Performance evaluation of medical LCD displays using 3D channelized Hotelling observers

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1 Performance evaluation of medical LCD displays using 3D channelized Hotelling observers Ljiljana Platiša a,cédric Marchessoux b, Bart Goossens a and Wilfried Philips a a Ghent University, TELIN-IPI-IBBT, St-Pietersnieuwstraat 41, B-9000 Ghent, Belgium; b Barco N.V., Healthcare Division, President Kennedypark 35, 8500 Kortrijk, Belgium ABSTRACT High performance of the radiologists in the task of image lesion detection is crucial for successful medical practice. One relevant factor in clinical image reading is the quality of the medical display. With the current trends of stack-mode liquid crystal displays (LCDs), the slow temporal response of the display plays a significant role in image quality assurance. In this paper, we report on the experimental study performed to evaluate the quality of a novel LCD with advanced temporal response compensation, and compare it to an existing state-of-the-art display of the same category but with no temporal response compensation. The data in the study comprise clinical digital tomosynthesis images of the breast with added simulated mass lesions. The detectability for the two displays is estimated using the recent multi-slice channelized Hotelling observer (mscho) model which is especially designed for multi-slice image data. Our results suggest that the novel LCD allows higher detectability than the existing one. Moreover, the mscho results are used to advise on the parameters for the follow up image reading study with real medical doctors as observers. Finally, the main findings of the mscho study were confirmed by a human reader study (details to be published in a separate paper). Keywords: Model Observers, Observer Performance Evaluation, Image Display, Temporal Response 1. INTRODUCTION Lately, three-dimensional (3D) digital breast tomosynthesis (DBT), recently also approved by the FDA, is gradually taking over the already conventional 2D digital breast mammography (DBM) imaging. As Park et al. 1 point out, while mammography is an effective imaging tool for detecting breast cancer at an early stage, the overlap of tissues depicted on mammograms may create significant obstacles to the detection and diagnosis of abnormalities. In contrast to DBM where the absorption of x-rays from a stationary tube is used to create a 2D projected image, in DBT the x-ray source is moved about the breast to acquire a series of projection view (PV) mammograms of the breast. Subsequently, the PV data is used to reconstruct the tomographic breast volume. The process of DBT data acquisition suggests promising potential for avoiding the problem of tissue overlap in mammographic images produced by tissue overlap. Accordingly, the DBT can be expected to improve detection of the breast lesions, not only masses but also subtle microcalcification clusters. The results of Anderson et al., 2 for example, indicate that the cancer visibility on DBT is superior to DBM suggesting that DBT may have a higher sensitivity for breast cancer detection. Similarly, the simulation results of Ma et al. 3 show consistently that 3D DBT allows detection of smaller tumors and smaller microcalcifications than the 2D DBM images. With the current growing evidence of the practical diagnostic benefits of DBT, it is not surprising that this technology has drawn much attention from the medical imaging community, either in the domain of image acquisition and reconstruction or in the field of image data presentation and finally image interpretation. In this work, we focus on the aspect of image presentation. Commonly, the radiologists inspect the thin tomographic slice images using a stack-mode liquid crystal displays (LCD) where slices of a reconstructed volume are shown sequentially, at an arbitrary browsing rate. Importantly, despite the fact that the quality of LCDs has significantly improved over the last few years, slow response times of the liquid crystal cells remain a limiting factor for signal detection performance of stack-mode reading at high browsing speeds. 4 6 As indicated by the human reader study by Badano, 7 slow response of liquid crystal display devices reduces the detection performance when using high browsing rates to inspect volumetric images in stack-mode presentation. Send correspondence to Ljiljana Platiša, Ljiljana.Platisa@Telin.UGent.be Medical Imaging 2011: Image Perception, Observer Performance, and Technology Assessment, edited by David J. Manning, Craig K. Abbey, Proc. of SPIE Vol. 7966, 79660T 2011 SPIE CCC code: /11/$18 doi: / Proc. of SPIE Vol T-1

