × 0.45 × 2.00 × 57 0.45 × 0.45 × 2.00 × 90

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retinal blood supply (L3), the cellular layers in the retina, retinal pigment epithelium (RPE) and choroid (L2), and to the choroidal blood supply (L1). The histological retinal, RPE, and choroidal layer thickness measurements of 0.44 mm [6], also confirm that the 0.58 ± 0.16 mm MRI L2 layer thickness averaged over R1 to R5 corresponds to the cellular layers in the retina, RPE, and choroid. Three different research groups measured in vivo the absolute mean T1 and T2 values of three normal human eye structures (vitreous humor, lens, and aqueous humor) at magnetic field strengths of 1, 1.5, and 7 T [12,22,26]. The current study extends this to 3 T scanners, while 8 ocular structures were successfully mapped. Only in the region of the iris and sclera (T2 relaxometry) was it not possible to extract quantitative information, perhaps not surprising considering the small size of the iris and the lower quality of the relaxometry maps compared to the acquired anatomical images. This effect on the relaxometry maps could lead to partial volume effects in these regions, and hence introduce errors into the quantification accuracy. The three physicochemical tissue classes identified in the normal human eye are presented in Table 3. This classification is based on the mean relative-to-water values of the T1/T2 relaxation times: < 50/40% (first T1/T2 category), between 50/40 and 60/70% (second T1/T2 category), and > 60/70% (third T1/T2 category). Aqueous and vitreous humor enter the first T1/T2 category. The optic nerve is situated in the second T1/T2 category. Cornea, lens, and sclera enter the third T1/T2 category, while ciliary body and RCC enter the second/third T1/T2 category. The first T1/T2 category has physicochemical properties more similar to the water, while the third is the most different physicochemically compared to the water. Of all ocular structures, the aqueous and vitreous humor have the closest physicochemical consistency to that of the water, and thus, they both enter the first T1/T2 category. The second T1/T2 category identifies the ocular tissues with a blood supply, but the T1 measurements are more sensitive, being able to detect the blood supply in all ocular structures analyzed in this study: ciliary body, RCC, and the optic nerve. The T2 measurements are only capable to detect the more abundant blood supply in the optic nerve. This is not surprising because the T1 maps have a better quality compared to the corresponding T2 maps and hence the quantitative information extracted will be more sensitive to lower physicochemical changes. The less abundant blood supply present in the ciliary body and RCC could not be identified by the T2 measurements in this study and thus, these two ocular structures enter the third T2 category. The largest physicochemical differences between an ocular structure and the water are given by the compactly dense cellular solid composition of the cornea, lens, and sclera. Consequently, these three ocular structures are situated in the third T1/T2 category. A clearer classification of the ocular structures using a T1T2 index is also proposed in Table 3. According to the T1T2 index classification, the ocular physio-anatomy can be classified physicochemically in three classes: most liquid with T1T2 indexes lower than 2, between liquid and solid (i.e. cellular solid tissues irrigated with blood or other liquids) with T1T2 indexes ranging between 2 and 10, and most solid ocular structures characterized by T1T2 indexes larger than 10. The aqueous and vitreous humor of the normal human eye have a physicochemical composition similar to the water [1] and the T1 and T2 values measured in these regions should, therefore, be close to 3000 ms [15]. A very general comparison can be made between the T1 and T2 values measured in the aqueous and vitreous humor measured at different magnetic field strengths, based on this physicochemical similarity with the water. Overall, the smallest differences between the T1 and T2 values measured in the water and in the aqueous and vitreous humor of the normal human eye are found for the 3 T measurements. The mean T1 values measured in the aqueous/vitreous humor of the normal human eye at 3 T were with 23/19% longer and 56/58% shorter than that of the water. Decreases with up to 20/25% of the T1 over- and 67/69% of the T2 under-estimations in the aqueous/vitreous humor at 1, 1.5, and 7 T (T1) and 1.5 T (T2) were obtained at 3 T. Compared to

