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Keywords:

  • Prostate cancer;
  • 1H MRS;
  • metabolite ratios;
  • acquisition protocols

Abstract

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. PROSTATE METABOLITES
  5. EVALUATION OF THE METABOLITE RATIO
  6. CONCLUSION
  7. ACKNOWLEDGMENTS
  8. REFERENCES

In 1H MR spectroscopic imaging (1H-MRSI) of the prostate the spatial distribution of the signal levels of the metabolites choline, creatine, polyamines, and citrate are assessed. The ratio of choline (plus spermine as the main polyamine) plus creatine over citrate [(Cho+(Spm+)Cr)/Cit] is derived from these metabolites and is used as a marker for the presence of prostate cancer. In this review, the factors that are of importance for the metabolite ratio are discussed. This is relevant, because the appearance of the metabolites in the spectrum depends not only on the underlying anatomy, metabolism, and physiology of the tissue, but also on acquisition parameters. These parameters influence especially the spectral shapes of citrate and spermine resonances, and consequently, the (Cho+(Spm+)Cr)/Cit ratio. Both qualitative and quantitative approaches can be used for the evaluation of 1H-MRSI spectra of the prostate. For the quantitative approach, the (Cho+(Spm+)Cr)/Cit ratio can be determined by integration or by a fit based on model signals. Using the latter, the influence of the acquisition parameters on citrate can be taken into account. The strong overlap between the choline, creatine, and spermine resonances complicates fitting of the individual metabolites. This overlap and (unknown, possibly tissue-related) variations in T1, T2, and J-modulation hamper the application of corrections needed for a “normalized” (Cho+(Spm+)Cr)/Cit ratio that would enable comparison of spectra measured with different prostate MR spectroscopy protocols. Quantitative (Cho+(Spm+)Cr)/Cit thresholds for the evaluation of prostate cancer are therefore commonly established per institution or per protocol. However, if the same acquisition and postprocessing protocol were used, the ratio and the thresholds would be institution-independent, promoting the clinical usability of prostate 1H-MRSI. Magn Reson Med 73:1–12, 2015. © 2014 Wiley Periodicals, Inc.


INTRODUCTION

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. PROSTATE METABOLITES
  5. EVALUATION OF THE METABOLITE RATIO
  6. CONCLUSION
  7. ACKNOWLEDGMENTS
  8. REFERENCES

Prostate cancer is the most prevalent noncutaneous cancer in men and the second leading cause of cancer-related death in western countries [1]. The use of MR in prostate cancer management is emerging, and the demand from patients and clinicians is increasing as a result of the growing number of men suspected of having prostate cancer due to the uptake of blood tests for prostate-specific antigen. In clinical practice, MR is used for its anatomical details in the detection, localization, and characterization of the disease, but the technique also offers possibilities to obtain more functional information—for example, by diffusion-weighted imaging, dynamic contrast-enhanced MRI, and MR spectroscopic imaging (MRSI) [2].

Proton MR spectroscopy (1H-MRS) enables one to study a range of (bio-)molecules by making use of the signals of 1H nuclei in these molecules. In vivo, the detection of molecules is limited to those present at tissue levels of more than 0.5–1 mM. The first metabolite studied with in vivo 1H-MRS of the prostate was citrate, and a decrease in its resonances' amplitude was observed in prostate cancer patients compared with healthy controls [3]. Next, the choline methyl resonance gained attention as an increase in this signal in prostate cancer was observed [4, 5]. Due to frequency-selective water and lipid suppression, only signals of major metabolites between approximately 2.2 and 3.8 ppm remain in prostate spectra, which also includes those of creatine and polyamines. Because the choline and creatine methyl signals are only separated by a relatively small chemical shift difference, a nonoptimal B0 homogeneity, causing line broadening, will lead to signal overlap. The presence of polyamine signals between these two metabolite signals further complicate their separation. This potential overlap led to the introduction of the signal ratio of choline plus creatine divided by citrate (Cho+Cr)/Cit [4, 6] and its inverted counterpart [citrate over choline plus creatine ratio [5]] as a marker for prostate cancer.

In this review, the major variables that can influence the (Cho+Cr)/Cit ratio are discussed. An overview of the main metabolites detectable in the commonly obtained MR spectra of the prostate, their function, and the acquisition parameters that influence their appearance in the spectrum will be provided. These metabolites form the basis for the (Cho+Cr)/Cit ratio, of which the intensity not only depends on the underlying physiology, metabolism, and anatomy, but also on the acquisition parameters. This latter aspect is particularly relevant for the citrate and polyamine signals.

Although 1H-MRSI has great potential in prostate cancer management, its use in routine clinical practice is limited. A major hurdle toward clinical use is that several acquisition and processing steps still rely on manual procedures—in particular postprocessing of the data, including quality control—and displaying easily visualized and interpretable results [7]. As is the case for diffusion-weighted imaging and dynamic contrast-enhanced MRI of the prostate, a more widespread consensus in the acquisition and fitting of data would also promote clinical translation. A generalized classification scheme for prostate MR spectroscopy to assist in prostate cancer management is not available. The second part of this review discusses the use of a protocol-dependent classification scheme that could increase the clinical usability of the metabolite ratio for prostate cancer management.

PROSTATE METABOLITES

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. PROSTATE METABOLITES
  5. EVALUATION OF THE METABOLITE RATIO
  6. CONCLUSION
  7. ACKNOWLEDGMENTS
  8. REFERENCES

The main metabolite signals in commonly obtained 1H-MRSI spectra of the prostate are choline, creatine, polyamines and citrate (Fig. 1).

image

Figure 1. 1H MR spectrum of the peripheral zone of the prostate of a healthy male containing signals of choline (Cho), spermine (Spm), creatine (Cr), and citrate (Cit). The spectrum was acquired at 3T using a PRESS sequence with a TE of 145 ms and MEGA pulses for water and lipid suppression.

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Choline

Different compounds containing a choline moiety can contribute to the main peak at about 3.2 ppm in in vivo 1H-MR spectra. These are free choline, glycerophosphocholine, and phosphocholine, but also contributions from protons in taurine, ethanolamines and myo-inositol may be present at this spectral position [8]. As a convenient shorthand, we refer to this composite resonance as the “choline” signal. Choline-containing metabolites are precursors and breakdown products of the phospholipid phosphatidylcholine, a major cell membrane compound [9]. In prostate cancer cell lines, an increase in choline is observed due to an altered phospholipid metabolism [10]. This alteration is probably due to an increased expression and activity of choline-kinase, a higher rate of choline transport, and an increased phospholipase activity [9, 10].

The choline moiety has nine chemically equivalent protons of three methyl groups resonating as a singlet around 3.19 ppm and two methylene groups, resulting in two multiplets at 4.05 and 3.50 ppm [11]. Because the intensity of these multiplets is very low in in vivo MR spectra of the prostate, it is common to only evaluate the nine-proton singlet at 3.19 ppm. Estimates of the T1 and T2 relaxation times of the choline methyl protons (Table 1) are valuable to determine the effect of the chosen echo time (TE) and repetition time (TR) on the choline signal intensity.

Table 1. In vivo T1 and T2 Relaxation Times of the Prostate Metabolites Choline, Creatine, and Citrate
  T1T2
ReferenceMetabolite1.5T3T1.5T3T
  1. Unless indicated, no distinction was made between the peripheral zone (PZ) and transition zone (TZ).

