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Using DiffusionGradientTable on HCP-style acquisition #128

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josephmje opened this issue Nov 10, 2020 · 3 comments
Open

Using DiffusionGradientTable on HCP-style acquisition #128

josephmje opened this issue Nov 10, 2020 · 3 comments
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@arokem @dPys

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@josephmje
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Should this be the intended behaviour?

np.genfromtxt(bval)

>>> array([   5.,    5.,  610., 1240.,  615., 1225.,  625., 1230.,  620.,
           1230.,  610., 1240.,  615., 1225.,  620., 1220.,  620.,    5.,
           1225.,  615., 1240.,  625., 1230.,  620., 1240.,  615., 1225.,
            615., 1230.,  625., 1230.,  610., 1230.,    5.,  615., 1235.,
            615., 1220.,  625., 1245.,  620., 1240.,  620., 1225.,  615.,
            1235.,  620., 1225.,  605.,    5., 1225.,  625., 1235.,  615.,
            1230.,  620., 1230.,  610., 1235.,  620., 1230.,  620., 1220.,
            625., 1235.,    5.,  610., 1230.,  620., 1240.,  615., 1230.,
            625., 1230.,  610., 1235.,  610., 1240.,  620., 1235.,  620.,
            5., 1220.,  615., 1230.,  615., 1230.,  620., 1240.,  610.,
            1215.,  620., 1240.,  620., 1225.,  620., 1225.,  615., 1230.,
            5.,  890., 1800.,  895., 1785.,  910., 1790.,  900., 1795.,
            890., 1800.,  895., 1785.,  905., 1780.,  900.,    5., 1785.,
            895., 1805.,  905., 1795.,  900., 1800.,  895., 1780.,  895.,
           1790.,  905., 1790.,  890., 1790.,    5.,  895., 1795.,  895.,
           1775.,  905., 1805.,  900., 1805.,  900., 1785.,  895., 1795.,
           900., 1785.,  885.,    5., 1785.,  905., 1795.,  895., 1795.,
           900., 1790.,  890., 1795.,  900., 1790.,  900., 1780.,  910.,
           1795.,    5.,  890., 1790.,  900., 1805.,  895., 1790.,  905.,
           1790.,  890., 1795.,  890., 1800.,  905., 1800.,  900.,    5.,
           1780.,  895., 1790.,  895., 1790.,  900., 1805.,  890., 1775.,
           900., 1805.,  900., 1785.,  900., 1785.,  895., 1790.])

gtab = DiffusionGradientTable(dwi_file=dwi, bvecs=bvec, bvals=bval)

gtab._bvals

>>> Inconsistent bvals and bvecs (13, 0 low-b, respectively).
array([   0,    0,  600, 1200,  600, 1200,  600, 1200,  600, 1200,  600,
       1200,  600, 1200,  600, 1200,  600,    0, 1200,  600, 1200,  600,
       1200,  600, 1200,  600, 1200,  600, 1200,  600, 1200,  600, 1200,
          0,  600, 1200,  600, 1200,  600, 1200,  600, 1200,  600, 1200,
        600, 1200,  600, 1200,  600,    0, 1200,  600, 1200,  600, 1200,
        600, 1200,  600, 1200,  600, 1200,  600, 1200,  600, 1200,    0,
        600, 1200,  600, 1200,  600, 1200,  600, 1200,  600, 1200,  600,
       1200,  600, 1200,  600,    0, 1200,  600, 1200,  600, 1200,  600,
       1200,  600, 1200,  600, 1200,  600, 1200,  600, 1200,  600, 1200,
          0,  900, 1800,  900, 1800,  900, 1800,  900, 1800,  900, 1800,
        900, 1800,  900, 1800,  900,    0, 1800,  900, 1800,  900, 1800,
        900, 1800,  900, 1800,  900, 1800,  900, 1800,  900, 1800,    0,
        900, 1800,  900, 1800,  900, 1800,  900, 1800,  900, 1800,  900,
       1800,  900, 1800,  900,    0, 1800,  900, 1800,  900, 1800,  900,
       1800,  900, 1800,  900, 1800,  900, 1800,  900, 1800,    0,  900,
       1800,  900, 1800,  900, 1800,  900, 1800,  900, 1800,  900, 1800,
        900, 1800,  900,    0, 1800,  900, 1800,  900, 1800,  900, 1800,
        900, 1800,  900, 1800,  900, 1800,  900, 1800,  900, 1800],
      dtype=uint16)
@arokem
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arokem commented Nov 10, 2020

I think so. The questions is basically to what order of magnitude should you be rounding the b-values. This is determined through an optional bmag input to the round_bvals function. If you do not specify that, this order of magnitude is determined by the value of the largest b-value, to be order of magnitude smaller than that of the maximal value. So, in this case, it automatically rounds into the nearest 100's. We could give users extra controls, by exposing this keyword argument through our API.