2 We report here on the experimental study set to evaluate the quality of a novel medical image display optimized for DBT (Barco MDMG 5221 display optimized for DBT) by comparing it to the existing state-of-theart full field digital mammography (FFDM) display. One of the major advancements of the new display device is the temporal response compensation. This is aimed to diminish the negative influence of the slow temporal response of the current LCD technology on signal detectability in the fast changing displayed image scenes like those in the stack-mode reading at high browsing speeds. Given the fact that the main purpose of a medical display is to assist medical experts (radiologists) in the diagnostic procedure, we choose to perform a task-based image quality assessment. 8 To that end, the criterion for display quality assessment in our study is the level of signal (here, breast lesion) detectability with the considered medical display. For estimating the detectability, we use the channelized Hotelling observer 9 designed specifically for multi-slice images named the multi-slice CHO, mscho. 10 The results reported in the recent study by Platisa et al. 6 suggest that this CHO model is able to successfully capture the degradation in the detection performance caused by the slow temporal response of the display. The two displays are compared for clinical digital tomosynthesis images of the breast with added simulated mass lesions. To simulate the effects of the temporal response of the display, we use the model proposed by Wang et al., 11 the details are explained later. Our experimental results, the mscho estimates of the lesion detectability, suggest that the novel LCD allows higher detection performance than the existing one. This holds for the high browsing speeds of approximately 50 fps but for the lower ones as well, e.g. 25 fps or even 10 fps. Moreover, the obtained mscho results were successfully used to advise on the parameters for the follow up image reading study with real medical doctors as observers. In the following section, we set out the experimental setup used in assessing the detection performance for the two displays. The section starts with the image data description, goes through the details of the temporal display simulation model, and ends with an outline of the mscho model and the corresponding performance metrics. Then, in section 3, experimental results are presented and discussed. Finally, the conclusions of the study are drawn in section METHODS 2.1 Multi-slice images We use a total of 6000 multi-slice images of pixel size, where N = 41 is the number of slices and M = 64 denotes the height and width of each image slice. The background images are extracted from clinical digital breast tomosynthesis (DBT) images. The pixel values are coded in 10 bits. Half of the backgrounds are used as signal-absent images, see example in Fig. 1 (a). To generate signalpresent images, we add a synthesized volumetric mass (3D signal) in the central three slices of the remaining 3000 backgrounds. The 3D signals are generated using the data set of 2D lesions extracted from real clinical digital mammography images, provided by Dr. Elizabeth Krupinski from The University of Arizona. First, a 2D lesion is warped using mathematical morphology operations to get a 3D shape. Then, the resulting volume is interpolated between the slices in order to mimic the X-ray interaction (absorption). Finally, the lesion is smoothed to avoid any sharp gradient at the borders and it is normalized. An example 2D lesion and the central slice of its corresponding 3D lesion are depicted in Fig. 1 (b)-(d). The synthesized 3D mass breast lesion of a given density is inserted in the reconstructed background volume. Fig. 1 (e) shows central slice of one signal-present image in the database. The images described here, signal-present or signal-absent ones, are referred to as static or pre-lcd images as they do not take into account the temporal response of the display. The details of the human reader study will be reported in a separate publication. More recently, some of the authors of this paper developed an improved method for generation of the 3D mass breast lesions where the lesion is inserted directly in the projection images. 12 Proc. of SPIE Vol T-2