three branches (two ascending and one descending) was identified. The central branch was inverted in ascending (Fig. 1c) and the resulting three ascending measurement points were fitted using the Gaussian procedure. The full width at half maximum of the fitted curve branches represented the L1, L2, L3, and their sum represents the O thickness. These measurements were performed in each of the five regions defined for each eye section selected. The duration of the entire offline MRI image post-processing was approximately 1 h per set of images. Mean values of L1, L2, L3, and O thicknesses were calculated in each of the five regions defined in Fig. 1(a) and (b) using ImageJ and inhouse codes in Matlab. Mean T1 and T2 values and their standard deviations were calculated for each eye using MIPAV in the regions of the: standard phantom, cornea, ciliary body, aqueous humor, lens, vitreous humor, RCC, sclera, and the optic nerve on the T1 maps and the same eye structures with exception of the sclera on the T2 maps. For each of the L1, L2, L3, and O layers in each RCC region, the absolute and relative-to-eye axis length mean thicknesses, and for each eye region, the absolute and relative-to-water mean T1 and T2 values and all corresponding standard deviations were calculated using Excel (Microsoft, USA). To optimize accuracy and reliability of the quantitative measurements performed in ROIs as small as three pixels, only measurement values with standard deviations of a maximum of 30% from the mean value were included in mean value statistics. 3. Results A set of MRI images of slices through a representative normal left eye used to create the relaxometry maps, and the maps generated, are presented in Fig. 2. Data from ROI analyses of these parameter maps are presented in Fig. 3, with absolute (lower graph) and relative-to-water (upper graph) mean values and their standard deviations shown. The mean absolute T1 and T2 relaxation times of the normal human eye ranged between 918 and 3870 ms, and 67 and 1334 ms, respectively (Fig. 3, lower graph). The corresponding relative-to-water mean values (Fig. 3, upper graph) ranged between 42 and 66% (T1) and 19 and 84% (T2), respectively. The quantitative T1 and T2 relaxometry measurements were performed in all ocular regions with exception of iris and sclera (for the T2 maps). Absolute and relative-to-eye axis length mean thickness values of L1, L2, L3, and O were measured in the R1 to R5 regions along the RCC length, as defined in Fig. 1 and presented in Fig. 4. All RCC thicknesses increased from the peripheral nasal and temporal sides: R1 and R5 to the centre: R3. The mean absolute/relative-toeye axis length thickness values increased from 0.48 mm/1.9% to 2.2 mm/8.7%. The mean absolute and relative-to-eye axis length thicknesses measured for each layer and their averaged absolute thicknesses over the five regions are presented in Table 2. 4. Discussion MRI has a demonstrated ability to visualize pathophysiology of the eye, even in the presence of artificial and opaque ocular media [11]. It can detect and assess retinal detachments or distinguish between haemorrhage and cellular infiltrates, while also identifying abnormal structures in the eye. The current work demonstrates the additional quantitative information offered by MRI which can be obtained in most eye structures with a scan protocol short enough to make this clinically acceptable and feasible. Three layers were identified in the RCC region, additional to visualization of all main ocular structures. The contrast between the signal intensities in the layers is based on the difference between the MRI physicochemical properties of the layers, demonstrating the water similar composition of the internal and external layers and the protein similar composition of the central layer. The physicochemical properties generating the contrast mechanisms on the MRI images in the three RCC layers very closely correspond physio-anatomically [1,8] to the 52

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Fig. 2. T1 and T2 maps of the normal human eye. A representative T2-weighted (a) and the corresponding T1-weighted image (b) of one eye used to create the T1 maps (c) with the DESPOT1 technique are shown. (d) and (e) present two proton density MRI images used to create (f) the T2 maps with the DESPOT2 technique.

only one human study reporting similar RCC layers. The RCC layers in the human study were successfully resolved using MRI with higher spatial resolution (0.04 versus current: 0.14 mm^3) in four healthy volunteers [32]. The overall RCC thickness was significantly less than that reported herein: 0.71 ± 0.04 mm [32], compared to 1.92 ± 0.41 mm. This could be explained by measurements being made within 2 mm distance from the optic nerve head, whereas the values reported in the current paper represent the mean of measurements made far from the optic nerve head, across the retinal length in the five regions illustrated in Fig. 1. This emphasizes the need to carefully establish the specific measurement location for any longitudinal study or when comparing results from different studies. Partial volume effects and blurring due to long echo train lengths in the 3D SPGR pulse sequence used herein may have contributed to an overestimation of the measured values. Nevertheless, MRI normative values from this easy-to-implement clinical imaging protocol could be used to assess layer thickness changes due to disease affecting the retina. Indeed, the measurement of RCC layer thicknesses offers the potential of a sensitive marker of both layerspecific cellular and vascular changes which could be exploited in longitudinal studies of disease progression or to monitor therapeutic interventions. An exact accuracy assessment of the RCC thickness measurements presented in this study is not possible due to the combined partial