Heerschap et al. ( [6]Choline0.84 ± 0.09 s 0.23 ± 0.06 s 
Creatine0.86 ± 0.1 s 0.21 ± 0.1 s 
Citrate0.34 ± 0.04 s   
Heerschap et al. ( [64]Citrate  0.18 ± 0.1 s 
Lowry et al. ( [46]Citrate0.84 ± 0.08 s 0.14 ± 0.02 s (PZ) 
0.12 ± 0.03 s (TZ)
Scheenen et al. ( [22]Choline 1.1 ± 0.4 s 0.22 ± 0.09 s
Citrate 0.47 ± 0.14 s 0.17 ± 0.05 s
Chen et al. ( [65]Choline 0.96 ± 0.25 s  
Citrate 0.54 ± 0.14 s  

Creatine

Both creatine and phosphocreatine contribute to the methyl resonance observed at about 3.0 ppm in 1H-MRS of the prostate (referred to, in combination, as the creatine signal in this paper). Creatine plays a crucial role in the energy metabolism of tissues [12], as phosphocreatine acts as a spatial and temporal buffer to maintain constant adenosine triphosphate levels in tissue through the creatine-kinase reaction. The stromal cells consist predominantly of smooth muscle cells [13], which are expected to contribute most to the creatine and phosphocreatine signals.

Creatine has five nonexchanging protons: a methyl group resonating at 3.03 ppm and the methylene group at 3.93 ppm. The protons in each group are chemically equivalent and uncoupled, resulting in two singlets with a ratio of 3:2. The relaxation times of the methyl protons of creatine are given in Table 1. The concentration of creatine was estimated with in vivo MRS at 4.4 ± 0.8 mM [6] and with ex vivo high-resolution magic angle spinning spectroscopy as being between 7.6 ± 2.7 and 9.7 ± 4.4 mmol/kg for normal and cancer tissue (no significant differences) [14].

Citrate

The production and storage of citrate is one of the main functions of the prostate. Citrate is an intermediate in the tricarboxylic acid cycle. In most organs, citrate is quickly oxidized in the tricarboxylic acid cycle and is therefore only present in low concentrations. In contrast, prostate epithelial cells actively produce citrate and store it in the luminal space, where it is one of the main components of the prostatic fluid [15]. Prostate tissue has high levels of zinc, which inhibits mitochondrial (m-)aconitase activity. This leads to the buildup of a high concentration of citrate [16]. In prostate cancer, a decrease in zinc levels is observed that leads to activation of m-aconitase and the consequential oxidation of citrate [16]. At the same time, the morphology of the prostate gland changes, leading to a loss of luminal space, which might also cause a decrease in the observed (or total) citrate levels.

Citrate contains two methylene groups that are magnetically equivalent (Fig. 2A). The four protons of these groups form a strongly coupled AB spin system. The difference in chemical shifts (Δ), the midpoint of the chemical shifts (δ), and the scalar coupling (J) of this spin system depend on pH [17, 18] and cation concentration [18] and are approximately 0.15 ppm, 2.61 ppm, and 16.3Hz, respectively (Fig. 2B). Because citrate is a strongly coupled spin system, its shape depends on interpulse timing, pulse shape, TE, and field strength [17-23]. In Table 1, the relaxation times for citrate at 1.5T and 3T are given. The determination of the T2 relaxation time of citrate is less straightforward than for singlets. By increasing the TE in such an experiment, there is not just attenuation of the signal intensity due to T2 relaxation, but also shape and intensity variation due to J-modulation.

image

Figure 2. A: Schematic chemical structure of citrate. B: Simulated spectral shape of citrate at 600 MHz. Indicated are the scalar coupling constant (J), the chemical shift difference (Δ) and the midpoint of the chemical shifts of the second and third peak (δ).

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In the first in vivo prostate 1H-MRS studies at 1.5T, stimulated echo acquisition mode (STEAM) and point resolved spectroscopy (PRESS) techniques were used for volume localization [4, 24]. One advantage of the STEAM for these data is its ability to use a very short TE (at the expense of the general loss of half of the signal in a stimulated echo). In this way, the strongly coupled protons of citrate will have limited phase evolution, which will result in an almost completely in-phase citrate signal. Integration of the area of the citrate peak(s) will then result in maximal signal intensity. For the PRESS sequence, generally longer TEs are used for prostate MRS, and dispersive parts can be present in the spectrum that affect the peak area by cancellation with absorptive parts in simple integration. Several studies have been performed to determine the PRESS pulse timing with maximum absorptive signal at the central lines of the citrate signal. Van der Graaf et al. [25] used a delay of 7.5 ms between excitation and the first refocusing pulse, and varied the TE. Their optimal TE was 130 ms at 1.5T. One sequence optimization procedure at 3T led to an optimal TE of 75 ms (negative absorptive shape) and 145 ms (positive absorptive shape) with a delay of 25 ms between excitation and refocusing [22]. However, other researchers found an optimal TE of 85 ms [26] and there are more possibilities that result in a favorable citrate signal [19]. It is therefore not surprising that prostate MRSI acquisition software packages of three MR vendors at 3T are equipped with quite different TEs, varying from 85 to 145 ms [27]. The differences in the spectral shape of citrate in different pulse sequences will lead to variations in the integral of citrate signal at a constant citrate concentration. The influence of interpulse timing is also evident from matrix density simulations [19, 22] illustrated in Figure 3, which shows simulated and in vivo spectra of one patient, using a PRESS sequence at 3T with a TE of 145 ms and a semi-LASER sequence (Localization by Adiabatic Selective Refocusing) at nearly the same TE of 144 ms [28]. In the semi-LASER sequence the excitation pulse is followed by four adiabatic refocusing pulses.

image

Figure 3. A: Simulated spectral shape of citrate (Cit) using the PRESS sequence with an optimized pulse timing (90°—25 ms—180°—72.5 ms—180°—47.5 ms—echo) at a TE of 145ms at 3T [22]. B: The simulated Cit shape using an optimized semi-LASER sequence at a TE of 144 ms (90°—11 ms—180°—21 ms—180°—29 ms—180°—51 ms—180°—32 ms—echo) [28]. C: T2-weighted image of the prostate of a 71-year-old man with biopsy proven prostate cancer (Gleason score 9). D: In vivo spectrum of the PRESS (TR = 750 ms) of normal tissue. E: Spectrum of corresponding region with the semi-LASER sequence (TR = 2070 ms). The region is indicated in panel C with a blue circle. F: In vivo spectrum of tumor tissue (red circle in panel C) with the PRESS sequence. G, H, and I: The corresponding fits of LCModel using the simulated citrate shape of panels A and B show minimal residuals (J, K, and L). Each spectrum is scaled to maximum intensity.

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The optimal TR for citrate detection can be quite short, because of its relatively short T1 (Table 1). It was calculated that the use of a TR of 750 ms, instead of 1500 ms, would lead to an increase in signal-to-noise ratio per unit time of 17% for citrate and a decrease of 6% for choline for 3T data [22]. When a weighted averaged acquisition scheme is used for MRSI, the use of a short TR allows for more averages in the centre of k-space in the same amount of measurement time. In this way, the acquisition of the phase-encoding steps in k-space can be done following the shape of a Hanning filter. Apodization in k-space with a Hanning filter produces a point spread function with widened full-width-at-half-maximum, but strongly decreased signal contamination form more spatially distant signals [22]. The effective voxel size increases, but extraprostatic lipid signal contamination is strongly decreased.