@josephmje
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josephmje commented Nov 10, 2020 •
edited
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It seems like there are 2 places where the bvals are getting rescaled: 1 and 2

I was looking at how MRtrix3 handles this and they scale by the squared amplitude of the gradient vectors

@josephmje
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josephmje commented Feb 8, 2021 •
edited
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I ran into this issue again when working with the NTU-DSI-122 dataset. The round_bvals function artificially introduces b0s into the dataset. Below is what MRtrix3 outputs vs DiffusionGradientTable:

MRtrix3

                     0                      0                      0                      0
   -0.9899531957603029   0.005809392215236331     -0.141276045974019      23.69176963999999
 -0.006502908410694216    -0.9999646796184842   0.005324631227082544      23.70627335999999
    0.1410910353363024  -0.006607010764052516    -0.9899745790455904            23.69922632
   -0.1407945428760187    0.00625032986577012     0.9900191059130637      23.67952356000002
  0.006860320422247859     0.9999602581574732  -0.005693690309796706      23.68945347999999
    0.9900094119725403  -0.006166866655823903     0.1408663691639498      23.70607623999998
   -0.7045540089015556    -0.7031449846408822   -0.09586855123094538      94.63130459999998
   -0.6001289237969868    -0.0005646504771176    -0.7999030916255475      94.64211015000005
   -0.7996815774092526   0.008759225039016638     0.6003604340135726      94.57770119999999
   -0.6952306934237482     0.7112326081846938    -0.1039348833468926      94.59178469999996
   0.09513700199524415    -0.7117946055596297    -0.6959147866999006      94.66587375000003
   -0.1043794235335665    -0.7026097143670808     0.7038782033987196      94.61751630000005
    0.1046273090687004     0.7023341984240934    -0.7041163255606483      94.62329115000003
  -0.09492256010233978     0.7118148396036609     0.6959233734425307      94.59294089999997
    0.6952535017302935    -0.7112531678920744     0.1036412055856805      94.65391814999997
    0.7996456549792608  -0.009010127363994088    -0.6004045670026783      94.64561564999997
    0.6004142069813299   0.000310617935881372     0.7996891168269572      94.63131690000002
    0.7048237809209271     0.7029095809977064    0.09561149925588244      94.62628004999992
    -0.493809498332005    -0.5777528760218104    -0.6498875238113285           213.01434271
   -0.6567618534198728    -0.5703284057907044     0.4933450896084069      213.0151272599999
   -0.4861878538895903     0.5768854414110277    -0.6563722710613529      212.9470006300001
   -0.6491074074800444     0.5845102005647062     0.4868340569335127           212.96463916
    0.6490803459881754     -0.584508681060358    -0.4868719608038146      213.1169618499999
    0.4863538361429843    -0.5770465955993693     0.6561075922256814      212.9811977800001
    0.6568874031501775     0.5700520054254746    -0.4934973664499241      213.0666583499999
    0.4940917381045879     0.5776357224313291     0.6497771360305191           212.94774826
   -0.9899785880381897   0.005794062414228898    -0.1410986324053229      378.7514825899997
  -0.00668158292718224    -0.9999625022749526   0.005509128207131228      378.8580133299998
    0.1410910353363024  -0.006607010764052516    -0.9899745790455904      378.8798389600001
    -0.140942823755781   0.006428707571396775     0.9899968647176152      378.7223078900003
  0.006860320422247859     0.9999602581574732  -0.005693690309796706      378.7236004399999
    0.9900066998982968  -0.005972831099460889     0.1408937878160044      378.8658301799998
   -0.8884309004261718    -0.4420410454093279    -0.1236537477853798      591.4315788199997
   -0.8223376853574679   0.002227073738432438    -0.5689954054150929      591.5360090199998
   -0.9485558590624227   0.008138254289808971     0.3165052148945788      591.3164133799996
   -0.8824709581135973     0.4524002319696442    -0.1287596140095248      591.4100621999996
   -0.4486547759013213    -0.8918155694771829   -0.05808512803655598           591.66935362
   -0.3165053592573777  -0.003326032088558235     -0.948584890809412      591.5686607600003