3 (a) (b) (c) (d) (e) Figure 1. Image data used in the study (see text): (a) central slice of an example signal-absent image, (b) an example 2D lesion extracted from a real clinical DBM image, (c) central slice of the synthesized volumetric mass corresponding to (b) (64 64 pixels), (d) enlarged mass area from (c) (11 11 pixels), (e) central slice of an example signal-present image. 2.2 Simulation of temporal LCD effects As mentioned earlier, a pre-lcd image stack is comprised of N image slices or frames. Note that the terms slice and frame are used interchangeably throughout the paper. When a pre-lcd image is shown on a display we refer to it as a post-lcd image. In the following, before explaining the details of the temporal response model of the display, we introduce the terms and notations used in this paper to refer to the post-lcd image as opposed to the pre-lcd one. As is common, the frame rate or browsing rate, f browse, determines the number of frames that is displayed in 1 second. Hence, each frame is displayed during the time interval of 1/f browse referred to as the frame time, T browse.usingf refresh to denote the display refresh rate, and T refresh =1/f refresh to denote the time between the two consecutive display refresh cycles, we can write the following T browse = f refresh f browse T refresh. (1) Here, we assume the frame time is constant over all frames in an image and thus each frame is displayed exactly T browse /T refresh times during the frame time. Further on, we will use the term frame repeat, FR,torefertothe number of consecutive repetitions of a given frame in post-lcd images, that is the number of frames per frame time, FR = T browse T refresh = f refresh f browse. (2) For example, let us consider a display with f refresh =50Hz(T refresh = 20 ms), when the browsing rate is f browse = 25 frames per second (fps). Given Eq.(1), the frame time is T browse =(50/25)T refresh =2T refresh =40 ms. And, in line with Eq.(2), each slice is displayed exactly FR = T browse /T refresh = 2 times during the frame time. Next, we explain the difference between the pixel values in pre- and post-lcd image. Let us denote g(x, y, n) the gray level of the image pixel at position (x, y) within slice n, wherex =1,..., M, y =1,..., M and n =1,..., N. In our images, the gray levels vary in the range from 0 to g max = Similarly, we use L(x, y, n) todenotethe level of luminance corresponding to g(x, y, n). Here, the conversion from gray level to luminance and vice versa is performed using the information of the luminance curve of the display, as illustrated in Fig. 3 (a). To simplify the notations, in the remaining of this section we consider one pixel from a slice at position (x, y) and drop the corresponding position indices. Thus, g(n) =g(x, y, n) andl(n) =L(x, y, n). In general, we assume that pixel values in the image sequence change from slice to slice, from g(n) orl(n) in the current slice to g(n +1) or L(n + 1) in the subsequent slice. The details are explained next. In the case of static (pre-lcd) images, we can assume that T browse is long enough to consider that L(n) const. throughout the frame time. Ideally, when the temporal response of the display would be instant (infinitely fast), the same assumption would hold for the post-lcd images. However, due to temporal response of the Proc. of SPIE Vol T-3

4 Figure 2. Pixel luminance change at different frames. L(n 1) is the achieved luminance of pixel (x, y) attheend of frame n 1, corresponding to a gray level g(n 1). At frames n, n +1 and n + 2 the target luminance level is L 0(n) =L 0(n +1) = L 0(n +2) (gray level g 0(n) =g 0(n +1) = g 0(n + 2)). Because of slow temporal response, the achieved luminance at the end of frame n, L(n) =L(n 1) + ΔL, is lower than the target one: L(n) <L 0(n). Further on, L(n) is the starting luminance for frame n + 1. Likewise, L(n + 1) and L(n + 2) are the achieved luminances at the end of frames n +1andn + 2, respectively. LCD, the transition of luminance from one frame to another in not instantaneous. See Fig. 2 for the following explanation. Let L(n) denote the luminance level of a given pixel achieved at the end of the reference frame n. In the new frame n + 1, the target luminance level of the same pixel is L 0 (n + 1), where for example L 0 (n +1)>L(n). But, because of slow temporal response of the display, the actual achieved luminance level in the new frame will be L(n +1), wherel(n +1) L 0 (n + 1). The matrix in Fig. 3 (b) depicts the reorientation times of the liquid crystal directors in our study. Note that for a large number of the luminance transition L, that is the corresponding gray level transitions g, the reorientation time is larger than 0.02 sec (T refresh ). Depending on the browsing speed, it is possible that the target luminance level L 0 (n + 1) could not be achieved during the frame time, T browse. As will be shown later in the experimental results in section 3, this can play an important role for signal detection performance, especially at higher browsing rates. Now, we proceed to explain the details of the specific temporal response model used in our simulations. The temporal effects of the medical LCD when viewing an image in the stack-browsing mode are simulated using a software platform named MEVIC (MEdical Virtual Imaging Chain). 