In the physiological range of pH (6.8–7.4), variations in Δ of 3.2 Hz and variations in δ of 0.025 ppm were observed using a 400-MHz magnet [17]. The changes in J are minimal in this pH range [17, 18], but the concentrations of zinc, calcium, and magnesium also influence the value of this coupling [18], which may have significant effects on the in vivo resonances. Figure 4 shows that small changes in Δ and J can have substantial influences on the spectral shape of citrate. Although a relation was found in vitro between the spectral shape of citrate and the ion concentration or pH, variations in the spectral citrate shape in vivo are difficult to relate to ion concentrations or pH, as these are difficult to measure. For a good fitting, it is necessary to use a model signal that is based on J, δ, and Δ values that closely resemble those present in vivo. At our institution, we use J = −16.2, δ = 2.625, Δ = 0.154, as this led to the smallest residuals for the citrate resonance using LCModel fitting (unpublished data). Using these parameters, the in vivo citrate shape closely resembles the simulated spectrum (Fig. 3). This was the case for both the PRESS spectrum and that obtained with the semi-LASER sequence, indicating that these values are a good approximation of the in vivo coupling parameters.

image

Figure 4. Simulations with NMRSIM (part of Topspin, Bruker BioSpin Corporation, Billerica, Massachusetts, USA) show the influence of small differences in the scalar coupling and chemical shifts on the citrate shape at a field strength of 3T with an optimized pulse sequence (TE 145 ms) [22]. Line broadening of 1 Hz (black spectra) and 4 Hz (red spectra) were used. Δ was 2.6105 ppm for all spectra. A–D: Δ was kept constant at 0.151 and the scalar coupling was varied between 15.3 Hz and 16.8 Hz. E–H: J was kept constant at 16.3 Hz and Δ was varied between 0.141 ppm and 0.156 ppm. The amplitudes of all spectra are scaled to a reference signal.

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The side lobes of the citrate resonance extend to the 3 ppm region and contain a mixture of absorption and dispersion shapes at a TE of 145 ms at 3T [22], which can result in a negative effect on the total intensity in this region. When a model signal is used for the fitting, this effect can be taken into account; however, when simple peak integration is used, one cannot fully compensate for this effect. Negative components of the citrate signal will be excluded from citrate quantification, and one might need to revert to severe baseline corrections to prevent an influence on other metabolite signals.

The RF pulses that are used to suppress the large lipid and water signals may adversely affect the citrate signals. Often dual frequency-selective pulses are used to suppress both these signals simultaneously, mainly by Mescher-Garwood (MEGA) [29] or double band-selective inversion with gradient dephasing (BASING) [30, 31] pulses. These pulses selectively invert the lipid and water resonances and are surrounded by crusher gradients. Their bandwidth and position in the frequency domain should be sufficient to invert all lipid signals, but distant enough from the chemical shift of citrate. When the bandwidth of the lipid inversion pulse is too broad, this will cause a decreased signal intensity of citrate (Fig. 5). As a consequence, healthy spectra may get a “cancerous” profile. Therefore, a good adjustment of the dual frequency pulses is essential for obtaining consistent results. Spectrally selective refocusing pulses may be used instead of signal suppression pulses, which prevent refocusing of lipids by simultaneous volume and frequency selection [32, 33]. Care should be taken that these pulses fully excite or refocus the citrate spins and leave the lipid signals untouched.

image

Figure 5. A–C: In vivo prostate spectra of a healthy volunteer at 3T using PRESS with MEGA pulses for water and lipid suppression. The spectra are from the same location, but the width of the frequency selective inversion bands was 1.40 ppm in panel A, 1.45 ppm in panel B, and 1.55 ppm in panel C, leading to a decrease in the citrate (Cit) intensity, while the intensities of choline (Cho), spermine (Spm), and creatine (Cr) remain unchanged. Consequently, the (Cho+Spm+Cr)/Cit ratio increases influencing the classification of the spectrum. D–F: The same effect in a phantom containing Cit, Cho, Spm, and Cr using MEGA pulses with a width of the inversion bands of 1.35 ppm in D, 1.45 ppm in E, and 1.55 ppm in F. G: Shape of the MEGA pulse in the frequency domain. These (Cho+Spm+Cr)/Cit ratios were determined with LCModel. In line with previously published metabolite ratios, the citrate intensity in LCModel was scaled to the number of protons in citrate, rather than to the magnitude integral at this TE, which is smaller due to cancellation of signal intensity by the strongly coupled pattern.

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For proper selective suppression or spectral excitation, the homogeneity of the B0 field is critical. Poor homogeneity will not only negatively affect spectral quality as it causes broadening of the spectral lines; it will also decrease the effectiveness of frequency selective pulses. Broadened or shifted fat and water signals can suffer from diminished suppression and components of these signals may overlap with the resonances of interest. In addition, in the case of shifted or broadened spectral lines of citrate, the metabolite can be influenced by the frequency selective pulses for lipid and water suppression leading to decreased citrate intensities, comparable to the effect shown in Figure 5.

The dependence of the citrate signals on interpulse timing can also be exploited for spectral editing. By varying this timing (at a constant TE) in such a way that citrate is inverted in one measurement and in phase in the next measurement, uncoupled resonances can be removed from the spectrum by subtraction [34, 35]. In this way, rapid citrate imaging without lipid suppression is possible.

Polyamines/Spermine

The tissue concentration of polyamines in the prostate is relatively high. Because spermine is the dominant polyamine in the prostate, we focus on this compound. Like citrate, polyamines are stored in the luminal space, and a very strong correlation between the citrate and spermine concentration is reported for prostatic fluid specimens [36]. A hypothesis for the strong correlation (r = 0.94) is the formation of complexes between citrate and spermine since citrate is negatively charged, whereas spermine is positively charged. In this way ionic neutrality can be achieved [36]. Polyamines play a role in prostatic growth and differentiation [37]. A decrease in spermine has been suggested as a marker for prostatic malignancy [37, 38]. In prostate cancer, a decrease in spermine or polyamine levels is observed compared with benign tissue using MRS [38] and high-resolution magic angle spinning experiments [14]. The incorporation of polyamine levels measured with MRS to improve detection of prostate cancer has been proposed and has yielded an increased sensitivity at the same specificity [39].

Spermine is a coupled spin system and, in addition to its amine groups, contains 10 methylene groups. These methylene protons consist of symmetrical pairs, giving a total of four protons that resonate approximately at 1.81 ppm with further groups of four at 2.11 ppm, 3.13 ppm, 3.12 ppm, and 3.18 ppm [40]. These chemical shifts are pH-dependent [41] and these quoted chemical shifts were measured at pH 7 [40]. At a higher pH, the amine groups are more protonated, and the chemical shifts are therefore more downfield [41]. Protons near a nitrogen atom show the largest pH dependence. Spermine proton chemical shifts are also sensitive to temperature differences. We performed temperature measurements at 500 MHz with a spermine compound dissolved in water. These measurements showed that protons near a nitrogen atom had the highest temperature dependence (unpublished data). For that reason, when one wants to perform a phantom measurement to determine the shape of spermine (with a certain sequence), the phantom should be measured at body temperature and have a pH in the physiological range. Local chemical shift correction to improve the separation between choline and spermine is hindered by the dependence of the chemical shift of spermine on the environment. Also, usually no water reference measurement is done that could be used for this purpose. The metabolites in the prostate spectrum are unsuitable for this purpose, as the chemical shift of citrate is environment-dependent and choline is not always well separable from spermine.

