One of the primary aims was to understand the relationship between tissue oxygenation and renal parenchyma depth. Our current study revealed two categories of renal oxygenation manifestation patterns. One pattern, the sharp uptrend style, has better tissue oxygenation with a slight fluctuation in the superficial layers of the kidney. Subsequently, a sharp increment of deoxygenation was observed in the deep layers of the renal parenchyma. Although this pattern has similar characteristics with the well-known renal tissue oxygenation feature, such as higher deoxygenation in the medulla than in the cortex , a distinct two-phase oxygenation feature was different from that in many previous studies [18-20]. This sharp uptrend style of renal R2* values might correlate with a sophisticated relationship between tissue partial pressure of oxygen (PaO2) and oxyhemoglobin saturation dissociation curve. Tissue oxyhemoglobin saturation could be maintained in a stabilized level when PaO2 is above 60 mmHg, whereas there is a steep gradient with a sharp decrease in oxyhemoglobin when the PaO2 is less than 26.6 mmHg. Previous research has shown that PaO2 in the majority of cortexes is usually higher than 60 mmHg, whereas medullary PaO2 rarely exceeds 26.6 mmHg .
Another relevant reason was interrelated with 2,3-diphosphoglycerate (DPG)-mediated oxyhemoglobin affinity. A lessening in oxyhemoglobin affinity has been deemed as an important physiological adaptive response to conditions in which oxygen delivery is impaired. The increased oxyhemoglobin affinity may actually impair tissue oxygenation . Moreover, another pattern of R2* values, the flat uptrend style, was also found in our current study. We found that the R2* values fluctuated in a narrow range throughout the depth of renal parenchyma. R2* values in the deep medulla were only slightly higher than those in the superficial cortex. We even found no discrepancy in R2* level between the superficial renal zone and the deep renal zone, in sporadic samples. This phenomenon implies that tissue oxygenation in the deep medullary zone is not always lower than that in the superficial cortical zone. This discovery is a challenge to the recognized opinion that had been testified by many studies.
The reason why previous studies did not find this exception may derive from the ROI mode, which could remarkably affect the detective R2* value. For example, the TLCO technique was recognized as the preferable manner by which renal R2* values were measured in the past few years . The entire renal parenchyma of the R2 map image was divided into 12 consecutive equivalent depths of the zone. Subsequently, the R2* value of each pixel was detected, and the average R2* value in each zone was also calculated. Because of lower variability and higher stability in repeated measures, this renal R2* acquired technique was well accepted by many investigators. However, the calculated average R2* value was usually prone to affect by higher medullary R2* data instead of those lower R2* data. Under this circumstance, the phenomenon of lower R2* level in the deep medulla was concealed by this technique. There was no plausible explanation for this new discovery as observed in our study. We thought that the microstructure and physiological status in that deep medulla zone might discriminate those renal medullary tissues where the typical oxygenation pattern was easily observed.
Another result of our study involved exploring the possibility of describing renal parenchyma R2* patterns by mathematic models. Some investigators thought that the renal tissue oxygenation status was influenced by multiple factors, such as local blood supplementation and tissue oxygen consumption. Therefore, detected R2* values in each pixel from anywhere in the BOLD images were always the integrated results of multiple physiological factors. The key problem was that the precise participation proportion of each factor was not well understood. However, we believed that information on all involved physiological factors with similar to the encrypted message was still sealed in these R2* maps. We still hope to unlock this sealed information with the right decoding method. By testing the multiple mathematic functions, we found that Gaussian function had the best capability to fit practical R2* data. Moreover, both the sharp uptrend style data and the flat uptrend style data fit the Gaussian function well with two or more compartments. Although there was no reliable evidence to verify the corresponding relationship between renal biological factors and compartments of fit Gaussian functions, we hypothesized that those multiple encrypted and perplexing physiological messages in the R2* data could be transformed into another mode. Perhaps we will decode the encrypted biological messages that were composed in R2* data by studying fit Gaussian functions.
In our current study, we also investigated the oxygenation discrepancy between patients with LN and healthy volunteers. Instead of measuring average R2* values in the renal parenchyma, we focused on the extreme R2* data in both the LN group and the control group. In each study subject, two categories of extreme data were also selected. One had the highest average with the maximal range, and the other had the lowest mean value with the minimal range. We thought these two categories of data stood for the tolerant capacity in tissue deoxygenation and preserved capability in tissue oxygenation. Our study revealed that the tolerant capacity in renal tissue deoxygenation was damaged in the patients with LN. The R2* values of the deep medullary zone in the patients with LN were slightly lower than those in healthy volunteers. In contrast, the preserved capability in tissue oxygenation was also spoiled in the patients with LN. The R2* values of the superficial cortical zone in the patients with LN were also slightly lower than those in healthy volunteers. One possible explanation was that renal tissue deoxygenation in the medulla primarily derived from oxygen consumption in tubular transport proteins. If these transport proteins were diminished or disabled under pathological circumstances, the oxygen consumption also decreased, and that was why the lower R2* values could be observed in the kidneys of the patients with LN. A previous study conducted by our team showed similar results.
In proliferative LN such as type III or type IV, lower renal tissue R2* values usually correspond with severe tubular damage. The inverse manifestation was observed in nonproliferative LN in which mild tubular injuries could be confirmed with renal biopsy samples . Despite statistical discrepancy of R2* values detected between the two groups in both sharp uptrend and flat uptrend style data, the substantial R2* difference was very small. We speculated that at least two factors were involved and that the native mechanism was similar to that in patients with renal artery stenosis. Hansell et al. found that kidneys usually had increased renal blood flow instead of local Po2 when the renal parenchyma was under severe hypoxia conditions. The increased renal blood flow also led to glomerular infiltration rate increments that subsequently induced more filtered sodium in the renal tubules. The reinforced tubular sodium reabsorption was the main reason for increased oxygen consumption .
We also developed a novel ROI method called the “narrow rectangle” or “virtual probe” to measure renal R2* values. This was the principal reason why we devised and applied this special ROI technique. Renal microstructure displays distinct directional characteristics and spatial heterogeneity. Renal parenchyma oxygenation features are based on its histological anatomy foundation. The well-known opinion that the degree of renal blood oxygen saturation in the cortical zone is always higher than that in the medullary zone is based on previous ROI analysis methods such as regional ROI selection , compartmental approach , fractional kidney hypoxia , CO , and TLCO . However, investigators either handled nonconsecutive crude R2* data by regional ROI selection or acquired calculated mean R2* results by CO and TLCO methods. These inherent shortcomings remained an obstacle to the procurement of the explicit oxygenation tendency throughout the renal parenchyma. Contrary to previous conventional ROI analysis methods, our new analysis technique revealed that deep medullary oxygenation was not always overtly lower than cortical oxygenation. This unusual discovery challenged the well-known opinion.
Our current study still had several shortcomings. First, our study merely investigated renal R2* signal instead of other functional MR signals such as ADC, synchronously. We only knew the existing oxygenation condition, whereas we did not understand the precise mechanism of hypoxia status formation. Second, the meaning of fitted Gaussian functions had not yet been explained or proved by other physiological studies, and we only hypothesized that fitted Gaussian formulas represent renal hypoxia outline in the kidney cortical and medullary zones. Third, the narrow rectangular ROI analysis method that was adopted in our study had not been tested by many researchers. Because the highly observer-dependent property of previous conventional ROI analysis methods has been proved, we did not know the new ROI analysis technique had the highly reproducible and lower variant characteristic.