    -0.568868238191698   0.008349094632621139     0.8223862962109041           591.33580756
   -0.4366329090312537     0.8970448619936776   -0.06828043879215234      591.3946052999997
   0.05710964645604326    -0.8973340983424405    -0.4376414105562053           591.71424784
  -0.06892055970921297    -0.8915171562323196     0.4477132079722535      591.5923459599999
    0.1231332314170047    -0.4530313796439907    -0.8829500418362688      591.6634477000003
   -0.1291220352910538    -0.4414154913016568     0.8879638866762616      591.5484822000003
    0.1292684993217259     0.4412127024240272    -0.8880433583461992      591.6613560200001
   -0.1230036383974283     0.4529781870019794     0.8829953946886668            591.3826858
    0.0690834101640624     0.8914088529116291    -0.4479037166522264      591.5228129799997
  -0.05696369370430737     0.8973754047988891     0.4375757311158741      591.4479123800002
    0.4367033714557719    -0.8970240922409807    0.06810244708089999      591.6339334799997
    0.5688980603518858  -0.008507891104895858     -0.822364039046461           591.60401938
     0.316697719442156   0.003165673538300151     0.9485212348762606      591.4432676200004
    0.4488501643784535      0.891727618627997    0.05792567749691497      591.5728441199999
     0.882460575140504    -0.4524741927965215     0.1285707516343706      591.6368710599996
    0.9485212254543728  -0.008297290316853355    -0.3166048638854632      591.5576486799996
    0.8224655944965047  -0.002387682577986434     0.5688098494588663      591.5804418399998
    0.8885454764525162     0.4418583349005428     0.1234833922221551      591.4904688399998
   -0.7533834826133998    -0.4062106252124091     -0.517113388039653      851.9036544199998
   -0.8686361430677322    -0.4008207916553304     0.2912283364187087      851.8252917199991
   -0.7479435981593581     0.4102720606408222    -0.5217827231999602      851.8689311599999
    -0.863179838650881     0.4156906413791559     0.2865691135074139      851.8271192599993
   -0.3519473045253541    -0.8167765161927547    -0.4571752589908646      852.2581971799998
   -0.4670587342569737    -0.8114958647902543     0.3511845671190453      852.0466640399994
   -0.2917227184055466    -0.4112244174766262    -0.8635926898932665      852.0802797000005
   -0.5220486664500021    -0.4006130767426127     0.7529769934072301           851.88966174
     -0.28627273647741      0.405171762856365     -0.868264800008385      852.0530142800003
    -0.516600790026178     0.4160297924607157     0.7483600974994743      851.8250886599999
   -0.3411110303320227     0.8161406186474721      -0.46643086902514      851.8933537400001
   -0.4561956325431975     0.8215671869639007     0.3419252873841518      851.8280791799999
    0.4562320574314936    -0.8215184341347409    -0.3419938188748878      852.2334792399998
    0.3412221195574559    -0.8162062793233974     0.4662346777297602      852.0625396400001
    0.5166175864847988    -0.4160732704769194    -0.7483243300396305           852.24784112
    0.2864260496132027    -0.4053302795987155     0.8681402435916687      852.0033015000004
    0.5221258643694325     0.4004143957354313    -0.7530291451493111      852.1108310000003
    0.2919107154615287     0.4111383498263035     0.8635701427798685      851.8290206400003
     0.467203069576093     0.8113480910497152    -0.3513339820294353      852.0489346199996
    0.3521405863409586     0.8167419643203911     0.4570881437638862      852.0242166799998
    0.8631270804454189    -0.4157421101508428    -0.2866533461327322      852.2174559599995
    0.7480209233495438    -0.4103816554874255     0.5215856545867569           852.02301678
    0.8686848615611676     0.4006296865075726    -0.2913459551517732      852.0140821399992
    0.7535494435145621      0.406075231868157     0.5169778933785432      851.8561937599998
   -0.7046889102329872    -0.7030273014536346   -0.09574002927441276      1515.291546979999
    -0.600195794664835 -0.0005755931077146655    -0.7998529094522452      1515.215175740001
   -0.7997312580113511   0.008769821541340104     0.6002940989129535          1514.76522062
   -0.6953093089583275     0.7111742648843095    -0.1038081395756383          1514.94989524
   0.09503157912174616    -0.7117359783189992    -0.6959891494383547          1515.54776732