As with citrate, TE and interpulse timing influence the spectral shape of spermine and leading to dispersive components in the resonances. If dispersive parts are present in the 3.1-ppm region, this can negatively affect the apparent intensity of choline and/or creatine resonances. Furthermore, BASING and MEGA pulses that are used for simultaneous water and lipid suppression invert the 2.1- and 1.8-ppm resonances of spermine [42]. Without these pulses and crushers, the 2.1- and 1.8-ppm resonances could be helpful for decomposition of spermine from the 3.1-ppm region. Figure 6 shows the influence of the MEGA pulses on the spectral shape of spermine. As expected, the resonances at 1.8 and 2.1 ppm are almost completely crushed by the combination of MEGA pulses and crushing gradients. The resonances at 1.8 and 2.1 ppm are scalar coupled to the resonances in the 3.1 ppm region; therefore, the selective refocusing of these upfield groups also refocuses the J-evolution of the downfield protons. This is evident from the difference in spectral shape and intensity of spermine in Figure 6B and 6C, where a different timing for the MEGA pulses is used. These measured spermine shapes can be used as prior knowledge for spectral fitting of the metabolites [42]. Figure 7 demonstrates how the spermine shape can affect the choline and creatine region, showing two spectra from the same location in one volunteer that are measured with a different MEGA pulse timing, resulting in different (Cho+(Spm+)Cr)/Cit ratios.

image

Figure 6. The spectral shape of spermine measured in a phantom at 3T using PRESS with a TE of 145 ms (90°—25 ms—180°—72.5 ms—180°—47.5 ms—echo). The phantom contained 18 mM spermine, 9 mM ZnCl, 15 mM MgCl2, 18 mM CaCl2, and 60 mM of KCl [41, 71]. The pH was adjusted to 6.8 and the temperature was 310 K. A: The spermine spectral shape without the use of MEGA pulses. B: Spermine spectral shape when two MEGA pulses for combined water and lipid suppression are used surrounding the second refocusing pulse. C: Spectral shape when two MEGA pulses are used surrounding the first refocusing pulse.

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image

Figure 7. A: Spectra of a healthy volunteer of the same voxel in two measurements, containing resonances of choline (Cho), spermine (Spm), creatine (Cr), and citrate (Cit). The difference between the measurements is the timing of the MEGA pulses. In the black spectrum, the MEGA pulses surround the second refocusing pulse (as in Fig. 5B) and in the red spectrum the MEGA pulses surround the first refocusing pulse (as in Fig. 5C). Two different LCModel basis sets were used for the fitting (to take the differences in spermine shapes into account), and the (Cho+Spm+Cr)/Cit amplitude ratios were (B) 0.35 (black and fit) and (C) 0.46 (red and fit). This demonstrates the influence of spectral spermine shape on the (Cho+Spm+Cr)/Cit ratio.

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T1 and T2 values of spermine reported in the literature are obtained in vitro and the T2 values were rather short and dependent on the presence of ions and proteins [41]. No in vivo data of relaxation times of spermine spins is available yet.

EVALUATION OF THE METABOLITE RATIO

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. PROSTATE METABOLITES
  5. EVALUATION OF THE METABOLITE RATIO
  6. CONCLUSION
  7. ACKNOWLEDGMENTS
  8. REFERENCES

Prostate 1H-MRSI spectra can be evaluated qualitatively or (semi-)quantitatively. Qualitative guidelines are based on visual inspection of the height of choline compared with the citrate height [43, 44]. When using this qualitative approach of comparing peak heights in a prospective multicenter setting, no additional value for 1H-MRSI was found compared with the use of MRI alone for the localization of prostate cancer in the peripheral zone [45]. In this study, data homogeneity across different centers was not validated with, for example, mean values of noncancerous tissue across centers. This, together with the lack of a clear definition of tumor focus size that needed to be localized, could explain the disappointing results. A more quantitative aproach is determination of metabolite concentrations in prostate spectra with the help of a reference compound. If the tissue water concentration is known in the volume of interest and the individual metabolites are fitted reliably, water can be used as an internal reference to obtain absolute prostate metabolite concentrations [e.g., [6, 46]]. This requires additional time for a water reference measurement, which is often not available. More practical, and sufficient for diagnostic purposes, is the use of the (Cho+Cr)/Cit ratio for classification. The usability of this ratio for prostate cancer localization was demonstrated in a prospective multicenter study, where an area under the receiver operating characteristic curve of 0.88 was obtained for discriminating normal peripheral zone tissue from cancer [47]. Classification thresholds for the (Cho+Cr)/Cit ratio are needed to apply MR spectroscopy for prostate disease in clinical routine.

Establishing the Ratio of (Cho+(Spm+)Cr)/Cit

The simplest method of calculating the (Cho+Cr)/Cit ratio is to use integration techniques and calculate a ratio based on these values (integral values). This has been used by many researchers to calculate the ratio (Table 2). However, as discussed above, strong coupling effects and a long TE can produce a spectral shape of citrate that has an integral close to zero, and small differences in citrate intensity will induce very variable ratios. One way to circumvent this problem is by fitting the metabolites to a model signal. Model signals can be measured with phantom solutions [e.g., [42]]. Alternatively, they can be simulated, when the chemical shifts and J-coupling constants are known for the metabolites of interest [e.g., [42, 48]]. There are several software packages available that can be used to make model signals, including NMRSIM (part of Topspin, Bruker BioSpin Corporation, Billerica, Massachusetts, USA), GAMMA [49], and its successor VeSPA (http://scion.duhs.duke.edu/vespa/). The model signals can be used to obtain fits of the metabolites in the time [50, 51] or frequency domain [52]. The output is typically a relative metabolite concentration value incorporating the amount of protons of the metabolite. These relative concentrations depend on the goodness of the fit and correlation between fits; for overlapping metabolites, overestimation of one metabolite at the cost of underestimation of another will result in a ratio that is unrepresentative for the tissue if the metabolites have a different amount of protons. Therefore, if the metabolites are fitted individually with such a quantification algorithm, the concentrations must be reconverted to their relative spectral amplitude prior to summation and division in the ratio. This procedure removes individual information gained from each metabolite but improves the reliability of the ratio. For spectral patterns dominated by absorption components, this amplitude ratio will give comparable results as the integral ratio. However, in contrast to the integral ratio, the amplitude ratio is less sensitive to the dispersive parts of citrate. The amplitude ratio gave good results for discrimination between different prostate tissues in a 3T study, where the spectral citrate shape had nonnegligible dispersive parts [47].

Table 2. Average (Cho+Cr)/Cit Ratios for Benign Tissue in the Peripheral Zone (PZ), Transition Zone (TZ), or Combined at 1.5T from Different Studies Using Different Postprocessing Methods
ReferencesAverage (Cho+Cr)/Cit Ratio ± Standard DeviationVendorPostprocessing MethodTE/TR (ms)
  1. a

    Two techniques for water suppression were used. No statistical difference in the (Cho+Cr)/Cit ratio was found.

  2. b

    Data are presented as the median (25th and 75th percentile).