the corresponding T2 value measured at 3 T, a 7% larger value was measured in the vitreous humor at 1 T. Further comparisons with other scanners and field strengths are hampered by several factors such as the use of different MRI protocols and resultant spatial resolutions and relaxometry mapping trade-offs, and also by the post-processing methods used to extract the T1 and T2 values from the raw data or the low numbers of scanned subjects. To eliminate the effects of these factors, Fanea et al. [10,12] used a similar standard phantom and MRI protocol at 1 T compared to that used in the current study. The use of a combined water phantom reference and field strength correction factor can further reduce the differences between the relative-to-water relaxation times calculated at different magnetic field strengths using different MRI systems, making the values calculated generally usable and comparable. The corrected values of the relaxation times will thus only depend on the biophysiology of the structure analyzed. The values calculated and reported in this study can therefore be considered as a reference standard when introducing the field correction factors. Relative-to-eye axis length RCC thicknesses represent another immediately useful quantitative tool which could be implemented in routine clinical environments for ocular disease assessment and management. The mean L1, L2, L3, and O RCC values calculated in this study could be used as MRI standard reference normative values. In the literature, to date, there have been several animal studies [21,27], but 53

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Fig. 3. Relative-to-water (upper graph) and corresponding absolute (lower graph) T1 and T2 mean values for the eye structures evaluated. The error bars correspond to the standard deviation of the measurements in each case. The numbers above the error bars in the graphs represent: the number of total measurements: NT/the number of the selected measurements: NSEL. NT and NSEL for the T1 measurements was 24 with exception of the following measurements: lens (absolute, lower graph) and aqueous and vitreous humor (relative-to-water, upper graph) had NSEL of 22, 15, and 23, respectively.

and sclera-like gel layers. The image acquisition and analysis protocols presented in this study for the RCC evaluations should be used. The DESPOT1 and DESPOT2 techniques can then be applied to the highresolution images visualizing the three RCC layers to calculate the mean T1 and T2 values of each RCC layer. This will allow identification of the T1 and T2 value for each of the five layers in the gel phantom. The fivelayer gel phantoms with the T1 and T2 values of the vitreous humor/L1/ L2/L3/sclera could be developed and built. Each MRI thickness measurement in the five-layer gel phantom can then be compared with the corresponding direct gel phantom layer thickness measurement, and accuracy of each MRI thickness measurement can then be evaluated exactly. If necessary, correction factors can be calculated and used for MRI geometrical RCC thickness measurement optimization. This optimization will then allow a generalization of the MRI RCC thickness measurements presented in this study. This generalization can also bring complementary information to that obtained using different imaging techniques. The imaging protocol presented in the Methods section can be used to perform RCC thickness measurements with theoretical precisions larger than 67%. This precision can be maintained for geometrical measurements performed on 1.6 mm thick MRI eye slices, presenting the largest lens axes and the longest optic nerve. The spectral decomposition method used for the RCC thickness measurements improves the theoretical precision of the RCC thickness measurements [31]. Further improvements of the precision can be obtained in the future for images acquired with decreased slice thicknesses.

volume and susceptibility effects. For example, the most inaccurate thickness measurements are that of the smallest size structures, i.e. that of single in-plane pixel size structures. However, susceptibility effects produced by a single voxel can affect the signal intensity over multiple voxels [3]. In such a situation the theoretical inaccuracy of the linear size measurement generated by the partial volume effect will be completely overcome by that introduced by the susceptibility effect. The geometrical measurements presented in this study, therefore, represent standard MRI markers which cannot be generalized to other imaging techniques or real in vivo thicknesses, at this stage. Generalization of the linear size measurements to the real in vivo situation and any other imaging technique can be obtained using accurate MRI RCC thickness measurements. Exact details on the accuracy of the geometrical measurements in this study can only be obtained through future phantom studies. An example of such a phantom study could involve layer thickness measurements of a five-layer gel phantom with the physicochemical properties (T1 and T2 values) of the three retinal layers visualized and the ocular structures surrounding them: vitreous humor and sclera (Fig. 1). MRI standardization for generalization can be achieved through correlations between each layer thickness measurements using the MRI images and the direct layer thickness measurements. If necessary, correction factors can then be introduced to generalize the standard MRI thicknesses. Such a study could involve development and MRI analyses of 5-layer: vitreous humor-, L1-, L2-, L3-, and sclera-like gel phantoms with the following layer thicknesses: 5 mm for the vitreous humor-, 0.1, 0.3, 0.5, and 1 mm for L1-, L2-, L3-, 54