13 In particular, we simulate two medical displays: MDMG-5221, optimized for DBT, and the Mammo display FDA approved for FFDM. All simulation parameters, except those of the temporal response, are the same for the two displays. A novel motion compensation feature is included only with the DBT display, hereafter referred to as the motion compensated display, mclcd. The FFDM display will be referred to as the regular LCD, reglcd. sharpness. Within MEVIC, the temporal response of the display is simulated using the model of Wang et al. 11 They use the small angle approximation to derive the analytical correlation between the director reorientation time and Proc. of SPIE Vol T-4

5 Pixel luminance level [log 10 (cd)/m2] Pixel gray level Fall Rise Figure 3. Parameters of the displays in the study. Left: Luminance response curve of the display. Right: Matrix of the liquid crystal director reorientation times, τ 0 [sec]. These values correspond to the LCD display with no compensation for the slow temporal response. Table 1. Image simulation parameters Database Browsing rate Frame repeat Frame time name f browse [fps] FR T browse 0 DBstatic DB T refresh 2 DB T refresh 3 DB T refresh 4 DB T refresh 5 DB T refresh its consequent optical rise and decay times. As shown in Ref. 11, if an LCD cell is initially biased at a voltage not too far above the threshold voltage, and the voltage is removed instantaneously at t = 0, the transient phase change, δ(t), can be approximated as ( δ (t) = δ 0 exp 2t ), τ 0 = γ 1d 2 τ 0 K 33 π 2. (3) Here δ 0 is the net phase change to the voltage V =0andd is the cell gap. To find optical response time, the intensity change needs to be calculated. The time-dependent normalized intensity change I(t) of the cell under crossed polarizers can be calculated using Eq.(4) which gives the mathematical and physical description of the transient curve of a switching LCD cell: ( ) δ (t) I (t) =sin 2. (4) 2 This describes the temporal response model of the reglcd device. For mclcd, in order to reduce the temporal effect, an overdriving value within one frame is introduced. 14 That way, the target values are reached with a special processing and any enhancement of the temporal noise is avoided. The solution for temporal response improvement does not introduce any artifacts by avoiding any overshooting. In this study, the reglcd and mclcd are compared for five different browsing speeds in the range of 10 to 50 fps. The corresponding image databases (after display simulation) are referred to as DB10, for f browse =10 fps, Proc. of SPIE Vol T-5

6 Figure 4. The multi-slice channelized Hotelling observer, mscho. through DB50, for f browse =50 fps. The details about temporal response simulation parameters are summarized in Table 1. Each database is created for both display models: the with- and the without motion compensation LCD. In addition, as a point of reference, we consider the static images (DBstatic) where no display effects are considered. The results of the display comparison are presented in section CHO for multi-slice images We choose to evaluate the quality of the displays based on the criterion of signal detectability, using the model 6, 10 observers. The recent studies of Platisa et al. suggest that the multi-slice CHO (mscho) model could be used for human-like assessment of multi-slice images. The mscho performs the detection task in a two stage process, as illustrated in Figure 4. In the first stage, the observer pre-processes the image in planar view (xy-plane), slice after slice, and buffers the scores obtained for each slice. This step is modeled by a filter bank of 2D channels, U, applied on the image data of each concerned slice, [g (1),..., g (R) ], to get the channelized slice data, [v (1),..., v (R) ]. Here, P denotes the number of channels and R denotes the number of slices in the experiment where R N. In our experiments, the first P = 20 Laguerre-Gaussian (LG) channels 15 with the channel spread parameter a u = 18 are used, while R {5, 7, 11}. Then, the