Kurhanewicz et al. [5]0.54 ± 0.11 (PZ)GEIntegration of designated frequency ranges130/1000
0.83 ± 0.34 (TZ)
Males et al. [30]0.22 ± 0.13 (PZ)aGEIntegration of designated frequency ranges130/1000
0.31 ± 0.17 (PZ)
Mueller Lisse et al. [66]0.24 ± 0.13 (PZ)GEIntegration of designated frequency ranges130/1000
Shukla-Dave et al. [39]0.59 ± 0.03 (PZ)GEIntegration of designated frequency ranges130/1000
Mazaheri et al. [67]0.73 ± 0.18 (PZ)GEIntegration of designated frequency ranges130/1000
García-Martín et al. [42]0.24 ± 0.06 (PZ)GELCModel fitting [52]130/1000
Weis et al. [68]0.50 ± 0.16PhilipsAMARES fitting in the MRUI software package [50]130/1200
Kaji et al. [69]0.58 ± 0.38 (PZ)SiemensIntegrals of Lorentzian lineshapes using Luise [69]135/1500
0.72 ± 0.51 (TZ)
van Dorsten et al. [70]0.38 ± 0.15 (PZ)SiemensAMARES fitting in the MRUI software package [50]120/1200
0.43 ± 0.16 (TZ)
Wetter et al. [71]0.39 ± 0.26SiemensIntegrals of Gaussian lineshapes120/1300
Scheenen et al. [47]0.28 (0.21−0.37) (PZ)bSiemensModel spectra time domain using PRISMA120/650
0.36 (0.28−0.44) (TZ)b
     

The fit of the individual metabolites has been used to derive the choline over citrate plus spermine ratio (Cho/Cit+Spm), a ratio that takes the counteracting effects of the increase in choline and decrease in citrate and spermine in prostate cancer into account [42]. However, due to the inherent uncertainty in individual metabolite quantification [53], we recommend to use a (Cho+Cr)/Cit ratio rather than a simpler choline/citrate (or Cho/Cit+Spm) ratio. In most studies, spermine is not fitted individually [e.g., [45, 53-56]], and spermine resonances are included in the creatine and choline fits [39]. The ratio should therefore be seen as the (Cho+(Spm+)Cr)/Cit ratio .

Classification Thresholds for (Cho+(Spm+)Cr)/Cit Ratio

In the previous sections, the acquisition parameters that influence the prostate metabolite signals, and consequently the (Cho+(Spm+)Cr)/Cit ratio, were discussed. In principle, it is possible to correct for T1, T2 relaxation, and J-modulations and obtain a normalized ratio that is acquisition-independent. However, this requires knowledge of their precise values, which are not available (current reports show quite some variation in T1, T2), and these values may also vary for different tissue morphology (e.g., cancer, benign disease, and normal prostate). Moreover, fitting of the individual metabolite signals is also necessary, which can be challenging because of overlap between choline, creatine, and spermine signals. For that reason, we cannot work with a normalized ratio, and the (Cho+Cr)/Cit ratio thus has to be established per institution or per protocol.

The mean (Cho+Cr)/Cit plus two or three times the standard deviation of normal tissue was used as a cutoff value to classify voxels as cancerous [5, 57-59]. The use of a five-point classification scale based on the mean and standard deviation of the (Cho+Cr)/Cit ratio of normal prostate tissue has been proposed [44]. Because the (Cho+Cr)/Cit ratio is higher for normal transition zone tissue than for peripheral zone tissue, the cutoff values vary for the two tissues. Tables 2 and 3 provide an overview of reported mean (Cho+Cr)/Cit ratios for normal peripheral zone tissue at 1.5T and 3T. For 1.5T, the used TEs in these studies are quite similar (120–130 ms) and often the same platform and postprocessing methods are used, but there is quite some variation in the reported (Cho+Cr)/Cit ratios. At 3T, there are considerable differences between prostate spectroscopy packages of the vendors, with TEs varying from 85 to 145 ms and TRs from 750 to 1300 ms [27]. At 7T, optimal acquisition protocols still need to be established [60]. The studies reporting (Cho+Cr)/Cit values for 3T spectra are still limited (Table 3), but it can be expected that the metabolite ratios for normal tissue at 3T will be more variable among studies, and quite different (Cho+Cr)/Cit values would be obtained for the same patient measured on systems of different vendors. The dependency on acquisition and postprocessing protocols asks for assessment of the mean and standard deviation of normal tissue per institution or per used protocol.

Table 3. Average (Cho+Cr)/Cit Ratios for Peripheral Zone (PZ) and Transition Zone (TZ) Tissue at 3T from Three Studies Using Different Postprocessing Methods
ReferencesAverage (Cho+Cr)/Cit Ratio ± Standard DeviationVendorPostprocessing MethodTE/TR (ms)
  1. a

    Measured in males aged >51 y.

  2. b

    Data are presented as the average (confidence interval).

Weis et al. [72]0.40 ± 0.09 (PZ)aPhilipsLCModel fitting140/1500
0.53 ± 0.12 (TZ)a
Scheenen et al. [54]0.22 ± 0.12 (PZ)SiemensModel spectra time domain using PRISMA145/750
0.34 ± 0.14 (TZ)
Selnæs et al [48]0.38 (0.35−0.42) (PZ)bSiemensModel spectra time domain using PRISMA145/750

The derived classification thresholds are not necessarily institution-dependent. If the same acquisition protocol and postprocessing method are used at different institutions, the (Cho+(Spm+)Cr)/Cit ratio can be compared between these institutions. Previously, no significant differences in the (Cho+Cr)/Cit amplitude ratio were found of any of the benign prostate tissues between patients among different institutions [47]. Furthermore, the amplitude ratio gave good reproducibility in repeated measurements of the same subjects [61].

Standardized Threshold Approach

A more advanced classification strategy is the use of the standardized threshold approach, which was introduced by Jung et al [62] to give more weight to the increase in choline and decrease in polyamines observed in prostate cancer. The standardized threshold approach was developed for use in the peripheral zone [62], but later expanded for the transition zone [63]. In this approach, an initial score (from 1 to 5) is given to each spectrum (Table 4 and 5) based on the mean (Cho+Cr)/Cit ratio of normal tissue and its standard deviation. The initial score is adapted for certain choline over creatine (Cho/Cr) values. The fit for the Cho/Cr ratio is simplified in cancer tissue, due to the decrease of polyamine resonances, and might therefore be more reliable in tumor voxels than in normal voxels. For voxels with an initial score of 2 or 3, the initial score is increased to 4 if the Cho/Cr ratio is larger than or equal to 2. An initial score of 4 or 5 is decreased to 3 or 4, respectively, if the Cho/Cr ratio is smaller than 2 (Tables 4 and 5). With this reproducible approach [61], good accuracy (72%–87 %) for the differentiation of benign and malignant voxels was obtained in the peripheral zone [62] and transition zone [63]. The cutoff value for the Cho/Cr ratio was optimized to develop the standardized threshold approach as an aggressiveness assessment [55]. Using this assessment, it was found that only 10% of the highly aggressive tumors would be misclassified as less aggressive [55]. The cutoff values provided in Tables 4 and 5 can only be used for spectra acquired with the same sequence at the same field strength and similar postprocessing (no fitting of separate polyamines). If one wants to use the standardized threshold approach with other settings, one should first establish the correct cutoff values.

Table 4. Example of Cutoff Values of the Standardized Threshold Approach Used at 1.5T [73] (TE/TR = 120/650 ms)
Score and Score Definition(Cho+Cr)/Cit Ratio
Peripheral ZoneTransition Zone
  1. The (Cho+Cr)/Cit ratio is obtained from integrals of Gaussian lineshapes.