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Table 3 Physicochemical classification of ocular physio-anatomy. The 8 analyzed ocular structures of the normal human eye can be classified in three tissue classes based on the individual T1 and T2 relative-to-water mean relaxation times or on the T1T2 index. Classification criteria

Individual mean relative-towater relaxation times (%)

Physicochemical details of the ocular physioanatomy

Ocular tissue class and characteristics 1 most liquid

2 between liquid and solid: solid composition irrigated with blood or other liquid(s)

3 most solid

T1 Ocular structure

< 50 aqueous humor vitreous humor < 40 aqueous humor vitreous humor

[50,60] ciliary body RCC optic nerve

> 60 cornea lens sclera

[40,70] optic nerve

> 70 ciliary body RCC cornea lens sclera

<2 aqueous humor: 1.0 vitreous humor: 1.7

[2,10] ciliary body: 2.9 RCC: 5.3 optic nerve: 5.5

> 10 cornea: 27.8 lens: 30.8 sclera: 35.3

T2 Ocular structure

Combined relative-towater mean relaxation times: T1T2 = 2*[(T1^8)/ 2 + T2/2]/ (10^13)

Fig. 4. Mean values of relative-to-eye axis length (upper graph) and absolute (lower graph) retinal layer thicknesses in five different regions along the RCC length in the centre of the eyeball. The error bars correspond to the standard deviation of the measurements in each case. The measurement technique is described in the Methods section. Of the total 25 eyes analyzed geometrically through the 20 RCC thicknesses measured, three layers were visualized in R1/R2/R3/R4/R5 of 14/22/25/18/8 eyes.

T1T2 Ocular structure: T1T2 index

is the most important, then higher magnetic field strengths are needed. If the spatiotemporal resolution offered by lower magnetic field strengths is enough for the eye condition evaluated, then lower magnetic field strengths are more suitable. For example, vitreous humor, lens and aqueous humor can be analyzed using MRI at 1 T. The RCC layers were visualized only on ocular MRI images acquired at 3 T, so more detailed eye analyses involving RCC layer thicknesses can be performed only at 3 T. More semi-automatic and automatic registration, image/pattern recognition and eye fixation methods can also be analyzed in the future to reduce the standard deviations of the individual values of the MRI parameters calculated and improve the differentiation process. Further software developments for the automatic user defined ROIs will move this study closer to its implementation in routine clinical imaging. For example, software developments should allow the automatic selection of a rectangular ROI through two simple user clicks: one with the cursor pointed in the left upper corner and the other one with the cursor pointed in the right bottom corner of the rectangle. Then, the software will automatically select all pixels in that rectangular region and

Increased spatial resolution will bring more quantitative information in smaller eye structures such as the iris, while also allowing for more accurate, precise, and reliable quantitative measurements free from potential partial volume errors. More improvements of accuracy and precision of the quantitative measurements can be achieved in the future through hardware and/or software developments of the MRI techniques. These include development of: signal-to-noise ratio optimized detector coils specifically designed to fit the eye morphology [25], image acquisition protocols [18,4] using pulse sequences and image processing software allowing faster acquisitions of images with decreased field-of-views and hence increased spatiotemporal resolution. Improved spatiotemporal resolution can also be achieved at higher magnetic field strengths, but, unfortunately the susceptibility effects at higher magnetic field strengths become stronger [3]. A compromise should, therefore, be achieved between the magnetic field strength chosen for each specific investigation. If the spatiotemporal resolution

Table 2 RCC layer thicknesses. The mean values of the absolute and relative-to-eye axis length L1, L2, L3, and O RCC thickness values and their standard deviations are given for each region: R1 to R5 together with the corresponding values averaged over all regions (AOR). Layer

L1

Value

Absolute Relative L2 Absolute Relative L3 Absolute Relative O Absolute Relative Number of measurements

Mean (mm)/SD (mm) R1

R2

R3

R4

R5

AOR

0.51/0.15 1.90/0.70 0.54/0.08 2.00/0.50 0.68/0.13 2.60/0.80 1.73/0.18 6.50/1.60 14