channelized slice data, v planar, are used to build a test statistic for each slice. This step corresponds to the regular 2D-CHO characterized by its template w CHO appliedoneachr slices to build t planar =[t (1),..., t (R) ]. In the second stage, the observer integrates the information in the z-direction to result in the final stack test statistic, t mscho, and make the classification decision: the signal is present or the signal is absent. In terms of the model, t planar is used as input to the Hotelling observer 10, 15 (HO) with the template w HO which then estimates the final t mscho. In our experiments, for post-lcd image data, the mscho performance is computed for the pixel values achieved at the end of each refresh cycle during the T browse. For example, when the frame repeat FR = 3 (see Table 1), the detection performance is computed for post-lcd image content at the end of each 1 T refresh, 2 T refresh and 3 T refresh. 2.4 Performance measures The experiments are multi-reader multi-slice (MRMC) studies 16 with N rd = 5 readers per image database, each trained on an independent subset of N tr = 500 training image pairs and applied on a unique set of N ts = 500 test image pairs. The training and the testing images do not overlap. In comparing the observer performances, we use the area under the ROC curve (AUC) in combination with the one-shot method 16 for variance analysis. Proc. of SPIE Vol T-6

7 3. RESULTS AND DISCUSSION Our experimental study is set to investigate the effects of the novel algorithm for compensation of the slow temporal response of medical displays. The temporal effects of the displays, the one with the motion compensation (mclcd) and the one without it (reglcd), are simulated as describedinsection2.2. Inparticular, five different browsing rates listed in Table 1 are considered. These cover a wide range from a relatively low f browse 1 =10 fps up to f browse5 = 50 fps, the highest possible rate at f refresh = 50 Hz. Fig. 5 depicts the results of these simulations for one example image in the database. These illustrate the effects of the temporal response of the two displays in the study. The changes in intensity of the central pixel in the xy-plane are shown for an example signal-present image sequence. Each plot represents simulation results for one of the five different browsing rates. The mscho model is used to evaluate the effects of slow temporal response of reglcd and examine the benefit of motion compensation in mclcd. Here, the model observer performance is computed after each refresh cycle of interest, that is after each kt refresh T browse interval, where k = 1,..., T browse /T refresh. Thus, for example, when T browse = T refresh corresponding to FR = 1, the value of k is 1 and we only compute the mscho performance for post-lcd frames after the first refresh cycle, T refresh = T browse. For higher values of T browse,for example T browse =3T refresh, or FR = 3, the mscho scores are computed for post-lcd frames at each 1T refresh, 2T refresh and 3T refresh (k = 3). The results of these computations for each of the five post-lcd databases together with those for the pre-lcd images, all for both reglcd and mclcd, are presented in Fig. 6. The AUC values and their corresponding error bars depicted in the plot from Fig. 6 are estimated using the one-shot algorithm. 16 Additionally, for post-lcd images, we show the mean values of k AUC scores computed at the end of each k refresh cycles. Overall, we observe a clear drop in the AUC values for post-lcd images compared to those for the pre-lcd data, from AUC 0.87 for pre-lcd to AUC< 0.83 in any of the post-lcd setups. Moreover, the AUC trends suggest degradation in the detection performance of the observer as the browsing speed is increased. These findings are in line with the model observer predictions of Liang et al. 5 and the more recent ones of Platisa et al., 6 as well as with the human scores from the study of Badano. 7 Importantly, we observe that the drop in the mscho performance of the LCD with temporal response compensation, mclcd, compared to the LCD with no temporal response compensation, reglcd, is significantly less, see especially the mean AUC estimates marked by stars in Fig. 6. This suggests that the display with temporal response compensation could allow higher detectability of lesions and hence higher diagnostic accuracy in the stack-browsing mode of image reading. The mscho study results were successfully used in selecting the parameters of interest for the followup study with the humans: 17 the browsing speeds as well as size and contrast of the signal. 