  2. Cho/Cr ratio adjustment: If Cho/Cr ratio is ≥2, adjust 3 and 2 into 4. If Cho/Cr ratio is <2, adjust 5 into 4 and 4 into 3.

1: Definitely benign tissue≤0.44≤0.52
2: Probably benign tissue0.44<CC/C≤0.580.52<CC/C≤0.66
3: Possibly malignant tissue0.58<CC/C≤0.720.66<CC/C≤0.80
4: Probably malignant tissue0.72<CC/C≤0.860.80<CC/C≤0.94
5: Definitely malignant tissue>0.86>0.94
Table 5. Example of Cutoff Values of the Standardized Threshold Approach Used at 3T [55]
Score and Score Definition(Cho+Cr)/Cit Ratio
Peripheral ZoneTransition Zone
  1. The CC/C cutoff values are based on the mean and standard deviation of normal tissue values published in Scheenen et al. [54].

  2. Cho/Cr ratio adjustment: If Cho/Cr ratio is ≥2, adjust 3 and 2 into 4. If Cho/Cr ratio is <2, adjust 5 into 4 and 4 into 3.

1: Definitely benign tissue≤0.34≤0.48
2: Probably benign tissue0.34<CC/C≤0.460.48<CC/C≤0.62
3: Possibly malignant tissue0.46<CC/C≤0.580.62<CC/C≤0.76
4: Probably malignant tissue0.58<CC/C≤0.700.76<CC/C≤0.90
5: Definitely malignant tissue>0.70>0.90

CONCLUSION

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. PROSTATE METABOLITES
  5. EVALUATION OF THE METABOLITE RATIO
  6. CONCLUSION
  7. ACKNOWLEDGMENTS
  8. REFERENCES

We have discussed different aspects of the acquisition and postprocessing that influence the (Cho+Cr)/Cit ratio. In principle, this ratio could be vendor-independent with similar acquisition protocols and post processing, which would be convenient for the common use of classification thresholds of the ratio for disease features. Although this may be the case for 1.5T to some extent, it is not for 3T systems. Thus in all clinical applications, institution or protocol-dependent thresholds have to be established. To promote clinical usability of prostate MR spectroscopic imaging, it would be desirable if consensus could be reached on field strength dependent common acquisition and postprocessing protocols.

ACKNOWLEDGMENTS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. PROSTATE METABOLITES
  5. EVALUATION OF THE METABOLITE RATIO
  6. CONCLUSION
  7. ACKNOWLEDGMENTS
  8. REFERENCES

We thank Marc Jupin for involvement with the spermine phantom measurement and Marnix Maas for help with metabolite ratio calculations in LCModel.