0.63/0.22 2.50/0.90 0.61/0.21 2.40/0.90 0.80/0.23 3.20/0.90 2.05/0.43 8.10/1.80 22

0.71/0.20 2.80/0.80 0.65/0.17 2.60/0.70 0.84/0.23 3.30/0.90 2.20/0.43 8.70/1.70 25

0.62/0.12 2.50/0.50 0.54/0.10 2.10/0.40 0.76/0.25 3.00/1.00 1.92/0.36 7.60/1.50 18

0.53/0.18 2.10/0.70 0.48/0.12 1.90/0.50 0.68/0.16 2.70/0.60 1.69/0.25 6.70/1.10 8

0.63/0.19 2.47/0.77 0.58/0.16 2.29/0.68 0.77/0.22 3.04/0.92 1.92/0.41 7.79/1.79 87

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5. Conclusions

consider for signal intensity measurements (for example for the T1 and T2 measurements) only pixels with signal intensity differences lower than 30%. Another option could involve the automatic calculation of the signal intensity mean value in the ROI defined. Pixel selection for the measurements can automatically be made for signal intensity differences up to 15%. Precision and reliability of each method should be evaluated statistically to optimize each type of MRI measurement. The time taken to perform the offline postprocessing of the MRI images could be shortened by implementing on-scanner software routines and hence this does not represent an impediment for realizing a more refined and complex clinical protocol. This implementation should offer a direct and rapid, push-button based quantitative analysis of the pathophysiology to ophthalmologists and medical specialists. These future software developments should, for example, allow eye disease-suspected subjects to be evaluated automatically using the image acquisition and analysis methods presented in this study. Each MRI parameter in the disease-suspected eye should be calculated and evaluated automatically against the corresponding parameters in the standard MRI eye chart presented in this study. The eye condition could, then be assessed based on the relative-to-normal MRI parameters calculated against the corresponding standard normal MRI marker for each evaluated ocular structure in the disease-suspected eye. Development of a detailed disease scoring scale could also be performed using for example, the artificial neural network [13] or support vector machine [14] methods. This scale could then be implemented in the custom software developed for the future automatic selection and analysis of any ocular structure visualized on the MRI images. Such software developments could allow the person evaluating the image to specify easily and automatically all necessary details. For example, beginning and end points of the ROI could be defined by simply clicking on these two points on the MRI image investigated. The shape of the ROI could be selected from a drop-down list. The calculation method could also be selected from a drop-down list, for example: spectral decomposition (for RCC or other thickness or relevant linear measurements such as: iris, cornea or sclera), DESPOT1 (for T1 measurements) and DESPOT2 (for T2 measurements). The software, should also allow the automatic calculation of the three layers and the overall RCC thicknesses in any specific region defined, using the spectral decomposition segmentation method developed and described in detail in the Methods section and Fig. 1. Mean and normalized-to-water mean T1 and T2 values should also be automatically displayed for each ROI automatically defined by the user. Each automatically calculated parameter value could be displayed together with a corresponding relative-to-normal value calculated against the corresponding normative standard MRI markers included in the eye chart of the normal human eye generated in this study. Results in this study combined with future studies focusing on: RCC thickness and relaxometry analyses on patients affected by eye diseases, ocular flow measurements on both normal and disease-affected eyes using MRI [9] and analyses of the MRI parameters of the normal and disease-affected eyes using the artificial neural network [13] or support vector machine [14] methods, can be used for the future development of complex eye disease scoring scales. These scoring scales should also be implemented in the software developed for the automatic disease management. The corresponding disease scoring stage for each calculated MRI parameter should be another display capability of the software developed for the future push-button diagnosis using MRI. The eye is a superficial organ containing many fine structures with an extremely diversified physicochemistry ranging from liquid to solid [1]. For this reason, this pilot ocular MRI study can also be extended to any other organ and medical imaging technique and, it, therefore, demonstrates that implementation of automated medical imaging in routine clinical imaging is possible if further developments are performed.