4. CONCLUSIONS In this work, we presented the mscho based experimental study which examined the benefit of a novel algorithm for compensation of the slow temporal response of medical liquid crystal displays. Overall, our results confirm previous findings about negative effect of the slow temporal response of medical LCD on signal detectability in the stack-mode image reading. Importantly, we find that the novel algorithm for compensation of these LCD effects can bring improvements by increasing the signal detection performance. In addition, the mscho experiments reported in here were able to correctly guide the parameters of the followup human observer study (details to be reported in a separate publication). The significance of our study is threefold. (1) We find that the novel algorithm for compensation of the temporal response effects of LCD can bring improvements in the sense of signal detectability. (2) To the authors knowledge, there are no reports in the literature so far on the practical application of 3D model observers, here the mscho, in the process of medical display system validation nor in preparation of the image reading studies with humans. (3) Our results suggest the potential for the mscho to advance to the more sophisticated and generally applicable human-like model observer. Proc. of SPIE Vol T-7

8 Image pixel intensity post LCD reglcd mclcd Image pixel intensity post LCD reglcd mclcd Image pixel intensity post LCD reglcd mclcd Image pixel intensity post LCD reglcd mclcd Image pixel intensity post LCD reglcd mclcd Image frame identifier, n Figure 5. Pixel luminance in post-lcd images, mclcd compared to in reglcd. For one signal-present image used in the study, the intensity profile of the central pixels (xy-plane) across slices 14 through 27 (z-direction) is shown. In the corresponding pre-lcd image, the signal is centered in the slice 21. Five different browsing rates are considered (top to bottom): 10 fps, 12.5 fps,16.67 fps, 25 fps, and 50 fps (see Table 1). The values of image frame identifier (x-axis labels) denote the start of the frame time interval for a given frame. Proc. of SPIE Vol T-8

9 AUC mclcd: refresh 1 mclcd: refresh 2 mclcd: refresh 3 mclcd: refresh 4 mclcd: refresh 5 reglcd: refresh 1 reglcd: refresh 2 reglcd: refresh 3 reglcd: refresh 4 reglcd: refresh 5 reglcd: mean mclcd: mean static static Frame repeat, FR 2 1 Figure 6. Detection performance of the multi-slice CHO: LCD with temporal response compensation (DBT display) compared to LCD with no temporal response compensation (FFDM display). The computations are performed in an MRMC study with N rd = 5 readers, each trained with an independent subset of N tr = 500 training image pairs and all reading the same test set of N ts = 500 test image pairs. The size of ROI is R = 5. The error bars are ±2 standard deviations estimated by the one-shot method. 16 ACKNOWLEDGMENTS This work is financially supported by IBBT in the context of the MEVIC project. MEVIC is an IBBT project in cooperation with the following companies and organizations: Barco, Hologic and Philips. IBBT is an independent multidisciplinary research institute founded by the Flemish government to stimulate ICT innovation. REFERENCES [1] J. M. Park, E. A. Franken, M. Garg, L. L. Fajardo, and L. T. Niklason, Breast tomosynthesis: present considerations and future applications, Radiographics 27, pp. S231 S240, [2] I. Andersson, D. M. Ikeda, S. Zackrisson, M. Ruschin, T. Svahn, P. Timberg, and A. Tingberg, Breast tomosynthesis and digital mammography: a comparison of breast cancer visibility and birads classification in a population of cancers with subtle mammographic findings, Eur Radiol. 18(12), pp , [3] A. K. Ma, S. Gunn, E. Bullard, and D. G. Darambara, Demonstration of the superiority of digital breast tomosynthesis over 2d mammography through a series of sophisticated computational breast phantoms - a preliminary monte carlo study, in IEEE Nuc. Sci. Symp. Conf. Rec., pp , Oct [4] H. Liang and A. Badano, Temporal response of medical liquid crystal displays, Med. Phys. 34(2), pp , [5] H. Liang, S. Park, B. D. Gallas, K. J. Myers, and A. Badano, Image browsing in slow medical liquid crystal displays, Acad. Radiol. 15, pp , Mar [6] L. Platiša, B. Goossens, E. Vansteenkiste, A. Badano, and W. Philips, Using channelized hotelling observers to quantify temporal effect of medical liquid crystal displays on detection performance, in SPIE MI, pp U 76270U 11, Proc. of SPIE Vol T-9

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