REFERENCES

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. PROSTATE METABOLITES
  5. EVALUATION OF THE METABOLITE RATIO
  6. CONCLUSION
  7. ACKNOWLEDGMENTS
  8. REFERENCES
  • 1
    Siegel R, Naishadham D, Jemal A. Cancer statistics, 2012. CA Cancer J Clin 2012;62:1029.
  • 2
    Hoeks CMA, Barentsz JO, Hambrock T, et al. Prostate cancer: multiparametric MR imaging for detection, localization, and staging. Radiology 2011;261:4666.
  • 3
    Thomas MA, Narayan P, Kurhanewicz J, Jajodia P, Weiner MW. 1H MR spectroscopy of normal and malignant human prostates in vivo. J Magn Reson B 1990;87:610619.
  • 4
    Kurhanewicz J, Vigneron DB, Nelson SJ, Hricak H, MacDonald JM, Konety B, Narayan P. Citrate as an in vivo marker to discriminate prostate cancer from benign prostatic hyperplasia and normal prostate peripheral zone: detection via localized proton spectroscopy. Urology 1995;45:459466.
  • 5
    Kurhanewicz J, Vigneron DB, Hricak H, Narayan P, Carroll P, Nelson SJ. Three-dimensional H-1 MR spectroscopic imaging of the in situ human prostate with high (0.24-0.7-cm3) spatial resolution. Radiology 1996;198:795805.
  • 6
    Heerschap A, Jager GJ, Van Der Graaf M, Barentsz JO, Ruijs SHJ. Proton MR spectroscopy of the normal human prostate with an endorectal coil and a double spin-echo pulse sequence. Magn Reson Med 1997;37:204213.
  • 7
    Kobus T, Wright AJ, Scheenen TW, Heerschap A. Mapping of prostate cancer by 1H MRSI. NMR Biomed 2014;27:3952.
  • 8
    Swanson MG, Keshari KR, Tabatabai ZL, Simko JP, Shinohara K, Carroll PR, Zektzer AS, Kurhanewicz J. Quantification of choline- and ethanolamine-containing metabolites in human prostate tissues using 1H HR-MAS total correlation spectroscopy. Magn Reson Med 2008;60:3340.
  • 9
    Glunde K, Bhujwalla ZM. Metabolic tumor imaging using magnetic resonance spectroscopy. Semin Oncol 2011;38:2641.
  • 10
    Ackerstaff E, Pflug BR, Nelson JB, Bhujwalla ZM. Detection of increased choline compounds with proton nuclear magnetic resonance spectroscopy subsequent to malignant transformation of human prostatic epithelial cells. Cancer Res 2001;61:35993603.
  • 11
    Govindaraju V, Young K, Maudsley AA. Proton NMR chemical shifts and coupling constants for brain metabolites. NMR in Biomed 2000;13:129153.
  • 12
    Wallimann T, Wyss M, Brdiczka D, Nicolay K, Eppenberger HM. Intracellular compartmentation, structure and function of creatine kinase isoenzymes in tissues with high and fluctuating energy demands: The ‘phosphocreatine circuit' for cellular energy homeostasis. Biochem J 1992;281:2140.
  • 13
    Kassen A, Sutkowski DM, Ahn H, Sensibar JA, Kozlowski JM, Lee C. Stromal cells of the human prostate: initial isolation and characterization. Prostate 1998;28:8997.
  • 14
    Swanson MG, Zektzer AS, Tabatabai ZL, et al. Quantitative analysis of prostate metabolites using 1H HR-MAS spectroscopy. Magn Reson Med 2006;55:12571264.
  • 15
    Lynch MJ, Masters J, Pryor JP, Lindon JC, Spraul M, Foxall PJ, Nicholson JK. Ultra high field NMR spectroscopic studies on human seminal fluid, seminal vesicle and prostatic secretions. J Pharm Biomed 1994;12:519.
  • 16
    Costello LC, Franklin RB. Citrate metabolism of normal and malignant prostate epithelial cells. Urology 1997;50:312.
  • 17
    Moore GJ, Sillerud LO. The pH dependence of chemical shift and spin-spin coupling for citrate. J Magn Reson B 1994;103:8788.
  • 18
    Van der Graaf M, Heerschap A. Effect of cation binding on the proton chemical shifts and the spin-spin coupling constant of citrate. J Magn Reson B 1996;112:5862.
  • 19
    Trabesinger AH, Meier D, Dydak U, Lamerichs R, Boesiger P. Optimizing PRESS localized citrate detection at 3 Tesla. Magn Reson Med 2005;54:5158.
  • 20
    Mulkern RV, Bowers JL, Peled S, Williamson DS. Density-matrix calculations of the 1.5 T citrate signal acquired with volume-localized STEAM sequences. J Magn Reson B 1996;110:255266.
  • 21
    Wilman AH, Allen PS. The response of the strongly coupled AB system of citrate to typical 1H MRS localization sequences. J Magn Reson B 1995;107:2533.
  • 22
    Scheenen TWJ, Gambarota G, Weiland E, Klomp DWJ, Fütterer JJ, Barentsz JO, Heerschap A. Optimal timing for in vivo 1H-MR spectroscopic imaging of the human prostate at 3T. Magn Reson Med 2005;53:12681274.
  • 23
    Van der Graaf M, Jager GJ, Heerschap A. Removal of the outer lines of the citrate multiplet in proton magnetic resonance spectra of the prostatic gland by accurate timing of a point-resolved spectroscopy pulse sequence. Magn Reson Mater Phy 1997;5:6569.
  • 24
    Schick F, Bongers H, Kurz S, Jung WI, Pfeffer M, Lutz O. Localized proton MR spectroscopy of citrate in vitro and of the human prostate in vivo at 1.5 T. Magn Reson Med 1993;29:3843.
  • 25
    Van der Graaf M, Van den Boogert HJ, Jager GJ, Barentsz JO, Heerschap A. Human prostate: multisection proton MR spectroscopic imaging with a single spin-echo sequence—preliminary experience. Radiology 1999;213:919925.
  • 26
    Cunningham CH, Vigneron DB, Marjanska M, Chen AP, Xu D, Hurd RE, Kurhanewicz J, Garwood M, Pauly JM. Sequence design for magnetic resonance spectroscopic imaging of prostate cancer at 3 T. Magn Reson Med 2005;53:10331039.
  • 27
    Verma S, Rajesh A, Fütterer JJ, Turkbey B, Scheenen TWJ, Pang Y, Choyke PL, Kurhanewicz J. Prostate MRI and 3D MR spectroscopy: how we do it. AJR Am J Roentgenol 2010;194:14141426.
  • 28
    Kobus T, Wright AJ, Asten JJ, Heerschap A, Scheenen TW. In vivo 1H MR spectroscopic imaging of aggressive prostate cancer: can we detect lactate? Magn Reson Med 2014;71:2634.
  • 29
    Mescher M, Tannus A, Johnson MO, Garwood M. Solvent suppression using selective echo dephasing. J Magn Reson A 1996;123:226229.
  • 30
    Males RG, Vigneron DB, Star Lack O, Falbo SC, Nelson SJ, Hricak H, Kurhanewicz J. Clinical application of BASING and spectral/spatial water and lipid suppression pulses for prostate cancer staging and localization by in vivo 3D H-1 magnetic resonance spectroscopic imaging. Magn Reson Med 2000;43:1722.
  • 31
    Star-Lack J, Nelson SJ, Kurhanewicz J, Huang LR, Vigneron DB. Improved water and lipid suppression for 3D PRESS CSI using RF band selective inversion with gradient dephasing (BASING). Magn Reson Med 1997;38:311321.
  • 32
    Schricker AA, Pauly JM, Kurhanewicz J, Swanson MG, Vigneron DB. Dualband spectral-spatial RF pulses for prostate MR spectroscopic imaging. Magn Reson Med 2001;46:10791087.
  • 33
    Cunningham CH, Vigneron DB, Chen AP, Xu D, Hurd RE, Sailasuta N, Pauly JM. Design of symmetric-sweep spectral-spatial RF pulses for spectral editing. Magn Reson Med 2004;52:147153.
  • 34
    Gambarota G, Van Der Graaf M, Klomp D, Mulkern RV, Heerschap A. Echo-time independent signal modulations using PRESS sequences: a new approach to spectral editing of strongly coupled AB spin systems. J Magn Reson 2005;177:299306.
  • 35
    Kickler N, Gambarota G, Mekle R, Gruetter R, Mulkern R. Echo-time independent signal modulations for strongly coupled systems in triple echo localization schemes: an extension of S-PRESS editing. J Magn Reson 2010;203:108112.
  • 36
    Lynch MJ, Nicholson JK. Proton MRS of human prostatic fluid: correlations between citrate, spermine, and myo-inositol levels and changes with disease. Prostate 1997;30:248255.
  • 37
    Schipper RG, Romijn JC, Cuijpers VMJI, Verhofstad AAJ. Polyamines and prostatic cancer. Biochem Soc T 2003;31:375380.
  • 38
    Van der Graaf M, Schipper RG, Oosterhof GO, Schalken JA, Verhofstad AA, Heerschap A. Proton MR spectroscopy of prostatic tissue focused on the detection of spermine, a possible biomarker of malignant behavior in prostate cancer. Magma (New York, NY 2000;10:153159.
  • 39
    Shukla-Dave A, Hricak H, Moskowitz C, et al. Detection of prostate cancer with MR spectroscopic imaging: an expanded paradigm incorporating polyamines. Radiology 2007;245:499506.
  • 40
    Willker W, Flögel U, Leibfritz D. A 1H/13C inverse 2D method for the analysis of the polyamines putrescine, spermidine and spermine in cell extracts and biofluids. NMR in Biomed 1998;11:4754.