Geometrico-physicochemical classification of the normal human eye physio-anatomy was achieved through the 35 MRI standard reference quantitative markers calculated. 14/22/25/18/8 measurements were averaged to calculate a total number of 20 mean and 20 relative-to-eye axis mean O, L1, L2 and L3 RCC thicknesses in each of the five equidistant regions defined along the RCC length: R1/R2/R3/R4/R5. Each of the 20 mean and/or 20 relative-to-eye axis mean RCC thicknesses characterizes the geometry of the normal human eye. The physicochemical properties of 8 and 7 normal human eye structures is quantitatively assessed through the mean and the normalized-to-water mean T1 and T2 values averaged from: 24/24&0 and 24/11&0 (phantom); all 24 and 7/4&7 (cornea); 24/24&15, 23/9&13 (aqueous humor), all 24 and 19/3&18 (ciliary body); 24/22&24 and 10/3&8 (lens); 24/24&23 and 24/23&10 (vitreous humor); all 24 and 8/4&8 (RCC); all 24 and 0/ 0&0 (sclera); all 24 and 14/3&10 (optic nerve) total/used absolute & relative-to-water T1 and T2 measurements. Each of the 35: 20 RCC thicknesses, 8 T1 and 7 T2 standard reference MRI markers in the eye chart presented in this study can be used in the future to perform diagnoses of disease-suspected eyes evaluated using the 15-min MRI image acquisition and processing protocols presented in this study. Future software and hardware developments will contribute to the implementation of the multiparametric information presented in this study in the routinely used automated clinical MRI. Acknowledgements POSDRU/89/1.5/S/60189 grant support from EC and Professors’ Andrew Fagan and Jim Meaney careful revision of the paper and assistance with access to the scanning facilities and MRI scanning through institutional funding to the Centre for Advanced Medical Imaging (CAMI), St. James’s Hospital Dublin from the Health Research Board, Ireland are acknowledged. This work is dedicated to its approximately 50 volunteer contributors. References [1] Alm A. Chapter 6: ocular circulation. In: Hart WM, editor. Adler’s physiology of the eye. ninth ed.St. Louis, MO: Mosby Inc; 1992. p. 198–222. [2] Beenakker JW, Shamonin DP, Webb AG, et al. Automated retinal topographic maps measured with magnetic resonance imaging. Invest Ophthalmol Vis Sci 2015;56:1033–9. [3] Brown RW, Cheng YCN, Haacke EM, et al. Magnetic resonance imaging: physical principles and sequence design. Second Edition Hoboken, New Jersey, USA: John Wiley & Sons Inc; 2014. [4] Chang C-H, Yu X, Ji JX. Compressed sensing MRI reconstruction from 3D multichannel data using GPUs. Magn Reson Med 2017;78:2266–74. [5] Ciardella AP, Borodoker N, Costa DL, et al. Imaging the Posterior Segment in Uveitis. Ophthalmol Clin North Am 2002;15:281–96. [6] Curcio CA, Messinger JD, Sloan KR, et al. Human chorioretinal layer thicknesses measured in macula-wide, high-resolution histologic sections. Invest Ophthalmol Vis Sci 2011;52:3943–54. [7] Deoni SC, Peters TM, Rutt BK. High-resolution T1 and T2 mapping of the brain in a clinically acceptable time with DESPOT1 and DESPOT2. Magn Reson Med 2005;53:237–41. [8] Dudea SM. Ultrasonography of the eye and orbit. Med Ultrasonogr 2011;13:171–4. [9] Fanea L. Theoretical mathematical model of noninvasive flow quantification for future implementation in routine clinical MRI. Int Res J Med Med Sci 2017;5:58–63. [10] Fanea L, Sfrangeu SA. Relaxation times mapping using magnetic resonance imaging. Roman Rep Phys 2011;63:456–64. [11] Fanea L, Fagan AJ. Magnetic resonance imaging in ophthamology. Mol Vision 2012;18:2538–60. [12] Fanea L, Nicoara S, Bodea SV, et al. Human eye magnetic resonance imaging relaxometry in diabetic retinopathy. Roman Rep Phys 2014;66:1029–37. [13] Fei Y, Hu J, Li W-Q, et al. Artificial neural networks predict the incidence of portosplenomesenteric venous thrombosis in patients with acute pancreatitis. J Thromb Haemost 2017;15:439–45. [14] Gaonkar B, Shinohara RT, Davatzikos C. Interpreting support vector machine models for multivariate group wise analysis in neuroimaging. Med Image Anal 2015;24:190–204. [15] Gene Sullvian S, Stern A, Rosent JS. NMR water-proton spin-lattice relaxation time of human red blood cells and red blood cell suspensions. FEBS Lett

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