   -0.1043766151664705    -0.7024828448187593     0.7040052378652685          1515.15965764
    0.1045005570446163     0.7023450832745057    -0.7041242912849212          1515.20591862
  -0.09492434608072184     0.7117460795026269     0.6959934531544012          1514.96385478
    0.6953206955686101    -0.7111845494579555     0.1036613087235014          1515.44739158
    0.7997132938262949  -0.008895281317442789    -0.6003161847291655          1515.30895332
     0.600338454002406  0.0004485686333996093     0.7997459217977809          1515.12873492
    0.7048237809209271     0.7029095809977064    0.09561149925588244      1515.251391759999
   -0.9899701274364242   0.005799172409878569    -0.1411577712450526      1916.925590579998
   -0.6173936842303266    -0.6649581564486334    -0.4203042812593496      1916.957821739999
   -0.7114357348014649    -0.6606297702102505     0.2396403596213524      1917.143621639999
   -0.5681384216010937    -0.3338168227922801    -0.7521868535952256          1917.29730114
   -0.7562248026233656    -0.3251569423814092     0.5678001503332177          1916.93996169
   -0.5636907750696808     0.3327457452699993    -0.7559980020516293      1917.257510610001
   -0.7517878599288741     0.3415958249823061     0.5640277528794316          1916.80228701
   -0.6084983677331682     0.6682918001457023    -0.4279203270751306           1916.8792929
     -0.70251446122126     0.6727609977964326     0.2320906538810886      1916.513120339999
    -0.240367081706168     -0.669084520996192    -0.7032421843116043          1917.03106179
   -0.4284092566129611    -0.6604176484851919     0.6166960664846336          1916.83263525
   -0.2314659577468467     0.6641448894837612    -0.7108692398584551      1916.923624590001
   -0.4195165219505614     0.6730221131994961     0.6091363746772874          1916.60084226
 -0.006621665306556075    -0.9999625155773325   0.005578618899059132          1917.26099955
    0.1410989233339639  -0.006618065797046873     -0.989973380975015      1917.101948190001
     -0.14100011290462   0.006499192632161513     0.9899882467261996      1916.570438640001
  0.006740815394305235     0.9999610254392798  -0.005701667321395197          1916.80746504
    0.4195489560772245    -0.6730040471101617     -0.609133996775631          1917.38610297
    0.2315796962647312    -0.6642514040932337     0.7107326617217634          1917.00018744
     0.428507440275229     0.6602667679254923    -0.6167894039313541          1917.01320174
    0.2405185711335091     0.6690569020218755     0.7032166656136539           1916.5029858
    0.7024978428901176    -0.6727515123280156    -0.2321684375514883      1917.249757409999
    0.6085560479614197    -0.6683449348936378     0.4277552857552987          1917.26160873
    0.7517566747494677    -0.3416476422071081    -0.5640379336037132      1917.565008059999
    0.5638011929302532    -0.3328608298531396     0.7558649897963939      1917.311478420001
    0.7562523650985684     0.3249981419288268     -0.567854354588949      1917.400307939999
    0.5682935805719739     0.3337186690737641      0.752113193736369          1917.04894953
    0.7115295966625185     0.6604872599652611    -0.2397544837888441      1917.275619869999
    0.6175478580595608     0.6648769341127702     0.4202062654112281          1916.73549873
    0.9899888861620468  -0.005918356630029949     0.1410211981598144      1917.311339969999
   -0.9412730152281118    -0.3107270697509186     -0.132112826505933      2366.585501619998
   -0.8945684503270923   0.003402233679603241    -0.4469180153958598      2366.745967169999
   -0.9837845115878349   0.007591182922420133     0.1791937741712129      2366.869201019998
   -0.9370557078734542      0.321723407374855    -0.1357226933452656      2366.543685189999
   -0.3193203725357843    -0.9468329427227913   -0.03926675767755151      2367.183270409999
   -0.1791737856582176  -0.004441257737078998    -0.9838074149764352      2367.229456180002
   -0.4468359397816953   0.007992867080288183     0.8945802127228424          2366.59633266
   -0.3066721383396326     0.9504964500551029   -0.05008690446458604      2366.620794809999
   0.03832898943699911    -0.9507214655645158    -0.3076679760449529          2367.37229052