  • 41
    Spencer NG, Eykyn TR, DeSouza NM, Payne GS. The effect of experimental conditions on the detection of spermine in cell extracts and tissues. NMR Biomed 2010;23:163169.
  • 42
    García-Martín ML, Adrados M, Ortega MP, Fernández González I, Lõpez-Larrubia P, Viaño J, García-Segura JM. Quantitative 1H MR spectroscopic imaging of the prostate gland using LCModel and a dedicated basis-set: correlation with histologic findings. Magn Reson Med 2011;65:329339.
  • 43
    Yuen JS, Thng CH, Tan PH, Khin LW, Phee SJ, Xiao D, Lau WK, Ng WS, Cheng CW. Endorectal magnetic resonance imaging and spectroscopy for the detection of tumor foci in men with prior negative transrectal ultrasound prostate biopsy. J Urol 2004;171:14821486.
  • 44
    Barentsz JO, Richenberg J, Clements R, Choyke P, Verma S, Villeirs G, Rouviere O, Logager V, Fütterer JJ. ESUR prostate MR guidelines 2012. Eur Radiol 2012;22:746757.
  • 45
    Weinreb JC, Blume JD, Coakley FV, et al. Prostate cancer: sextant localization at MR imaging and MR spectroscopic imaging before prostatectomy—results of ACRIN prospective multi-institutional clinicopathologic study. Radiology 2009;251:122133.
  • 46
    Lowry M, Liney GP, Turnbull LW, Manton DJ, Blackband SJ, Horsman A. Quantification of citrate concentration in the prostate by proton magnetic resonance spectroscopy: zonal and age-related differences. Magn Reson Med 1996;36:352358.
  • 47
    Scheenen TWJ, Fütterer J, Weiland E, et al. Discriminating cancer from noncancer tissue in the prostate by 3-dimensional proton magnetic resonance spectroscopic imaging: a prospective multicenter validation study. Invest Radiol 2011;46:2533.
  • 48
    Selnæs KM, Heerschap A, Jensen LR, Tessem MB, Schweder GJV, Goa PE, Viset T, Angelsen A, Gribbestad IS. Peripheral zone prostate cancer localization by multiparametric magnetic resonance at 3 T: unbiased cancer identification by matching to histopathology. Invest Radiol 2012;47:624633.
  • 49
    Smith S, Levante T, Meier BH, Ernst RR. Computer simulations in magnetic resonance. An object-oriented programming approach. J Magn Reson A 1994;106:75105.
  • 50
    Naressi A, Couturier C, Devos J, Janssen M, Mangeat C, De Beer R, Graveron-Demilly D. Java-based graphical user interface for the MRUI quantitation package. MAGMA 2001;12:141152.
  • 51
    Vanhamme L, van den Boogaart A, Van Huffel S. Improved method for accurate and efficient quantification of MRS data with use of prior knowledge. J Magn Reson 1997;129:3543.
  • 52
    Provencher SW. Estimation of metabolite concentrations from localized in vivo proton NMR spectra. Magn Reson Med 1993;30:672679.
  • 53
    Pels P, Osturk-Isik E, Swanson MG, Vanhamme L, Kurhanewicz J, Nelson SJ, Van Huffel S. Quantification of prostate MRSI data by model-based time domain fitting and frequency domain analysis. NMR in Biomed 2006;19:188197.
  • 54
    Scheenen TW, Heijmink SW, Roell SA, Hulsbergen-Van de Kaa CA, Knipscheer BC, Witjes JA, Barentsz JO, Heerschap A. Three-dimensional proton MR spectroscopy of human prostate at 3 T without endorectal coil: feasibility. Radiology 2007;245:507516.
  • 55
    Kobus T, Hambrock T, Hulsbergen-Van De Kaa CA, Wright AJ, Barentsz JO, Heerschap A, Scheenen TWJ. In vivo assessment of prostate cancer aggressiveness using magnetic resonance spectroscopic imaging at 3 T with an endorectal coil. Eur Urol 2011;60:10741080.
  • 56
    Zakian KL, Sircar K, Hricak H, et al. Correlation of proton MR spectroscopic imaging with gleason score based on step-section pathologic analysis after radical prostatectomy. Radiology 2005;234:804814.
  • 57
    Sciarra A, Panebianco V, Ciccariello M, Salciccia S, Cattarino S, Lisi D, Gentilucci A, Alfarone A, Bernardo S, Passariello R. Value of magnetic resonance spectroscopy imaging and dynamic contrast-enhanced imaging for detecting prostate cancer foci in men with prior negative biopsy. Clin Cancer Res 2010;16:18751883.
  • 58
    Scheidler J, Hricak H, Vigneron DB, Yu KK, Sokolov DL, Huang LR, Zaloudek CJ, Nelson SJ, Carroll PR, Kurhanewicz J. Prostate cancer: localization with three-dimensional proton MR spectroscopic imaging—clinicopathologic study. Radiology 1999;213:473480.
  • 59
    Amsellem-Ouazana D, Younes P, Conquy S, Peyromaure M, Flam T, Debré B, Zerbib M. Negative prostatic biopsies in patients with a high risk of prostate cancer: is the combination of endorectal MRI and magnetic resonance spectroscopy imaging (MRSI) a useful tool? A preliminary study. Eur Urol 2005;47:582586.
  • 60
    Lagemaat MW, Scheenen TW. Role of high-field MR in studies of localized prostate cancer. NMR Biomed 2014;27:6779.
  • 61
    Lagemaat MW, Zechmann CM, Fütterer JJ, et al. Reproducibility of 3D 1H MR spectroscopic imaging of the prostate at 1.5T. J Magn Reson Imaging 2012;35:166173.
  • 62
    Jung JA, Coakley FV, Vigneron DB, Swanson MG, Qayyum A, Weinberg V, Jones KD, Carroll PR, Kurhanewicz J. Prostate depiction at endorectal MR spectroscopic imaging: investigation of a standardized evaluation system. Radiology 2004;233:701708.
  • 63
    Fütterer JJ, Scheenen TWJ, Heijmink SWTPJ, Huisman HJ, Hulsbergen-Van De Kaa CA, Witjes JA, Heerschap A, Barentsz JO. Standardized threshold approach using three-dimensional proton magnetic resonance spectroscopic imaging in prostate cancer localization of the entire prostate. Invest Radiol 2007;42:116122.
  • 64
    Heerschap A, De Jager G, De Koster A, Barentsz J, De la Rosette J, Debruyne F, Ruijs J. 1H MRS of prostate pathology. Proc Int Soc Magn Reson Med. Volume S1. New York, New York, USA; 1993.
  • 65
    Chen AP, Cunningham CH, Kurhanewicz J, Xu D, Hurd RE, Pauly JM, Carvajal L, Karpodinis K, Vigneron DB. High-resolution 3D MR spectroscopic imaging of the prostate at 3 T with the MLEV-PRESS sequence. Magn Reson Imaging 2006;24:825832.
  • 66
    Mueller Lisse UG, Vigneron DB, Hricak H, et al. Localized prostate cancer: effect of hormone deprivation therapy measured by using combined three-dimensional H-1 MR spectroscopy and MR imaging: clinicopathologic case-controlled study. Radiology 2001;221:380390.
  • 67
    Mazaheri Y, Shukla-Dave A, Hricak H, et al. Prostate cancer: identification with combined diffusion-weighted MR imaging and 3D1H MR spectroscopic imaging—correlation with pathologic findings. Radiology 2008;246:480488.
  • 68
    Weis J, Ahlström H, Hlavcak P, Häggman M, Ortiz-Nieto F, Bergman A. Two-dimensional spectroscopic imaging for pretreatment evaluation of prostate cancer: comparison with the step-section histology after radical prostatectomy. Magn Reson Imaging 2009;27:8793.
  • 69
    Kaji Y, Wada A, Imaoka I, Matsuo M, Terachi T, Kobashi Y, Sugimura K, Fujii M, Maruyama K, Takizawa O. Proton two-dimensional chemical shift imaging for evaluation of prostate cancer: external surface coil vs. endorectal surface coil. J Magn Reson Imaging 2002;16:697706.
  • 70
    van Dorsten FA, van der Graaf M, Engelbrecht MR, van Leenders GJ, Verhofstad A, Rijpkema M, de la Rosette JJ, Barentsz JO, Heerschap A. Combined quantitative dynamic contrast-enhanced MR imaging and 1H MR spectroscopic imaging of human prostate cancer. J Magn Reson Imaging 2004;20:279287.
  • 71
    Wetter A, Hübner F, Lehnert T, Fliessbach K, Vorbuchner M, Roell S, Zangos S, Luboldt W, Vogl TJ. Three-dimensional 1 H-magnetic resonance spectroscopy of the prostate in clinical practice: technique and results in patients with elevated prostate-specific antigen and negative or no previous prostate biopsies. Eur Radiol 2005;15:645652.
  • 72
    Weis J, Jorulf H, Bergman A, Ortiz-Nieto F, Häggman M, Ahlström H. MR spectroscopy of the human prostate using surface coil at 3 T: metabolite ratios, age-dependent effects, and diagnostic possibilities. J Magn Reson Imaging 2011;34:12771284.
  • 73
    Futterer JJ, Scheenen TW, Huisman HJ, Klomp DW, van Dorsten FA, Hulsbergen-van de Kaa CA, Witjes JA, Heerschap A, Barentsz JO. Initial experience of 3 Tesla endorectal coil magnetic resonance imaging and 1H-spectroscopic imaging of the prostate. Invest Radiol 2004;39:671680.