  -0.05090413420123846    -0.9465934258980432     0.3183860159740499          2367.06942141
    0.1316895777194686    -0.3224677129197085    -0.9373752873019431      2367.026620340001
   -0.1358163757208486    -0.3100663269908438     0.9409637532617611      2366.542485160001
    0.1359165359806085     0.3099250145368349    -0.9409958451615899      2366.965111110001
   -0.1315983819303749      0.322405750260741     0.9374094079281027      2366.268047530001
   0.05101972688012212     0.9465411928083104    -0.3185227743601685      2366.762367579999
  -0.03822237134781056      0.950748588085418     0.3075974196610474      2366.671442229999
    0.3067425177260495    -0.9504802100804183    0.04996396766238997      2367.267610979999
    0.4468789848045321  -0.008105249683660033    -0.8945576995742847          2367.32985869
    0.1793030250077296   0.004328043693851294     0.9837843733567136      2366.781660370001
    0.3194570189273209     0.9467915319025614    0.03915364830600442      2366.822184459999
    0.9370498973207249    -0.3217964877809104     0.1355894921558798      2367.140284719998
    0.9837667991671223  -0.007703796963752689    -0.1792861856608486      2367.494785889999
    0.8946369338019593  -0.003515696550488997      0.446780031509012      2366.977757579999
    0.9413334729664334     0.3105963270064366     0.1319894477716441      2366.846154289998
   -0.8549609679148263    -0.2982328809073012    -0.4243805981520303      2864.317945999999
   -0.9400095918458854    -0.2942713576422251     0.1725871817636922      2863.875932699998
   -0.8509174546513529     0.3047536892530264    -0.4278605780582414           2863.6911794
   -0.9359855425211463       0.30873975200132     0.1691473609772225      2863.846246249998
   -0.2620100281067728    -0.9047053321344104    -0.3359449466490814      2864.843094899999
   -0.3470091186333988    -0.9007896395804802     0.2610798667261423      2864.468544649998
   -0.1729022532928009    -0.3056936922238296    -0.9362991922141313      2864.649709850001
   -0.4279968960004523    -0.2938875015408901     0.8546629706802741      2863.900981700001
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-0.8274190997710505 -0.549847240929348 -0.11421753357715166 4000.0
-0.7499330025473955 0.00042484203276193556 -0.6615136515594334 4000.0
-0.9045116662603169 0.009108327485697211 0.42635159665398054 4000.0
-0.8202506575682548 0.5594573361195452 -0.119148427689449 4000.0
-0.5545989708624862 -0.8288146362898541 -0.07406942817376126 4000.0
-0.4372084753097648 -0.003365067683741304 -0.8993538933238653 4000.0
-0.6715059699088077 0.009644944847007774 0.7409363720426535 4000.0
-0.5437778141893775 0.8352675473165634 -0.0814482240149862 4000.0
0.07238105815060095 -0.8388701330814612 -0.5394978055978458 4000.0
-0.08358572424151994 -0.8322207699771493 0.5481076689133819 4000.0
0.113828777003584 -0.5659206744517679 -0.8165640206094832 4000.0
-0.12141007748585289 -0.5542071789807765 0.8234767731096299 4000.0
0.12150469805024021 0.5540842492961428 -0.8235455379234653 4000.0
-0.1137467364844332 0.5659054875474807 0.8165859777774714 4000.0
0.08368630528650375 0.8321369652995952 -0.5482195484379176 4000.0
-0.07229288784719913 0.8388920520473985 0.5394755447454659 4000.0
0.5438061912072764 -0.8352598828410839 0.08133728862541502 4000.0
0.6715094355097525 -0.009744226315673725 -0.7409319321468621 4000.0
0.4373289548283984 0.0032643918126165593 0.8992956849750777 4000.0
0.5547170673820221 0.8287443353883462 0.0739716277826665 4000.0
0.8202468723931196 -0.5594874254712163 0.11903314273300296 4000.0
0.9044899260128851 -0.00920723167541776 -0.4263955917057312 4000.0
0.7500263305374764 -0.0005251356899835024 0.6614077620749512 4000.0
0.8275049988111247 0.5497391720592623 0.11411537865773048 4000.0

I don't know as much about diffusion gradient scheme handling. But I was wondering whether we should even be rounding at all? If not, we could use clustering to assign b-values to shells (unique_bvals_tolerance).

array([   0,   23,   94,  213,  378,  591,  852, 1515, 1917, 2367, 2864,
       3408, 4000], dtype=uint16)

Or by default, only round to the nearest 10?

EDIT: Both of these solutions would fail in the HCP dataset I described earlier.

@josephmje josephmje mentioned this issue Feb 9, 2021
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